Sample records for neural feedback systems
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Neural cryptography with feedback.
Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido
2004-04-01
Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.
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Neural networks for feedback feedforward nonlinear control systems.
Parisini, T; Zoppoli, R
1994-01-01
This paper deals with the problem of designing feedback feedforward control strategies to drive the state of a dynamic system (in general, nonlinear) so as to track any desired trajectory joining the points of given compact sets, while minimizing a certain cost function (in general, nonquadratic). Due to the generality of the problem, conventional methods are difficult to apply. Thus, an approximate solution is sought by constraining control strategies to take on the structure of multilayer feedforward neural networks. After discussing the approximation properties of neural control strategies, a particular neural architecture is presented, which is based on what has been called the "linear-structure preserving principle". The original functional problem is then reduced to a nonlinear programming one, and backpropagation is applied to derive the optimal values of the synaptic weights. Recursive equations to compute the gradient components are presented, which generalize the classical adjoint system equations of N-stage optimal control theory. Simulation results related to nonlinear nonquadratic problems show the effectiveness of the proposed method.
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Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding
2017-08-29
This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.
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International Nuclear Information System (INIS)
Sabahi, Kamel; Teshnehlab, Mohammad; Shoorhedeli, Mahdi Aliyari
2009-01-01
In this study, a new adaptive controller based on modified feedback error learning (FEL) approaches is proposed for load frequency control (LFC) problem. The FEL strategy consists of intelligent and conventional controllers in feedforward and feedback paths, respectively. In this strategy, a conventional feedback controller (CFC), i.e. proportional, integral and derivative (PID) controller, is essential to guarantee global asymptotic stability of the overall system; and an intelligent feedforward controller (INFC) is adopted to learn the inverse of the controlled system. Therefore, when the INFC learns the inverse of controlled system, the tracking of reference signal is done properly. Generally, the CFC is designed at nominal operating conditions of the system and, therefore, fails to provide the best control performance as well as global stability over a wide range of changes in the operating conditions of the system. So, in this study a supervised controller (SC), a lookup table based controller, is addressed for tuning of the CFC. During abrupt changes of the power system parameters, the SC adjusts the PID parameters according to these operating conditions. Moreover, for improving the performance of overall system, a recurrent fuzzy neural network (RFNN) is adopted in INFC instead of the conventional neural network, which was used in past studies. The proposed FEL controller has been compared with the conventional feedback error learning controller (CFEL) and the PID controller through some performance indices
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Xu, Bin; Yang, Chenguang; Pan, Yongping
2015-10-01
This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.
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Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang
2014-06-01
This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.
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Decorrelation of Neural-Network Activity by Inhibitory Feedback
Einevoll, Gaute T.; Diesmann, Markus
2012-01-01
Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between
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Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation
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Rong Mei
2017-01-01
Full Text Available This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.
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Directory of Open Access Journals (Sweden)
Christopher L Buckley
2018-01-01
Full Text Available During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results
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Buckley, Christopher L; Toyoizumi, Taro
2018-01-01
During active behaviours like running, swimming, whisking or sniffing, motor actions shape sensory input and sensory percepts guide future motor commands. Ongoing cycles of sensory and motor processing constitute a closed-loop feedback system which is central to motor control and, it has been argued, for perceptual processes. This closed-loop feedback is mediated by brainwide neural circuits but how the presence of feedback signals impacts on the dynamics and function of neurons is not well understood. Here we present a simple theory suggesting that closed-loop feedback between the brain/body/environment can modulate neural gain and, consequently, change endogenous neural fluctuations and responses to sensory input. We support this theory with modeling and data analysis in two vertebrate systems. First, in a model of rodent whisking we show that negative feedback mediated by whisking vibrissa can suppress coherent neural fluctuations and neural responses to sensory input in the barrel cortex. We argue this suppression provides an appealing account of a brain state transition (a marked change in global brain activity) coincident with the onset of whisking in rodents. Moreover, this mechanism suggests a novel signal detection mechanism that selectively accentuates active, rather than passive, whisker touch signals. This mechanism is consistent with a predictive coding strategy that is sensitive to the consequences of motor actions rather than the difference between the predicted and actual sensory input. We further support the theory by re-analysing previously published two-photon data recorded in zebrafish larvae performing closed-loop optomotor behaviour in a virtual swim simulator. We show, as predicted by this theory, that the degree to which each cell contributes in linking sensory and motor signals well explains how much its neural fluctuations are suppressed by closed-loop optomotor behaviour. More generally we argue that our results demonstrate the dependence
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Wang, W.; Wang, D.; Peng, Z. H.
2017-09-01
Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.
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Li, Yongming; Tong, Shaocheng
The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small
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Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.
Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert
2015-01-01
Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.
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Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.
Waheeb, Waddah; Ghazali, Rozaida; Herawan, Tutut
2016-01-01
Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.
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Ridge Polynomial Neural Network with Error Feedback for Time Series Forecasting.
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Waddah Waheeb
Full Text Available Time series forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for time series forecasting. It maintains fast learning and the ability to learn the dynamics of the time series over time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate time series with different forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass time-delay differential equation. We compared the forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN and the Dynamic Ridge Polynomial Neural Network (DRPNN. Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall forecasting performance for the network.
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The Neural Feedback Response to Error As a Teaching Signal for the Motor Learning System
Shadmehr, Reza
2016-01-01
When we experience an error during a movement, we update our motor commands to partially correct for this error on the next trial. How does experience of error produce the improvement in the subsequent motor commands? During the course of an erroneous reaching movement, proprioceptive and visual sensory pathways not only sense the error, but also engage feedback mechanisms, resulting in corrective motor responses that continue until the hand arrives at its goal. One possibility is that this feedback response is co-opted by the learning system and used as a template to improve performance on the next attempt. Here we used electromyography (EMG) to compare neural correlates of learning and feedback to test the hypothesis that the feedback response to error acts as a template for learning. We designed a task in which mixtures of error-clamp and force-field perturbation trials were used to deconstruct EMG time courses into error-feedback and learning components. We observed that the error-feedback response was composed of excitation of some muscles, and inhibition of others, producing a complex activation/deactivation pattern during the reach. Despite this complexity, across muscles the learning response was consistently a scaled version of the error-feedback response, but shifted 125 ms earlier in time. Across people, individuals who produced a greater feedback response to error, also learned more from error. This suggests that the feedback response to error serves as a teaching signal for the brain. Individuals who learn faster have a better teacher in their feedback control system. SIGNIFICANCE STATEMENT Our sensory organs transduce errors in behavior. To improve performance, we must generate better motor commands. How does the nervous system transform an error in sensory coordinates into better motor commands in muscle coordinates? Here we show that when an error occurs during a movement, the reflexes transform the sensory representation of error into motor
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Quantized Synchronization of Chaotic Neural Networks With Scheduled Output Feedback Control.
Wan, Ying; Cao, Jinde; Wen, Guanghui
In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control gain matrix, allowable length of sampling intervals, and upper bound of network-induced delays are derived to ensure the quantized synchronization of master-slave chaotic neural networks. Lastly, Chua's circuit system and 4-D Hopfield neural network are simulated to validate the effectiveness of the main results.In this paper, the synchronization problem of master-slave chaotic neural networks with remote sensors, quantization process, and communication time delays is investigated. The information communication channel between the master chaotic neural network and slave chaotic neural network consists of several remote sensors, with each sensor able to access only partial knowledge of output information of the master neural network. At each sampling instants, each sensor updates its own measurement and only one sensor is scheduled to transmit its latest information to the controller's side in order to update the control inputs for the slave neural network. Thus, such communication process and control strategy are much more energy-saving comparing with the traditional point-to-point scheme. Sufficient conditions for output feedback control
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de Lamare, Rodrigo C; Sampaio-Neto, Raimundo
2008-11-01
A space-time adaptive decision feedback (DF) receiver using recurrent neural networks (RNNs) is proposed for joint equalization and interference suppression in direct-sequence code-division multiple-access (DS-CDMA) systems equipped with antenna arrays. The proposed receiver structure employs dynamically driven RNNs in the feedforward section for equalization and multiaccess interference (MAI) suppression and a finite impulse response (FIR) linear filter in the feedback section for performing interference cancellation. A data selective gradient algorithm, based upon the set-membership (SM) design framework, is proposed for the estimation of the coefficients of RNN structures and is applied to the estimation of the parameters of the proposed neural receiver structure. Simulation results show that the proposed techniques achieve significant performance gains over existing schemes.
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Real-time system for studies of the effects of acoustic feedback on animal vocalizations.
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Mike eSkocik
2013-01-01
Full Text Available Studies of behavioral and neural responses to distorted auditory feedback can help shed light on the neural mechanisms of animal vocalizations. We describe an apparatus for generating real-time acoustic feedback. The system can very rapidly detect acoustic features in a song and output acoustic signals if the detected features match the desired acoustic template. The system uses spectrogram-based detection of acoustic elements. It is low-cost and can be programmed for a variety of behavioral experiments requiring acoustic feedback or neural stimulation. We use the system to study the effects of acoustic feedback on birds' vocalizations and demonstrate that such an acoustic feedback can cause both immediate and long-term changes to birds’ songs.
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Xu, Bin; Yang, Daipeng; Shi, Zhongke; Pan, Yongping; Chen, Badong; Sun, Fuchun
2017-09-25
This paper investigates the online recorded data-based composite neural control of uncertain strict-feedback systems using the backstepping framework. In each step of the virtual control design, neural network (NN) is employed for uncertainty approximation. In previous works, most designs are directly toward system stability ignoring the fact how the NN is working as an approximator. In this paper, to enhance the learning ability, a novel prediction error signal is constructed to provide additional correction information for NN weight update using online recorded data. In this way, the neural approximation precision is highly improved, and the convergence speed can be faster. Furthermore, the sliding mode differentiator is employed to approximate the derivative of the virtual control signal, and thus, the complex analysis of the backstepping design can be avoided. The closed-loop stability is rigorously established, and the boundedness of the tracking error can be guaranteed. Through simulation of hypersonic flight dynamics, the proposed approach exhibits better tracking performance.
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Directory of Open Access Journals (Sweden)
Adrian TOADER
2010-09-01
Full Text Available The paper was conceived in two parts. Part I, previously published in this journal, highlighted the main steps of adaptive output feedback control for non-affine uncertain systems, having a known relative degree. The main paradigm of this approach was the feedback linearization (dynamic inversion with neural network augmentation. Meanwhile, based on new contributions of the authors, a new paradigm, that of robust servomechanism problem solution, has been added to the controller architecture. The current Part II of the paper presents the validation of the controller hereby obtained by using the longitudinal channel of a hovering VTOL-type aircraft as mathematical model.
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Accelerator and feedback control simulation using neural networks
International Nuclear Information System (INIS)
Nguyen, D.; Lee, M.; Sass, R.; Shoaee, H.
1991-05-01
Unlike present constant model feedback system, neural networks can adapt as the dynamics of the process changes with time. Using a process model, the ''Accelerator'' network is first trained to simulate the dynamics of the beam for a given beam line. This ''Accelerator'' network is then used to train a second ''Controller'' network which performs the control function. In simulation, the networks are used to adjust corrector magnetics to control the launch angle and position of the beam to keep it on the desired trajectory when the incoming beam is perturbed. 4 refs., 3 figs
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International Nuclear Information System (INIS)
Aelen, P; Jurkov, A; Aulanier, A; Mintchev, M P
2009-01-01
Neural gastric electrical stimulation (NGES) is a new method for invoking gastric contractions under microprocessor control. However, optimization of this technique using feedback mechanisms to minimize power consumption and maximize effectiveness has been lacking. The present pilot study proposes a prototype feedback-controlled neural gastric electric stimulator for the treatment of obesity. Both force-based and inter-electrode impedance-based feedback neurostimulators were implemented and tested. Four mongrel dogs (2 M, 2 F, weight 14.9 ± 2.3 kg) underwent subserosal implantation of two-channel, 1 cm, bipolar electrode leads and two force transducers in the distal antrum. Two of the dogs were stimulated with a force feedback system utilizing the force transducers, and the other two animals were stimulated utilizing an inter-electrode impedance-based feedback system utilizing the proximal electrode leads. Both feedback systems were able to recognize erythromycin-driven contractions of the stomach and were capable of overriding them with NGES-invoked retrograde contractions which exceeded the magnitudes of the erythromycin-driven contractions by an average of 100.6 ± 33.5% in all animals. The NGES-invoked contractions blocked the erythromycin-driven contractions past the proximal electrode pair and induced temporary gastroparesis in the vicinity of the distal force transducer despite the continuing erythromycin infusion. The amplitudes of the erythromycin-invoked contractions in the vicinity of the proximal force transducer decreased abruptly by an average of 47.9 ± 6.3% in all four dogs after triggering-invoked retrograde contractions, regardless of the specific feedback-controlled mechanism. The proposed technique could be helpful for retaining food longer in the stomach, thus inducing early satiety and diminishing food intake
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Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.
Pan, Yongping; Yu, Haoyong
2017-06-01
This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.
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Neural correlates of anticipation and processing of performance feedback in social anxiety.
Heitmann, Carina Y; Peterburs, Jutta; Mothes-Lasch, Martin; Hallfarth, Marlit C; Böhme, Stephanie; Miltner, Wolfgang H R; Straube, Thomas
2014-12-01
Fear of negative evaluation, such as negative social performance feedback, is the core symptom of social anxiety. The present study investigated the neural correlates of anticipation and perception of social performance feedback in social anxiety. High (HSA) and low (LSA) socially anxious individuals were asked to give a speech on a personally relevant topic and received standardized but appropriate expert performance feedback in a succeeding experimental session in which neural activity was measured during anticipation and presentation of negative and positive performance feedback concerning the speech performance, or a neutral feedback-unrelated control condition. HSA compared to LSA subjects reported greater anxiety during anticipation of negative feedback. Functional magnetic resonance imaging results showed deactivation of medial prefrontal brain areas during anticipation of negative feedback relative to the control and the positive condition, and medial prefrontal and insular hyperactivation during presentation of negative as well as positive feedback in HSA compared to LSA subjects. The results indicate distinct processes underlying feedback processing during anticipation and presentation of feedback in HSA as compared to LSA individuals. In line with the role of the medial prefrontal cortex in self-referential information processing and the insula in interoception, social anxiety seems to be associated with lower self-monitoring during feedback anticipation, and an increased self-focus and interoception during feedback presentation, regardless of feedback valence. © 2014 Wiley Periodicals, Inc.
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Neural network based approach for tuning of SNS feedback and feedforward controllers
International Nuclear Information System (INIS)
Kwon, Sung-Il; Prokop, Mark S.; Regan, Amy H.
2002-01-01
The primary controllers in the SNS low level RF system are proportional-integral (PI) feedback controllers. To obtain the best performance of the linac control systems, approximately 91 individual PI controller gains should be optimally tuned. Tuning is time consuming and requires automation. In this paper, a neural network is used for the controller gain tuning. A neural network can approximate any continuous mapping through learning. In a sense, the cavity loop PI controller is a continuous mapping of the tracking error and its one-sample-delay inputs to the controller output. Also, monotonic cavity output with respect to its input makes knowing the detailed parameters of the cavity unnecessary. Hence the PI controller is a prime candidate for approximation through a neural network. Using mean square error minimization to train the neural network along with a continuous mapping of appropriate weights, optimally tuned PI controller gains can be determined. The same neural network approximation property is also applied to enhance the adaptive feedforward controller performance. This is done by adjusting the feedforward controller gains, forgetting factor, and learning ratio. Lastly, the automation of the tuning procedure data measurement, neural network training, tuning and loading the controller gain to the DSP is addressed.
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Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang
2010-09-01
This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.
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A simulation study of the global orbit feedback system for Pohang light source
International Nuclear Information System (INIS)
Kim, Kukhee; Shim, Kyuyeol; Cho, Moohyun; Namkung, Won; Ko, In Soo; Choi, Jinhyuk
2000-01-01
This paper describes the simulation of the global orbit feedback system using the singular value decomposition (SVD) method, the error minimization method, and the neural network method. Instead of facing unacceptable correction result raised occasionally in the SVD method, we choose the error minimization method for the global orbit feedback. This method provides minimum orbit errors while avoiding unacceptable corrections, and keeps the orbit within the dynamic aperture of the storage ring. We simulate the Pohang Light Source (PLS) storage ring using the Methodical Accelerator Design (MAD) code that generates the orbit distortions for the error minimization method and the learning data set for neural network method. In order to compare the effectiveness of the neural network method with others, a neural network is trained by the learning algorithm using the learning data set. The global response matrix with a minimum error and the trained neural network are used to the global orbit feedback system. The simulation shows that a selection of beam position monitors (BPMs) is very sensitive in the reduction of rms orbit distortions, and the random choice gives better results than any other cases. (author)
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Strategies influence neural activity for feedback learning across child and adolescent development.
Peters, Sabine; Koolschijn, P Cédric M P; Crone, Eveline A; Van Duijvenvoorde, Anna C K; Raijmakers, Maartje E J
2014-09-01
Learning from feedback is an important aspect of executive functioning that shows profound improvements during childhood and adolescence. This is accompanied by neural changes in the feedback-learning network, which includes pre-supplementary motor area (pre- SMA)/anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), superior parietal cortex (SPC), and the basal ganglia. However, there can be considerable differences within age ranges in performance that are ascribed to differences in strategy use. This is problematic for traditional approaches of analyzing developmental data, in which age groups are assumed to be homogenous in strategy use. In this study, we used latent variable models to investigate if underlying strategy groups could be detected for a feedback-learning task and whether there were differences in neural activation patterns between strategies. In a sample of 268 participants between ages 8 to 25 years, we observed four underlying strategy groups, which were cut across age groups and varied in the optimality of executive functioning. These strategy groups also differed in neural activity during learning; especially the most optimal performing group showed more activity in DLPFC, SPC and pre-SMA/ACC compared to the other groups. However, age differences remained an important contributor to neural activation, even when correcting for strategy. These findings contribute to the debate of age versus performance predictors of neural development, and highlight the importance of studying individual differences in strategy use when studying development. Copyright © 2014 Elsevier Ltd. All rights reserved.
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Parameter estimation in space systems using recurrent neural networks
Parlos, Alexander G.; Atiya, Amir F.; Sunkel, John W.
1991-01-01
The identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
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Neural network-based optimal adaptive output feedback control of a helicopter UAV.
Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani
2013-07-01
Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.
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International Nuclear Information System (INIS)
Arriagada, A J; Jurkov, A S; Mintchev, M P; Neshev, E; Andrews, C N; Muench, G
2011-01-01
Functional neural gastrointestinal electrical stimulation (NGES) is a methodology of gastric electrical stimulation that can be applied as a possible treatment for disorders such as obesity and gastroparesis. NGES is capable of generating strong lumen-occluding local contractions that can produce retrograde or antegrade movement of gastric content. A feedback-controlled implantable NGES system has been designed, implemented and tested both in laboratory conditions and in an acute animal setting. The feedback system, based on gastric tissue impedance change, is aimed at reducing battery energy requirements and managing the phenomenon of gastric tissue accommodation. Acute animal testing was undertaken in four mongrel dogs (2 M, 2 F, weight 25.53 ± 7.3 kg) that underwent subserosal two-channel electrode implantation. Three force transducers sutured serosally along the gastric axis and a wireless signal acquisition system were utilized to record stimulation-generated contractions and tissue impedance variations respectively. Mechanically induced contractions in the stomach were utilized to indirectly generate a tissue impedance change that was detected by the feedback system. Results showed that increasing or decreasing impedance changes were detected by the implantable stimulator and that therapy can be triggered as a result. The implantable feedback system brings NGES one step closer to long term treatment of burdening gastric motility disorders in humans
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Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.
Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua
2016-11-14
In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.
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Synchronization of cellular neural networks of neutral type via dynamic feedback controller
International Nuclear Information System (INIS)
Park, Ju H.
2009-01-01
In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.
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Trait self-esteem and neural activities related to self-evaluation and social feedback
Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui
2016-01-01
Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one’s own personality traits and of others’ opinion about one’s own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one’s own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback. PMID:26842975
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Trait self-esteem and neural activities related to self-evaluation and social feedback.
Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui
2016-02-04
Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback.
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Directory of Open Access Journals (Sweden)
Miriam eZacksenhouse
2015-05-01
Full Text Available Recent experiments with brain-machine-interfaces (BMIs indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.
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Benyamini, Miri; Zacksenhouse, Miriam
2015-01-01
Recent experiments with brain-machine-interfaces (BMIs) indicate that the extent of neural modulations increased abruptly upon starting to operate the interface, and especially after the monkey stopped moving its hand. In contrast, neural modulations that are correlated with the kinematics of the movement remained relatively unchanged. Here we demonstrate that similar changes are produced by simulated neurons that encode the relevant signals generated by an optimal feedback controller during simulated BMI experiments. The optimal feedback controller relies on state estimation that integrates both visual and proprioceptive feedback with prior estimations from an internal model. The processing required for optimal state estimation and control were conducted in the state-space, and neural recording was simulated by modeling two populations of neurons that encode either only the estimated state or also the control signal. Spike counts were generated as realizations of doubly stochastic Poisson processes with linear tuning curves. The model successfully reconstructs the main features of the kinematics and neural activity during regular reaching movements. Most importantly, the activity of the simulated neurons successfully reproduces the observed changes in neural modulations upon switching to brain control. Further theoretical analysis and simulations indicate that increasing the process noise during normal reaching movement results in similar changes in neural modulations. Thus, we conclude that the observed changes in neural modulations during BMI experiments can be attributed to increasing process noise associated with the imperfect BMI filter, and, more directly, to the resulting increase in the variance of the encoded signals associated with state estimation and the required control signal.
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Identification of neural structures involved in stuttering using vibrotactile feedback.
Cheadle, Oliver; Sorger, Clarissa; Howell, Peter
Feedback delivered over auditory and vibratory afferent pathways has different effects on the fluency of people who stutter (PWS). These features were exploited to investigate the neural structures involved in stuttering. The speech signal vibrated locations on the body (vibrotactile feedback, VTF). Eleven PWS read passages under VTF and control (no-VTF) conditions. All combinations of vibration amplitude, synchronous or delayed VTF and vibrator position (hand, sternum or forehead) were presented. Control conditions were performed at the beginning, middle and end of test sessions. Stuttering rate, but not speaking rate, differed between the control and VTF conditions. Notably, speaking rate did not change between when VTF was delayed versus when it was synchronous in contrast with what happens with auditory feedback. This showed that cerebellar mechanisms, which are affected when auditory feedback is delayed, were not implicated in the fluency-enhancing effects of VTF, suggesting that there is a second fluency-enhancing mechanism. Copyright © 2018 Elsevier Inc. All rights reserved.
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Effect of intermittent feedback control on robustness of human-like postural control system
Tanabe, Hiroko; Fujii, Keisuke; Suzuki, Yasuyuki; Kouzaki, Motoki
2016-03-01
Humans have to acquire postural robustness to maintain stability against internal and external perturbations. Human standing has been recently modelled using an intermittent feedback control. However, the causality inside of the closed-loop postural control system associated with the neural control strategy is still unknown. Here, we examined the effect of intermittent feedback control on postural robustness and of changes in active/passive components on joint coordinative structure. We implemented computer simulation of a quadruple inverted pendulum that is mechanically close to human tiptoe standing. We simulated three pairs of joint viscoelasticity and three choices of neural control strategies for each joint: intermittent, continuous, or passive control. We examined postural robustness for each parameter set by analysing the region of active feedback gain. We found intermittent control at the hip joint was necessary for model stabilisation and model parameters affected the robustness of the pendulum. Joint sways of the pendulum model were partially smaller than or similar to those of experimental data. In conclusion, intermittent feedback control was necessary for the stabilisation of the quadruple inverted pendulum. Also, postural robustness of human-like multi-link standing would be achieved by both passive joint viscoelasticity and neural joint control strategies.
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Neural control of magnetic suspension systems
Gray, W. Steven
1993-01-01
The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.
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Two-Layer Feedback Neural Networks with Associative Memories
International Nuclear Information System (INIS)
Gui-Kun, Wu; Hong, Zhao
2008-01-01
We construct a two-layer feedback neural network by a Monte Carlo based algorithm to store memories as fixed-point attractors or as limit-cycle attractors. Special attention is focused on comparing the dynamics of the network with limit-cycle attractors and with fixed-point attractors. It is found that the former has better retrieval property than the latter. Particularly, spurious memories may be suppressed completely when the memories are stored as a long-limit cycle. Potential application of limit-cycle-attractor networks is discussed briefly. (general)
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van Duijvenvoorde, Anna C K; Zanolie, Kiki; Rombouts, Serge A R B; Raijmakers, Maartje E J; Crone, Eveline A
2008-09-17
How children learn from positive and negative performance feedback lies at the foundation of successful learning and is therefore of great importance for educational practice. In this study, we used functional magnetic resonance imaging (fMRI) to examine the neural developmental changes related to feedback-based learning when performing a rule search and application task. Behavioral results from three age groups (8-9, 11-13, and 18-25 years of age) demonstrated that, compared with adults, 8- to 9-year-old children performed disproportionally more inaccurately after receiving negative feedback relative to positive feedback. Additionally, imaging data pointed toward a qualitative difference in how children and adults use performance feedback. That is, dorsolateral prefrontal cortex and superior parietal cortex were more active after negative feedback for adults, but after positive feedback for children (8-9 years of age). For 11- to 13-year-olds, these regions did not show differential feedback sensitivity, suggesting that the transition occurs around this age. Pre-supplementary motor area/anterior cingulate cortex, in contrast, was more active after negative feedback in both 11- to 13-year-olds and adults, but not 8- to 9-year-olds. Together, the current data show that cognitive control areas are differentially engaged during feedback-based learning across development. Adults engage these regions after signals of response adjustment (i.e., negative feedback). Young children engage these regions after signals of response continuation (i.e., positive feedback). The neural activation patterns found in 11- to 13-year-olds indicate a transition around this age toward an increased influence of negative feedback on performance adjustment. This is the first developmental fMRI study to compare qualitative changes in brain activation during feedback learning across distinct stages of development.
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NNSYSID and NNCTRL Tools for system identification and control with neural networks
DEFF Research Database (Denmark)
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
2001-01-01
choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...
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NNSYSID and NNCTRL Tools for system identification and control with neural networks
DEFF Research Database (Denmark)
Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad
2001-01-01
a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...
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Chen, Weisheng; Ge, Shuzhi Sam; Wu, Jian; Gong, Maoguo
2015-09-01
This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is given a priori. In contrast to the classical adaptive backstepping NN control schemes, this paper analyzes the convergence of the tracking error using Barbalat's Lemma via some nonnegative functions rather than the positive-definite Lyapunov functions. Thus, the accuracy of the ultimate tracking error can be determined and adjusted accurately a priori, and the closed-loop system is guaranteed to be globally uniformly ultimately bounded. The main technical novelty is to construct three new n th-order continuously differentiable functions, which are used to design the control law, the virtual control variables, and the adaptive laws. Finally, two simulation examples are given to illustrate the effectiveness and advantages of the proposed control method.
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Energy Technology Data Exchange (ETDEWEB)
Gu, Z.; Rizy, D.T.
1996-02-01
A neural network for controlling shunt capacitor banks and feeder voltage regulators in electric distribution systems is presented. The objective of the neural controller is to minimize total I{sup 2}R losses and maintain all bus voltages within standard limits. The performance of the neural network for different input selections and training data is discussed and compared. Two different input selections are tried, one using the previous control states of the capacitors and regulator along with measured line flows and voltage which is equivalent to having feedback and the other with measured line flows and voltage without previous control settings. The results indicate that the neural net controller with feedback can outperform the one without. Also, proper selection of a training data set that adequately covers the operating space of the distribution system is important for achieving satisfactory performance with the neural controller. The neural controller is tested on a radially configured distribution system with 30 buses, 5 switchable capacitor banks an d one nine tap line regulator to demonstrate the performance characteristics associated with these principles. Monte Carlo simulations show that a carefully designed and relatively compact neural network with a small but carefully developed training set can perform quite well under slight and extreme variation of loading conditions.
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The importance of cutaneous feedback on neural activation during maximal voluntary contraction
Cruz-Montecinos, Carlos; Maas, Huub; Pellegrin-Friedmann, Carla; Tapia, Claudio
2017-01-01
Purpose: The purpose of this study was to investigate the importance of cutaneous feedback on neural activation during maximal voluntary contraction (MVC) of the ankle plantar flexors. Methods: The effects of cutaneous plantar anaesthesia were assessed in 15 subjects and compared to 15 controls,
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International Nuclear Information System (INIS)
Elshazly, A.A.E.
2002-01-01
Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior
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Optimal control of nonlinear continuous-time systems in strict-feedback form.
Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani
2015-10-01
This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.
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International Nuclear Information System (INIS)
Cui Baotong; Lou Xuyang
2009-01-01
In this paper, a new method to synchronize two identical chaotic recurrent neural networks is proposed. Using the drive-response concept, a nonlinear feedback control law is derived to achieve the state synchronization of the two identical chaotic neural networks. Furthermore, based on the Lyapunov method, a delay independent sufficient synchronization condition in terms of linear matrix inequality (LMI) is obtained. A numerical example with graphical illustrations is given to illuminate the presented synchronization scheme
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Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
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Dynamics of nonlinear feedback control
Snippe, H.P.; Hateren, J.H. van
Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain
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Hybrid feedback feedforward: An efficient design of adaptive neural network control.
Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong
2016-04-01
This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Forbes, Chad E; Leitner, Jordan B
2014-10-01
Stereotype threat, a situational pressure individuals experience when they fear confirming a negative group stereotype, engenders a cascade of physiological stress responses, negative appraisals, and performance monitoring processes that tax working memory resources necessary for optimal performance. Less is known, however, about how stereotype threat biases attentional processing in response to performance feedback, and how such attentional biases may undermine performance. Women received feedback on math problems in stereotype threatening compared to stereotype-neutral contexts while continuous EEG activity was recorded. Findings revealed that stereotype threatened women elicited larger midline P100 ERPs, increased phase locking between anterior cingulate cortex and dorsolateral prefrontal cortex (two regions integral for attentional processes), and increased power in left fusiform gyrus in response to negative feedback compared to positive feedback and women in stereotype-neutral contexts. Increased power in left fusiform gyrus in response to negative feedback predicted underperformance on the math task among stereotype threatened women only. Women in stereotype-neutral contexts exhibited the opposite trend. Findings suggest that in stereotype threatening contexts, neural networks integral for attention and working memory are biased toward negative, stereotype confirming feedback at very early speeds of information processing. This bias, in turn, plays a role in undermining performance. Copyright © 2014 Elsevier B.V. All rights reserved.
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Decoupling control of vehicle chassis system based on neural network inverse system
Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke
2018-06-01
Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.
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International Nuclear Information System (INIS)
Lonza, M; Schmickler, H
2014-01-01
Coupled-bunch instabilities excited by the interaction of the particle beam with its surroundings can seriously limit the performance of circular particle accelerators. These instabilities can be cured by the use of active feedback systems based on sensors capable of detecting the unwanted beam motion and actuators that apply the feedback correction to the beam. Advances in electronic technology now allow the implementation of feedback loops using programmable digital systems. Besides important advantages in terms of flexibility and reproducibility, digital systems open the way to the use of novel diagnostic tools and additional features. We first introduce coupled-bunch instabilities, analysing the equation of motion of charged particles and the different modes of oscillation of a multi-bunch beam, showing how they can be observed and measured. Different types of feedback systems will then be presented as examples of real implementations that belong to the history of multi-bunch feedback systems. The main components of a feedback system and the related issues will also be analysed. Finally, we shall focus on digital feedback systems, their characteristics, and features, as well as on how they can be concretely exploited for both the optimization of feedback performance and for beam dynamics studies
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1978-11-01
R 2. GOVT A $ SION NO. 3 RIEqLPýIVT’S.;TALOG NUMBER r/ 4. TITLE (and wbiFflT, -L M4 1 , FEEDBACK SYSTEM THEORY ~r Inter in- 6. PERFORMING ORG. REPORT...ANNUAL REPORT FEEDBACK SYSTEM THEORY AFOSR GRANT NO. 76-2946B Air Force Office of Scientific Research for year ending October 31, 1978 79 02 08 L|I...re less stringent than in other synthesis techniques which cannot handle significant parameter uncertainty. _I FEEDBACK SYSTEM THEORY 1. Introduction
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Evolvable synthetic neural system
Curtis, Steven A. (Inventor)
2009-01-01
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
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Neural dynamics of feedforward and feedback processing in figure-ground segregation.
Layton, Oliver W; Mingolla, Ennio; Yazdanbakhsh, Arash
2014-01-01
Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure's interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.
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Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation
Directory of Open Access Journals (Sweden)
Oliver W. Layton
2014-09-01
Full Text Available Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure’s interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells, and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells. Neurons (convex cells that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation.
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Neural dynamics of feedforward and feedback processing in figure-ground segregation
Layton, Oliver W.; Mingolla, Ennio; Yazdanbakhsh, Arash
2014-01-01
Determining whether a region belongs to the interior or exterior of a shape (figure-ground segregation) is a core competency of the primate brain, yet the underlying mechanisms are not well understood. Many models assume that figure-ground segregation occurs by assembling progressively more complex representations through feedforward connections, with feedback playing only a modulatory role. We present a dynamical model of figure-ground segregation in the primate ventral stream wherein feedback plays a crucial role in disambiguating a figure's interior and exterior. We introduce a processing strategy whereby jitter in RF center locations and variation in RF sizes is exploited to enhance and suppress neural activity inside and outside of figures, respectively. Feedforward projections emanate from units that model cells in V4 known to respond to the curvature of boundary contours (curved contour cells), and feedback projections from units predicted to exist in IT that strategically group neurons with different RF sizes and RF center locations (teardrop cells). Neurons (convex cells) that preferentially respond when centered on a figure dynamically balance feedforward (bottom-up) information and feedback from higher visual areas. The activation is enhanced when an interior portion of a figure is in the RF via feedback from units that detect closure in the boundary contours of a figure. Our model produces maximal activity along the medial axis of well-known figures with and without concavities, and inside algorithmically generated shapes. Our results suggest that the dynamic balancing of feedforward signals with the specific feedback mechanisms proposed by the model is crucial for figure-ground segregation. PMID:25346703
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Dynamics of nonlinear feedback control
Snippe, H.P.; Hateren, J.H. van
2007-01-01
Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input step...
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Daniel, Reka; Pollmann, Stefan
2010-01-06
The dopaminergic system is known to play a central role in reward-based learning (Schultz, 2006), yet it was also observed to be involved when only cognitive feedback is given (Aron et al., 2004). Within the domain of information-integration category learning, in which information from several stimulus dimensions has to be integrated predecisionally (Ashby and Maddox, 2005), the importance of contingent feedback is well established (Maddox et al., 2003). We examined the common neural correlates of reward anticipation and prediction error in this task. Sixteen subjects performed two parallel information-integration tasks within a single event-related functional magnetic resonance imaging session but received a monetary reward only for one of them. Similar functional areas including basal ganglia structures were activated in both task versions. In contrast, a single structure, the nucleus accumbens, showed higher activation during monetary reward anticipation compared with the anticipation of cognitive feedback in information-integration learning. Additionally, this activation was predicted by measures of intrinsic motivation in the cognitive feedback task and by measures of extrinsic motivation in the rewarded task. Our results indicate that, although all other structures implicated in category learning are not significantly affected by altering the type of reward, the nucleus accumbens responds to the positive incentive properties of an expected reward depending on the specific type of the reward.
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Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks
Directory of Open Access Journals (Sweden)
Minoru Sasaki
2012-01-01
Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.
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Lonza, M.
2014-12-19
Coupled-bunch instabilities excited by the interaction of the particle beam with its surroundings can seriously limit the performance of circular particle accelerators. These instabilities can be cured by the use of active feedback systems based on sensors capable of detecting the unwanted beam motion and actuators that apply the feedback correction to the beam. Advances in electronic technology now allow the implementation of feedback loops using programmable digital systems. Besides important advantages in terms of flexibility and reproducibility, digital systems open the way to the use of novel diagnostic tools and additional features. We first introduce coupled-bunch instabilities, analysing the equation of motion of charged particles and the different modes of oscillation of a multi-bunch beam, showing how they can be observed and measured. Different types of feedback systems will then be presented as examples of real implementations that belong to the history of multi-bunch feedback systems. The main co...
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Feedback Systems for Linear Colliders
International Nuclear Information System (INIS)
1999-01-01
Feedback systems are essential for stable operation of a linear collider, providing a cost-effective method for relaxing tight tolerances. In the Stanford Linear Collider (SLC), feedback controls beam parameters such as trajectory, energy, and intensity throughout the accelerator. A novel dithering optimization system which adjusts final focus parameters to maximize luminosity contributed to achieving record performance in the 1997-98 run. Performance limitations of the steering feedback have been investigated, and improvements have been made. For the Next Linear Collider (NLC), extensive feedback systems are planned as an integral part of the design. Feedback requirements for JLC (the Japanese Linear Collider) are essentially identical to NLC; some of the TESLA requirements are similar but there are significant differences. For NLC, algorithms which incorporate improvements upon the SLC implementation are being prototyped. Specialized systems for the damping rings, rf and interaction point will operate at high bandwidth and fast response. To correct for the motion of individual bunches within a train, both feedforward and feedback systems are planned. SLC experience has shown that feedback systems are an invaluable operational tool for decoupling systems, allowing precision tuning, and providing pulse-to-pulse diagnostics. Feedback systems for the NLC will incorporate the key SLC features and the benefits of advancing technologies
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Lonza, M
2008-01-01
Coupled-bunch instabilities excited by the interaction of the particle beam with its surroundings can seriously limit the performance of circular particle accelerators. These instabilities can be cured by the use of active feedback systems based on sensors capable of detecting the unwanted beam motion and actuators that apply the feedback correction to the beam. The advances in electronic technology now allow the implementation of feedback loops using programmable digital systems. Besides important advantages in terms of flexibility and reproducibility, digital systems open the way to the use of novel diagnostic tools and additional features. The lecture will first introduce coupled-bunch instabilities analysing the equation of motion of charged particles and the different modes of oscillation of a multi-bunch beam, showing how they can be observed and measured. Different types of feedbacks systems will then be presented as examples of real implementations that belong to the history of multi-bunch feedback sy...
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Partial state feedback control of chaotic neural network and its application
International Nuclear Information System (INIS)
He Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki
2007-01-01
The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition
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Output feedback control of a quadrotor UAV using neural networks.
Dierks, Travis; Jagannathan, Sarangapani
2010-01-01
In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.
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Evolvable Neural Software System
Curtis, Steven A.
2009-01-01
The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.
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Temporal neural networks and transient analysis of complex engineering systems
Uluyol, Onder
A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.
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ASSESSMENT OF LIBRARY USERS’ FEEDBACK USING MODIFIED MULTILAYER PERCEPTRON NEURAL NETWORKS
Directory of Open Access Journals (Sweden)
K G Nandha Kumar
2017-07-01
Full Text Available An attempt has been made to evaluate the feedbacks of library users of four different libraries by using neural network based data mining techniques. This paper presents the results of a survey of users’ satisfactory level on four different libraries. The survey has been conducted among the users of four libraries of educational institutions of Kovai Medical Center Research and Educational Trust. Data were collected through questionnaires. Artificial neural network based data mining techniques are proposed and applied to assess the libraries in terms of level of satisfaction of users. In order to assess the users’ satisfaction level, two neural network techniques: Modified Multilayer Perceptron Network-Supervised and Modified Multilayer Perceptron Network-Unsupervised are proposed. The proposed techniques are compared with the conventional classification algorithm Multilayer Perceptron Neural Network and found better in overall performance. It is found that the quality of service provided by the libraries is highly good and users are highly satisfied with various aspects of library service. The Arts and Science College Library secured the maximum percent in terms of user satisfaction. This shows that the users’ satisfaction of ASCL is better than the other libraries. This study provides an insight into the actual quality and satisfactory level of users of libraries after proper assessment. It is strongly expected that the results will help library authorities to enhance services and quality in the near future.
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Adaptive neural networks control for camera stabilization with active suspension system
Directory of Open Access Journals (Sweden)
Feng Zhao
2015-08-01
Full Text Available The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.
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Lonza, M.; Schmickler, H.
2016-01-01
Coupled-bunch instabilities excited by the interaction of the particle beam with its surroundings can seriously limit the performance of circular particle accelerators. These instabilities can be cured by the use of active feedback systems based on sensors capable of detecting the unwanted beam motion and actuators that apply the feedback correction to the beam. Advances in electronic technology now allow the implementation of feedback loops using programmable digital systems. Besides importa...
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Optimization of workflow scheduling in Utility Management System with hierarchical neural network
Directory of Open Access Journals (Sweden)
Srdjan Vukmirovic
2011-08-01
Full Text Available Grid computing could be the future computing paradigm for enterprise applications, one of its benefits being that it can be used for executing large scale applications. Utility Management Systems execute very large numbers of workflows with very high resource requirements. This paper proposes architecture for a new scheduling mechanism that dynamically executes a scheduling algorithm using feedback about the current status Grid nodes. Two Artificial Neural Networks were created in order to solve the scheduling problem. A case study is created for the Meter Data Management system with measurements from the Smart Metering system for the city of Novi Sad, Serbia. Performance tests show that significant improvement of overall execution time can be achieved by Hierarchical Artificial Neural Networks.
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Systematic Self-Regulation of the Neural System Essential for Peak Performance and Wellbeing.
Cassel, Russell N.
1985-01-01
Balance and harmony within one's neural system is dynamic and changing, and restoring that balance is essential for peak performance. With a minimum amount of training individuals are able to restore this delicate balance and thereby enhance their own wellbeing. Autogenic feedback training has been demonstrated to be an effective means for…
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Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback
Directory of Open Access Journals (Sweden)
Maode Yan
2017-11-01
Full Text Available This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations. In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.
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Feedback systems for linear colliders
Hendrickson, L; Himel, Thomas M; Minty, Michiko G; Phinney, N; Raimondi, Pantaleo; Raubenheimer, T O; Shoaee, H; Tenenbaum, P G
1999-01-01
Feedback systems are essential for stable operation of a linear collider, providing a cost-effective method for relaxing tight tolerances. In the Stanford Linear Collider (SLC), feedback controls beam parameters such as trajectory, energy, and intensity throughout the accelerator. A novel dithering optimization system which adjusts final focus parameters to maximize luminosity contributed to achieving record performance in the 1997-98 run. Performance limitations of the steering feedback have been investigated, and improvements have been made. For the Next Linear Collider (NLC), extensive feedback systems are planned as an intregal part of the design. Feedback requiremetns for JLC (the Japanese Linear Collider) are essentially identical to NLC; some of the TESLA requirements are similar but there are significant differences. For NLC, algorithms which incorporate improvements upon the SLC implementation are being prototyped. Specialized systems for the damping rings, rf and interaction point will operate at hi...
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Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks
Directory of Open Access Journals (Sweden)
Zhisheng Zhang
2016-01-01
Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.
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Operation of PEP longitudinal feedback system
International Nuclear Information System (INIS)
Allen, M.A.; Karvonen, L.G.; McConnell, R.A.; Schwarz, H.
1981-03-01
In order to suppress longitudinal coupled-bunch oscillations which might limit the capabilities of PEP, the 18 GeV e + e - storage ring at SLAC, a longitudinal feedback system is utilized. A frequency domain feedback system was chosen with the frequency spectrum of the stored beam being sampled close to a symmetry point in the ring where the feedback cavity itself is also located. The symmetry point chosen is symmetry point 5 which lies half-way between interaction regions 4 and 6. The system has been installed in PEP and is now operational. However, at stored currents up to the maximum stored in PEP to date at 14.5 GeV (approximately 40 mA in 6 bunches), the ring has been stable to all modes of longitudinal coupled-bunch oscillations both barycentric and the other fundamental modes. By deliberately detuning the main accelerating cavities, small multibunch oscillations can be introduced which, in turn, can be damped by the feedback system. Under optimized beam conditions the feedback system could be adjusted to positive feedback and excite oscillations with relatively small power to the feedback cavity. This will be described along with other details of the system
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Theory of multi-bunch feedback systems
International Nuclear Information System (INIS)
Kohaupt, R.D.
1991-06-01
In this article the theory of multibunch feedback systems is developed in a rigorous way including the fact that the elements of feedback systems are localized in the ring. The results of the theory which can be used for any strength of the systems are the base for the multibunch feedback systems for PETRA and HERA, already tested successfully in PETRA. (orig.)
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Feedbacks in human-landscape systems
Chin, Anne
2015-04-01
As human interactions with Earth systems intensify in the "Anthropocene", understanding the complex relationships among human activity, landscape change, and societal responses to those changes is increasingly important. Interdisciplinary research centered on the theme of "feedbacks" in human-landscape systems serves as a promising focus for unraveling these interactions. Deciphering interacting human-landscape feedbacks extends our traditional approach of considering humans as unidirectional drivers of change. Enormous challenges exist, however, in quantifying impact-feedback loops in landscapes with significant human alterations. This paper illustrates an example of human-landscape interactions following a wildfire in Colorado (USA) that elicited feedback responses. After the 2012 Waldo Canyon Fire, concerns for heightened flood potential and debris flows associated with post-fire hydrologic changes prompted local landowners to construct tall fences at the base of a burned watershed. These actions changed the sediment transport regime and promoted further landscape change and human responses in a positive feedback cycle. The interactions ultimately increase flood and sediment hazards, rather than dampening the effects of fire. A simple agent-based model, capable of integrating social and hydro-geomorphological data, demonstrates how such interacting impacts and feedbacks could be simulated. Challenges for fully capturing human-landscape feedback interactions include the identification of diffuse and subtle feedbacks at a range of scales, the availability of data linking impact with response, the identification of multiple thresholds that trigger feedback mechanisms, and the varied metrics and data needed to represent both the physical and human systems. By collaborating with social scientists with expertise in the human causes of landscape change, as well as the human responses to those changes, geoscientists could more fully recognize and anticipate the coupled
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CloudScan - A Configuration-Free Invoice Analysis System Using Recurrent Neural Networks
DEFF Research Database (Denmark)
Palm, Rasmus Berg; Winther, Ole; Laws, Florian
2017-01-01
We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts....... The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline...... logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts...
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Finger, Herbert; Weeks, Bill
1985-01-01
This presentation discusses instrumentation that will be used for a specific event, which we hope will carry on to future events within the Space Shuttle program. The experiment is the Autogenic Feedback Training Experiment (AFTE) scheduled for Spacelab 3, currently scheduled to be launched in November, 1984. The objectives of the AFTE are to determine the effectiveness of autogenic feedback in preventing or reducing space adaptation syndrome (SAS), to monitor and record in-flight data from the crew, to determine if prediction criteria for SAS can be established, and, finally, to develop an ambulatory instrument package to mount the crew throughout the mission. The purpose of the Ambulatory Feedback System (AFS) is to record the responses of the subject during a provocative event in space and provide a real-time feedback display to reinforce the training.
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Fei, Juntao; Lu, Cheng
2018-04-01
In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.
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Bunch by bunch feedback systems
International Nuclear Information System (INIS)
Tobiyama, Makoto
2006-01-01
Outlines of bunch-by-bunch feedback systems for suppressing multibunch instabilities in electron/positron storage rings are presented. The design principles and functions of the feedback components are reviewed. Recent topics of applying very fast and dense FPGA as feedback signal processor are also shown. (author)
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Neural Feedback Scheduling of Real-Time Control Tasks
Xia, Feng; Tian, Yu-Chu; Sun, Youxian; Dong, Jinxiang
2008-01-01
Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimi...
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Computational aspects of feedback in neural circuits.
Directory of Open Access Journals (Sweden)
Wolfgang Maass
2007-01-01
Full Text Available It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also
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Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)
2001-01-01
A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.
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Directory of Open Access Journals (Sweden)
Luisa F. Escobar-Dávila
2013-06-01
Full Text Available This paper presents the mathematical modeling of the Furuta Pendu-lum by power functions, taking into account the non linear own dynamics of the physical systems and considering the existing couplings between the electric and mechanic devices. A control process based on feedback of state variables (FSV for the equilibrium point is developed and two topics for the non linear zone are addressed. First of all, functions are implemented to represent the energetic states of the plant in a global way and the operation regions are established (“Swing up” zone, and later Artificial Neural Networks (ANN are employed to simulate the behavior of the energy functions. Finally, it is presented the combination between the control techniques, considering the own constrains of the actuators and sensors used, besides of this, a study is done in a simulated environment of the physical phenomena that may disturb system behavior, and the capacity, sensitivity and robustness of the controller is verified.
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Toyomaki, Atsuhito; Hashimoto, Naoki; Kako, Yuki; Murohashi, Harumitsu; Kusumi, Ichiro
2017-01-01
Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.
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Adaptive feedback synchronization of Lue system
International Nuclear Information System (INIS)
Han, X.; Lu, J.-A.; Wu, X.
2004-01-01
This letter further improves and extends the works of Chen and Lue [Chaos, Solitons and Fractals 14 (2002) 643] and Wang et al. [Phys. Lett. A 312 (2003) 34]. In detail, the linear feedback synchronization and adaptive feedback synchronization for Lue system are discussed. And the lower bound of the feedback gain in linear feedback synchronization is presented. The adaptive feedback synchronization with only one controller is designed, which improves the proof in the work by Wang et al. The adaptive synchronization with two controllers for completely uncertain Lue system is also discussed, which extends the work of Chen and Lue. Also, numerical simulations show the effectiveness of these methods
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Marcar, Valentine L; Baselgia, Silvana; Lüthi-Eisenegger, Barbara; Jäncke, Lutz
2018-03-01
Retinal input processing in the human visual system involves a phasic and tonic neural response. We investigated the role of the magno- and parvocellular systems by comparing the influence of the active neural population size and its discharge activity on the amplitude and latency of four VEP components. We recorded the scalp electric potential of 20 human volunteers viewing a series of dartboard images presented as a pattern reversing and pattern on-/offset stimulus. These patterns were designed to vary both neural population size coding the temporal- and spatial luminance contrast property and the discharge activity of the population involved in a systematic manner. When the VEP amplitude reflected the size of the neural population coding the temporal luminance contrast property of the image, the influence of luminance contrast followed the contrast response function of the parvocellular system. When the VEP amplitude reflected the size of the neural population responding to the spatial luminance contrast property the image, the influence of luminance contrast followed the contrast response function of the magnocellular system. The latencies of the VEP components examined exhibited the same behavior across our stimulus series. This investigation demonstrates the complex interplay of the magno- and parvocellular systems on the neural response as captured by the VEP. It also demonstrates a linear relationship between stimulus property, neural response, and the VEP and reveals the importance of feedback projections in modulating the ongoing neural response. In doing so, it corroborates the conclusions of our previous study.
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Recognition of boundary feedback systems
DEFF Research Database (Denmark)
Pedersen, Michael
1989-01-01
A system that has been the object of intense research is outlined. In view of that and recent progress of the theory of pseudodifferential boundary operator calculus, the author describes some features that could prove to be interesting in connection with the problems of boundary feedback stabili...... stabilizability. It is shown that it is possible to use the calculus to consider more general feedback systems in a variational setup.......A system that has been the object of intense research is outlined. In view of that and recent progress of the theory of pseudodifferential boundary operator calculus, the author describes some features that could prove to be interesting in connection with the problems of boundary feedback...
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Hitokoto, Hidefumi; Glazer, James; Kitayama, Shinobu
2016-01-01
Previous work shows that when an image of a face is presented immediately prior to each trial of a speeded cognitive task (face-priming), the error-related negativity (ERN) is upregulated for Asians, but it is downregulated for Caucasians. These findings are consistent with the hypothesis that images of "generalized other" vary cross-culturally such that they evoke anxiety for Asians, whereas they serve as safety cues for Caucasians. Here, we tested whether the cross-cultural variation in the face-priming effect would be observed in a gambling paradigm. Caucasian Americans, Asian Americans, and Asian sojourners were exposed to a brief flash of a schematic face during a gamble. For Asian Americans, face-priming resulted in significant increases of both negative-going deflection of ERP upon negative feedback (feedback-related negativity [FRN]) and positive-going deflection of ERP upon positive feedback (feedback-related positivity [FRP]). For Caucasian Americans, face-priming showed a significant reversal, decreasing both FRN and FRP. The cultural difference in the face-priming effect in FRN and FRP was partially mediated by interdependent self-construal. Curiously, Asian sojourners showed a pattern similar to the one for Caucasian Americans. Our findings suggest that culture shapes neural pathways in both systematic and highly dynamic fashion. © 2015 Society for Psychophysiological Research.
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An overview of neural function and feedback control in human communication.
Hood, L J
1998-01-01
The speech and hearing mechanisms depend on accurate sensory information and intact feedback mechanisms to facilitate communication. This article provides a brief overview of some components of the nervous system important for human communication and some electrophysiological methods used to measure cortical function in humans. An overview of automatic control and feedback mechanisms in general and as they pertain to the speech motor system and control of the hearing periphery is also presented, along with a discussion of how the speech and auditory systems interact.
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Feedback-linearization and feedback-feedforward decentralized control for multimachine power system
Energy Technology Data Exchange (ETDEWEB)
De Tuglie, Enrico [Dipartimento di Ingegneria dell' Ambiente, e per lo Sviluppo Sostenibile - DIASS, Politecnico di Bari, Viale del Turismo 8, 74100 Taranto (Italy); Iannone, Silvio Marcello; Torelli, Francesco [Dipartimento di Elettrotecnica, ed Elettronica - DEE, Politecnico di Bari, Via Re David 200, 70125 Bari (Italy)
2008-03-15
In this paper a decentralized nonlinear controller for large-scale power systems is investigated. The proposed controller design is based on the input-output feedback linearization methodology. In order to overcome computational difficulties in adopting such methodology, the overall interconnected nonlinear system, given as n-order, is analyzed as a cascade connection of an n{sub 1}-order nonlinear subsystem and an n{sub 2}-order linear subsystem. The controller design is obtained by applying input-output feedback linearization to the nonlinear subsystem and adopting a tracking control scheme, based on feedback-feedforward technique, for the linear subsystem. In the assumed system model, which is characterised by an interconnected structure between generating units, a decentralised adaptive controller is implemented by decentralizing these constraints. The use of a totally decentralised controller implies a system performance decay with respect to performance when the system is equipped with a centralised controller. Fortunately, the robustness of the proposed controller, based on input-output feedback procedure, guarantees good performance in terms of disturbance even when disturbances are caused by decentralization of interconnection constraints. Test results, provided on the IEEE 30 bus test system, demonstrate the effectiveness and practical applicability of proposed methodology. (author)
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Directory of Open Access Journals (Sweden)
Atsuhito Toyomaki
Full Text Available Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.
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Feedback-Equivalence of Nonlinear Systems with Applications to Power System Equations.
Marino, Riccardo
The key concept of the dissertation is feedback equivalence among systems affine in control. Feedback equivalence to linear systems in Brunovsky canonical form and the construction of the corresponding feedback transformation are used to: (i) design a nonlinear regulator for a detailed nonlinear model of a synchronous generator connected to an infinite bus; (ii) establish which power system network structures enjoy the feedback linearizability property and design a stabilizing control law for these networks with a constraint on the control space which comes from the use of d.c. lines. It is also shown that the feedback linearizability property allows the use of state feedback to contruct a linear controllable system with a positive definite linear Hamiltonian structure for the uncontrolled part if the state space is even; a stabilizing control law is derived for such systems. Feedback linearizability property is characterized by the involutivity of certain nested distributions for strongly accessible analytic systems; if the system is defined on a manifold M diffeomorphic to the Euclidean space, it is established that the set where the property holds is a submanifold open and dense in M. If an analytic output map is defined, a set of nested involutive distributions can be always defined and that allows the introduction of an observability property which is the dual concept, in some sense, to feedback linearizability: the goal is to investigate when a nonlinear system affine in control with an analytic output map is feedback equivalent to a linear controllable and observable system. Finally a nested involutive structure of distributions is shown to guarantee the existence of a state feedback that takes a nonlinear system affine in control to a single input one, both feedback equivalent to linear controllable systems, preserving one controlled vector field.
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Empirical modeling of nuclear power plants using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, A.; Chong, K.T.
1991-01-01
A summary of a procedure for nonlinear identification of process dynamics encountered in nuclear power plant components is presented in this paper using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the nonlinear structure for system identification. In the overall identification process, the feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of time-dependent system nonlinearities. The standard backpropagation learning algorithm is modified and is used to train the proposed hybrid network in a supervised manner. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The nonlinear response of a representative steam generator is predicted using a neural network and is compared to the response obtained from a sophisticated physical model during both high- and low-power operation. The transient responses compare well, though further research is warranted for training and testing of recurrent neural networks during more severe operational transients and accident scenarios
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Feedback coupling in dynamical systems
Trimper, Steffen; Zabrocki, Knud
2003-05-01
Different evolution models are considered with feedback-couplings. In particular, we study the Lotka-Volterra system under the influence of a cumulative term, the Ginzburg-Landau model with a convolution memory term and chemical rate equations with time delay. The memory leads to a modified dynamical behavior. In case of a positive coupling the generalized Lotka-Volterra system exhibits a maximum gain achieved after a finite time, but the population will die out in the long time limit. In the opposite case, the time evolution is terminated in a crash. Due to the nonlinear feedback coupling the two branches of a bistable model are controlled by the the strength and the sign of the memory. For a negative coupling the system is able to switch over between both branches of the stationary solution. The dynamics of the system is further controlled by the initial condition. The diffusion-limited reaction is likewise studied in case the reacting entities are not available simultaneously. Whereas for an external feedback the dynamics is altered, but the stationary solution remain unchanged, a self-organized internal feedback leads to a time persistent solution.
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Mobile robot nonlinear feedback control based on Elman neural network observer
Directory of Open Access Journals (Sweden)
Khaled Al-Mutib
2015-12-01
Full Text Available This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input–output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.
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Development of a longitudinal feedback cavity for the beam feedback system
International Nuclear Information System (INIS)
Huang Gang; Chen Huaibi; Huang Wenhui; Tong Dechun; Lin Yuzheng; Zhao Zhentang
2003-01-01
Longitudinal beam feedback system is widely used to damp coupling bunch instability. Kicker is one of the key components of the longitudinal feedback system. A prototype cavity of longitudinal feedback kicker is developed according to the parameter of BEPC II. The usage of nose cone in the kicker design increased the shunt impedance. In order to avoid the extra tapper in the storage ring, the racetrack shape beam pipe is applied in the kicker. The impedance and the bandwidth of the kicker is measured by the coaxial line impedance measurement platform and the result achieved the design goals
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Feedback control strategies for the Liu chaotic system
International Nuclear Information System (INIS)
Zhu Congxu; Chen Zhigang
2008-01-01
This Letter proposed three strategies of the dislocated feedback control, enhancing feedback control and speed feedback control of the Liu chaotic system to its unstable equilibrium points. It is found that the coefficients of enhancing feedback control and speed feedback control are smaller than those of ordinary feedback control, so, the complexity and cost of the system control are reduced. Theoretical analysis and numerical simulation are given, revealing the effectiveness of these strategies
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Adaptive nonlinear control using input normalized neural networks
International Nuclear Information System (INIS)
Leeghim, Henzeh; Seo, In Ho; Bang, Hyo Choong
2008-01-01
An adaptive feedback linearization technique combined with the neural network is addressed to control uncertain nonlinear systems. The neural network-based adaptive control theory has been widely studied. However, the stability analysis of the closed-loop system with the neural network is rather complicated and difficult to understand, and sometimes unnecessary assumptions are involved. As a result, unnecessary assumptions for stability analysis are avoided by using the neural network with input normalization technique. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. A new simple update law as an adaptive nonlinear control is derived by the simplification of the input normalized neural network assuming the variation of the uncertain term is sufficiently small
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Zamir, Amir R.; Wu, Te-Lin; Sun, Lin; Shen, William; Malik, Jitendra; Savarese, Silvio
2016-01-01
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through feedforward multilayer neural networks, e.g. ConvNets, where each layer forms one of such successive representations. However, an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iteration's...
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International Nuclear Information System (INIS)
Cao Jinde
2004-01-01
In this Letter, the domain of attraction of memory patterns and exponential convergence rate of the network trajectories to memory patterns for Hopfield continuous associative memory are estimated by means of matrix measure and comparison principle. A new estimation is given for the domain of attraction of memory patterns and exponential convergence rate. These results can be used for the evaluation of fault-tolerance capability and the synthesis procedures for Hopfield continuous feedback associative memory neural networks
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Electroencephalogy (EEG) Feedback in Decision-Making
2015-08-26
Electroencephalogy ( EEG ) Feedback In Decision- Making The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful...feedback when training rapid decision-making. More specifically, EEG will allow us to provide online feedback about the neural decision processes...Electroencephalogy ( EEG ) Feedback In Decision-Making Report Title The goal of this project is to investigate whether Electroencephalogy ( EEG ) can provide useful
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Truncated predictor feedback for time-delay systems
Zhou, Bin
2014-01-01
This book provides a systematic approach to the design of predictor based controllers for (time-varying) linear systems with either (time-varying) input or state delays. Differently from those traditional predictor based controllers, which are infinite-dimensional static feedback laws and may cause difficulties in their practical implementation, this book develops a truncated predictor feedback (TPF) which involves only finite dimensional static state feedback. Features and topics: A novel approach referred to as truncated predictor feedback for the stabilization of (time-varying) time-delay systems in both the continuous-time setting and the discrete-time setting is built systematically Semi-global and global stabilization problems of linear time-delay systems subject to either magnitude saturation or energy constraints are solved in a systematic manner Both stabilization of a single system and consensus of a group of systems (multi-agent systems) are treated in a unified manner by applying the truncated pre...
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RHIC 10 Hz global orbit feedback system
International Nuclear Information System (INIS)
Michnoff, R.; Arnold, L.; Carboni, L.; Cerniglia, P.; Curcio, A.; DeSanto, L.; Folz, C.; Ho, C.; Hoff, L.; Hulsart, R.; Karl, R.; Luo, Y.; Liu, C.; MacKay, W.; Mahler, G.; Meng, W.; Mernick, K.; Minty, M.; Montag, C.; Olsen, R.; Piacentino, J.; Popken, P.; Przybylinski, R.; Ptitsyn, V.; Ritter, J.; Schoenfeld, R.; Thieberger, P.; Tuozzolo, J.; Weston, A.; White, J.; Ziminski, P.; Zimmerman, P.
2011-01-01
Vibrations of the cryogenic triplet magnets at the Relativistic Heavy Ion Collider (RHIC) are suspected to be causing the horizontal beam perturbations observed at frequencies around 10 Hz. Several solutions to counteract the effect have been considered in the past, including a local beam feedback system at each of the two experimental areas, reinforcing the magnet base support assembly, and a mechanical servo feedback system. However, the local feedback system was insufficient because perturbation amplitudes outside the experimental areas were still problematic, and the mechanical solutions are very expensive. A global 10 Hz orbit feedback system consisting of 36 beam position monitors (BPMs) and 12 small dedicated dipole corrector magnets in each of the two 3.8 km circumference counter-rotating rings has been developed and commissioned in February 2011. A description of the system architecture and results with beam will be discussed.
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Directory of Open Access Journals (Sweden)
Francisco Resquín
2016-07-01
Full Text Available Hybrid robotic systems represent a novel research field, where functional electrical stimulation (FES is combined with a robotic device for rehabilitation of motor impairment. Under this approach, the design of robust FES controllers still remains an open challenge. In this work, we aimed at developing a learning FES controller to assist in the performance of reaching movements in a simple hybrid robotic system setting. We implemented a Feedback Error Learning (FEL control strategy consisting of a feedback PID controller and a feedforward controller based on a neural network. A passive exoskeleton complemented the FES controller by compensating the effects of gravity. We carried out experiments with healthy subjects to validate the performance of the system. Results show that the FEL control strategy is able to adjust the FES intensity to track the desired trajectory accurately without the need of a previous mathematical model.
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Neural correlates of feedback processing in toddlers
Meyer, M.; Bekkering, H.; Janssen, D.J.C.; Bruijn, E.R.A. de; Hunnius, S.
2014-01-01
External feedback provides essential information for successful learning. Feedback is especially important for learning in early childhood, as toddlers strongly rely on external signals to determine the consequences of their actions. In adults, many electrophysiological studies have elucidated
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Effects of Response-Driven Feedback in Computer Science Learning
Fernandez Aleman, J. L.; Palmer-Brown, D.; Jayne, C.
2011-01-01
This paper presents the results of a project on generating diagnostic feedback for guided learning in a first-year course on programming and a Master's course on software quality. An online multiple-choice questions (MCQs) system is integrated with neural network-based data analysis. Findings about how students use the system suggest that the…
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Daresbury SRS Positional Feedback Systems
Smith, S L
2000-01-01
The Daresbury SRS is a second generation synchrotron radiation source which ramps from its injection energy of 600 MeV to 2.0 GeV. Beam orbit feedback systems have been in routine operation on the SRS since 1994 and are now an essential element in delivering stable photon beams to experimental stations. The most recent enhancements to these systems have included the introduction of a ramp servo system to provide the orbit control demanded by the installation of two new narrow gap insertion device and development of the vertical orbit feedback system to cope with an increasing number of photon beamlines. This paper summaries the current status of these systems and briefly discusses proposed developments.
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van der Molen, Melle J W; Harrewijn, Anita; Westenberg, P Michiel
2018-03-07
The current study examined neural and behavioral responses to social-evaluative feedback processing in social anxiety. Twenty-two non-socially and 17 socially anxious females (mean age = 19.57 years) participated in a Social Judgment Paradigm in which they received peer acceptance/rejection feedback that was either congruent or incongruent with their prior predictions. Results indicated that socially anxious participants believed they would receive less social acceptance feedback than non-socially anxious participants. EEG results demonstrated that unexpected social rejection feedback elicited a significant increase in theta (4-8 Hz) power relative to other feedback conditions. This theta response was only observed in non-socially anxious individuals. Together, results corroborate cognitive-behavioral studies demonstrating a negative expectancy bias in socially anxiety with respect to social evaluation. Furthermore, the present findings highlight a functional role for theta oscillatory dynamics in processing cues that convey social-evaluative threat, and this social threat-monitoring mechanism seems less sensitive in socially anxious females. Copyright © 2018 Elsevier B.V. All rights reserved.
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A feedback model of visual attention.
Spratling, M W; Johnson, M H
2004-03-01
Feedback connections are a prominent feature of cortical anatomy and are likely to have a significant functional role in neural information processing. We present a neural network model of cortical feedback that successfully simulates neurophysiological data associated with attention. In this domain, our model can be considered a more detailed, and biologically plausible, implementation of the biased competition model of attention. However, our model is more general as it can also explain a variety of other top-down processes in vision, such as figure/ground segmentation and contextual cueing. This model thus suggests that a common mechanism, involving cortical feedback pathways, is responsible for a range of phenomena and provides a unified account of currently disparate areas of research.
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Si, Wenjie; Dong, Xunde; Yang, Feifei
2018-03-01
This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Passivation and control of partially known SISO nonlinear systems via dynamic neural networks
Directory of Open Access Journals (Sweden)
Reyes-Reyes J.
2000-01-01
Full Text Available In this paper, an adaptive technique is suggested to provide the passivity property for a class of partially known SISO nonlinear systems. A simple Dynamic Neural Network (DNN, containing only two neurons and without any hidden-layers, is used to identify the unknown nonlinear system. By means of a Lyapunov-like analysis the new learning law for this DNN, guarantying both successful identification and passivation effects, is derived. Based on this adaptive DNN model, an adaptive feedback controller, serving for wide class of nonlinear systems with an a priori incomplete model description, is designed. Two typical examples illustrate the effectiveness of the suggested approach.
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Lindquist, Kristen A.; Adebayo, Morenikeji; Barrett, Lisa Feldman
2016-01-01
Negative stimuli do not only evoke fear or disgust, but can also evoke a state of ‘morbid fascination’ which is an urge to approach and explore a negative stimulus. In the present neuroimaging study, we applied an innovative method to investigate the neural systems involved in typical and atypical conceptualizations of negative images. Participants received false feedback labeling their mental experience as fear, disgust or morbid fascination. This manipulation was successful; participants judged the false feedback correct for 70% of the trials on average. The neuroimaging results demonstrated differential activity within regions in the ‘neural reference space for discrete emotion’ depending on the type of feedback. We found robust differences in the ventrolateral prefrontal cortex, the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex comparing morbid fascination to control feedback. More subtle differences in the dorsomedial prefrontal cortex and the lateral orbitofrontal cortex were also found between morbid fascination feedback and the other emotion feedback conditions. This study is the first to forward evidence about the neural representation of the experimentally unexplored state of morbid fascination. In line with a constructionist framework, our findings suggest that neural resources associated with the process of conceptualization contribute to the neural representation of this state. PMID:26180088
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Asymptotic stabilization of nonlinear systems using state feedback
International Nuclear Information System (INIS)
D'Attellis, Carlos
1990-01-01
This paper studies the design of state-feedback controllers for the stabilization of single-input single-output nonlinear systems x = f(x) + g(x)u, y = h(x). Two approaches for the stabilization problem are given; the asymptotic stability is achieved by means of: a) nonlinear state feedback: two nonlinear feedbacks are used; the first separates the system in a controllable linear part and in the zeros-dynamic part. The second feedback generates an asymptotically stable equilibrium on the manifold where this dynamics evolves; b) nonlinear dynamic feedback: conditions are established under which the system can follow the output of a completely controllable bilinear system which uses bounded controls. This fact enables the system to reach, using bounded controls too, a desired output value in finite time. As this value corresponds to a state that lays in the attraction basin of a stable equilibrium with the same output, the system evolves to that point. The two methods are illustrated by examples. (Author) [es
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Social Security Administration — The Policy Feedback System (PFS) is a web application developed by the Office of Disability Policy Management Information (ODPMI) team that gathers empirical data...
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Cummine, Jacqueline; Cribben, Ivor; Luu, Connie; Kim, Esther; Bahktiari, Reyhaneh; Georgiou, George; Boliek, Carol A
2016-05-01
The neural circuitry associated with language processing is complex and dynamic. Graphical models are useful for studying complex neural networks as this method provides information about unique connectivity between regions within the context of the entire network of interest. Here, the authors explored the neural networks during covert reading to determine the role of feedforward and feedback loops in covert speech production. Brain activity of skilled adult readers was assessed in real word and pseudoword reading tasks with functional MRI (fMRI). The authors provide evidence for activity coherence in the feedforward system (inferior frontal gyrus-supplementary motor area) during real word reading and in the feedback system (supramarginal gyrus-precentral gyrus) during pseudoword reading. Graphical models provided evidence of an extensive, highly connected, neural network when individuals read real words that relied on coordination of the feedforward system. In contrast, when individuals read pseudowords the authors found a limited/restricted network that relied on coordination of the feedback system. Together, these results underscore the importance of considering multiple pathways and articulatory loops during language tasks and provide evidence for a print-to-speech neural network. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
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Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...
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Generalized fast feedback system in the SLC
International Nuclear Information System (INIS)
Hendrickson, L.; Allison, S.; Gromme, T.; Himel, T.; Krauter, K.; Rouse, F.; Sass, R.; Shoaee, H.
1991-11-01
A generalized fast feedback system has been developed to stabilize beams at various locations in the SLC. The system is designed to perform measurements and change actuator settings to control beam states such as position, angle and energy on a pulse to pulse basis. The software design is based on the state space formalism of digital control theory. The system is database-driven, facilitating the addition of new loops without requiring additional software. A communications system, KISNet, provides fast communications links between microprocessors for feedback loops which involve multiple micros. Feedback loops have been installed in seventeen locations throughout the SLC and have proven to be invaluable in stabilizing the machine
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International Nuclear Information System (INIS)
Yan Zhenya; Yu Pei
2007-01-01
In this paper, we study chaos (lag) synchronization of a new LC chaotic system, which can exhibit not only a two-scroll attractor but also two double-scroll attractors for different parameter values, via three types of state feedback controls: (i) linear feedback control; (ii) adaptive feedback control; and (iii) a combination of linear feedback and adaptive feedback controls. As a consequence, ten families of new feedback control laws are designed to obtain global chaos lag synchronization for τ < 0 and global chaos synchronization for τ = 0 of the LC system. Numerical simulations are used to illustrate these theoretical results. Each family of these obtained feedback control laws, including two linear (adaptive) functions or one linear function and one adaptive function, is added to two equations of the LC system. This is simpler than the known synchronization controllers, which apply controllers to all equations of the LC system. Moreover, based on the obtained results of the LC system, we also derive the control laws for chaos (lag) synchronization of another new type of chaotic system
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Kortink, Elise D; Weeda, Wouter D; Crowley, Michael J; Gunther Moor, Bregtje; van der Molen, Melle J W
2018-06-01
Monitoring social threat is essential for maintaining healthy social relationships, and recent studies suggest a neural alarm system that governs our response to social rejection. Frontal-midline theta (4-8 Hz) oscillatory power might act as a neural correlate of this system by being sensitive to unexpected social rejection. Here, we examined whether frontal-midline theta is modulated by individual differences in personality constructs sensitive to social disconnection. In addition, we examined the sensitivity of feedback-related brain potentials (i.e., the feedback-related negativity and P3) to social feedback. Sixty-five undergraduate female participants (mean age = 19.69 years) participated in the Social Judgment Paradigm, a fictitious peer-evaluation task in which participants provided expectancies about being liked/disliked by peer strangers. Thereafter, they received feedback signaling social acceptance/rejection. A community structure analysis was employed to delineate personality profiles in our data. Results provided evidence of two subgroups: one group scored high on attachment-related anxiety and fear of negative evaluation, whereas the other group scored high on attachment-related avoidance and low on fear of negative evaluation. In both groups, unexpected rejection feedback yielded a significant increase in theta power. The feedback-related negativity was sensitive to unexpected feedback, regardless of valence, and was largest for unexpected rejection feedback. The feedback-related P3 was significantly enhanced in response to expected social acceptance feedback. Together, these findings confirm the sensitivity of frontal midline theta oscillations to the processing of social threat, and suggest that this alleged neural alarm system behaves similarly in individuals that differ in personality constructs relevant to social evaluation.
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Neurophysiological correlates of anhedonia in feedback processing
Mies, Gabry W.; Van den Berg, Ivo; Franken, Ingmar H. A.; Smits, Marion; Van der Molen, Maurits W.; Van der Veen, Frederik M.
2013-01-01
Disturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a key role have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study investigated the relations between the two core symptoms of depression, i.e., depressed mood and anhedonia, and the neural correlates of feedback processing using fMRI. The focus was on the different subdivisions of the anterior cingulate cortex (ACC). Undergraduates with varying levels of depressed mood and anhedonia performed a time-estimation task in which they received positive and negative feedback that was either valid or invalid (i.e., related vs. unrelated to actual performance). The rostral cingulate zone (RCZ), corresponding to the dorsal part of the ACC, was less active in response to feedback in more anhedonic individuals, after correcting for the influence of depressed mood, whereas the subgenual ACC was more active in these individuals. Task performance was not affected by anhedonia, however. No statistically significant effects were found for depressed mood above and beyond the effects of anhedonia. This study therefore implies that increasing levels of anhedonia involve changes in the neural circuitry underlying feedback processing. PMID:23532800
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Neural activations associated with feedback and retrieval success
Wiklund-Hörnqvist, Carola; Andersson, Micael; Jonsson, Bert; Nyberg, Lars
2017-11-01
There is substantial behavioral evidence for a phenomenon commonly called "the testing effect", i.e. superior memory performance after repeated testing compared to re-study of to-be-learned materials. However, considerably less is known about the underlying neuro-cognitive processes that are involved in the initial testing phase, and thus underlies the actual testing effect. Here, we investigated functional brain activity related to test-enhanced learning with feedback. Subjects learned foreign vocabulary across three consecutive tests with correct-answer feedback. Functional brain-activity responses were analyzed in relation to retrieval and feedback events, respectively. Results revealed up-regulated activity in fronto-striatal regions during the first successful retrieval, followed by a marked reduction in activity as a function of improved learning. Whereas feedback improved behavioral performance across consecutive tests, feedback had a negligable role after the first successful retrieval for functional brain-activity modulations. It is suggested that the beneficial effects of test-enhanced learning is regulated by feedback-induced updating of memory representations, mediated via the striatum, that might underlie the stabilization of memory commonly seen in behavioral studies of the testing effect.
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Nonlinear identification of process dynamics using neural networks
International Nuclear Information System (INIS)
Parlos, A.G.; Atiya, A.F.; Chong, K.T.
1992-01-01
In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios
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Directory of Open Access Journals (Sweden)
Wang Chao
2016-03-01
Full Text Available Due to the complexities existing in the electric load simulator, this article develops a high-performance nonlinear adaptive controller to improve the torque tracking performance of the electric load simulator, which mainly consists of an adaptive fuzzy self-recurrent wavelet neural network controller with variable structure (VSFSWC and a complementary controller. The VSFSWC is clearly and easily used for real-time systems and greatly improves the convergence rate and control precision. The complementary controller is designed to eliminate the effect of the approximation error between the proposed neural network controller and the ideal feedback controller without chattering phenomena. Moreover, adaptive learning laws are derived to guarantee the system stability in the sense of the Lyapunov theory. Finally, the hardware-in-the-loop simulations are carried out to verify the feasibility and effectiveness of the proposed algorithms in different working styles.
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Generalized fast feedback system in the SLC
International Nuclear Information System (INIS)
Hendrickson, L.; Allison, S.; Gromme, T.; Himel, T.; Krauter, K.; Rouse, F.; Sass, R.; Shoaee, H.
1992-01-01
A generalized fast feedback system has been developed to stabilize beams at various locations in the SLC. The system is designed to perform measurements and change actuator settings to control beam states such as position, angle and energy on a pulse to pulse basis. The software design is based on the state space formalism of digital control theory. The system is database-driven, facilitating the addition of new loops without requiring additional software. A communications system, KISNet, provides fast communications links between microprocessors for feedback loops which involve multiple micros. Feedback loops have been installed in seventeen locations throughout the SLC and have proven to be invaluable in stabilizing the machine. (author)
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Long, Lijun; Zhao, Jun
2017-07-01
In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.
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Novel Reduced-Feedback Wireless Communication Systems
Shaqfeh, Mohammad Obaidah
2011-11-20
Modern communication systems apply channel-aware adaptive transmission techniques and dynamic resource allocation in order to exploit the peak conditions of the fading wireless links and to enable significant performance gains. However, conveying the channel state information among the users’ mobile terminals into the access points of the network consumes a significant portion of the scarce air-link resources and depletes the battery resources of the mobile terminals rapidly. Despite its evident drawbacks, the channel information feedback cannot be eliminated in modern wireless networks because blind communication technologies cannot support the ever-increasing transmission rates and high quality of experience demands of current ubiquitous services. Developing new transmission technologies with reduced-feedback requirements is sought. Network operators will benefit from releasing the bandwidth resources reserved for the feedback communications and the clients will enjoy the extended battery life of their mobile devices. The main technical challenge is to preserve the prospected transmission rates over the network despite decreasing the channel information feedback significantly. This is a noteworthy research theme especially that there is no mature theory for feedback communication in the existing literature despite the growing number of publications about the topic in the last few years. More research efforts are needed to characterize the trade-off between the achievable rate and the required channel information and to design new reduced-feedback schemes that can be flexibly controlled based on the operator preferences. Such schemes can be then introduced into the standardization bodies for consideration in next generation broadband systems. We have recently contributed to this field and published several journal and conference papers. We are the pioneers to propose a novel reduced-feedback opportunistic scheduling scheme that combines many desired features
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Artificial Neural Network Analysis System
2001-02-27
Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis
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Developing 360 degree feedback system for KINS
Energy Technology Data Exchange (ETDEWEB)
Han, In Soo; Cheon, B. M.; Kim, T. H.; Ryu, J. H. [Chungman National Univ., Daejeon (Korea, Republic of)
2003-12-15
This project aims to investigate the feasibility of a 360 degree feedback systems for KINS and to design guiding rules and structures in implementing that systems. Literature survey, environmental analysis and questionnaire survey were made to ensure that 360 degree feedback is the right tool to improve performance in KINS. That review leads to conclusion that more readiness and careful feasibility review are needed before implementation of 360 degree feedback in KINS. Further the project suggests some guiding rules that can be helpful for successful implementation of that system in KINS. Those include : start with development, experiment with one department, tie it to a clear organization's goal, train everyone involve, make sure to try that system in an atmosphere of trust.
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Developing 360 degree feedback system for KINS
International Nuclear Information System (INIS)
Han, In Soo; Cheon, B. M.; Kim, T. H.; Ryu, J. H.
2003-12-01
This project aims to investigate the feasibility of a 360 degree feedback systems for KINS and to design guiding rules and structures in implementing that systems. Literature survey, environmental analysis and questionnaire survey were made to ensure that 360 degree feedback is the right tool to improve performance in KINS. That review leads to conclusion that more readiness and careful feasibility review are needed before implementation of 360 degree feedback in KINS. Further the project suggests some guiding rules that can be helpful for successful implementation of that system in KINS. Those include : start with development, experiment with one department, tie it to a clear organization's goal, train everyone involve, make sure to try that system in an atmosphere of trust
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Directory of Open Access Journals (Sweden)
J. Humberto Pérez-Cruz
2014-01-01
Full Text Available The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed.
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Normal form and synchronization of strict-feedback chaotic systems
International Nuclear Information System (INIS)
Wang, Feng; Chen, Shihua; Yu Minghai; Wang Changping
2004-01-01
This study concerns the normal form and synchronization of strict-feedback chaotic systems. We prove that, any strict-feedback chaotic system can be rendered into a normal form with a invertible transform and then a design procedure to synchronize the normal form of a non-autonomous strict-feedback chaotic system is presented. This approach needs only a scalar driving signal to realize synchronization no matter how many dimensions the chaotic system contains. Furthermore, the Roessler chaotic system is taken as a concrete example to illustrate the procedure of designing without transforming a strict-feedback chaotic system into its normal form. Numerical simulations are also provided to show the effectiveness and feasibility of the developed methods
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Time-optimal feedback control for linear systems
International Nuclear Information System (INIS)
Mirica, S.
1976-01-01
The paper deals with the results of qualitative investigations of the time-optimal feedback control for linear systems with constant coefficients. In the first section, after some definitions and notations, two examples are given and it is shown that even the time-optimal control problem for linear systems with constant coefficients which looked like ''completely solved'' requires a further qualitative investigation of the stability to ''permanent perturbations'' of optimal feedback control. In the second section some basic results of the linear time-optimal control problem are reviewed. The third section deals with the definition of Boltyanskii's ''regular synthesis'' and its connection to Filippov's theory of right-hand side discontinuous differential equations. In the fourth section a theorem is proved concerning the stability to perturbations of time-optimal feedback control for linear systems with scalar control. In the last two sections it is proved that, if the matrix which defines the system has only real eigenvalues or is three-dimensional, the time-optimal feedback control defines a regular synthesis and therefore is stable to perturbations. (author)
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Dysfunctional feedback processing in adolescent males with conduct disorder.
Gao, Yidian; Chen, Haiyan; Jia, Huiqiao; Ming, Qingsen; Yi, Jinyao; Yao, Shuqiao
2016-01-01
Abnormalities in neural feedback-processing systems may play a role in the development of dysfunctional behavior in individuals diagnosed with conduct disorder (CD). The present study investigated the relation between CD adolescents and feedback processing by measuring event-related potentials (ERPs) in a single outcome gambling task, which included reward valence (loss and gain) and reward magnitude (10 and 50cents) as outcomes. N2 and P3 components have been established as effective indicators in studies of behavioral disinhibition, reward processing, and decision-making. Eighteen adolescent males (age: 13-17years) diagnosed with CD and 19 healthy age-matched male controls were recruited. Compared to healthy controls, CD individuals exhibited reduced N2 amplitudes in response to loss condition. There was also a significant decreased P3 amplitude in all conditions. The amplitudes of P3 were negatively correlated with impulsivity scores across both groups, and the amplitudes of N2 were positively correlated with impulsivity scores across both groups. Our findings suggest that adolescents with CD may be impaired in neural sensitivity feedback and the processing of environmental cues compared to healthy controls. Moreover, N2 and P3 may be reliable indices to detect different sensitivity in reward and punishment feedback processing. Copyright © 2015 Elsevier B.V. All rights reserved.
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Interaction in Spoken Word Recognition Models: Feedback Helps
Magnuson, James S.; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D.
2018-01-01
Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis. PMID:29666593
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Interaction in Spoken Word Recognition Models: Feedback Helps.
Magnuson, James S; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D
2018-01-01
Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.
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Interaction in Spoken Word Recognition Models: Feedback Helps
Directory of Open Access Journals (Sweden)
James S. Magnuson
2018-04-01
Full Text Available Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.
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Intelligent neural network diagnostic system
International Nuclear Information System (INIS)
Mohamed, A.H.
2010-01-01
Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.
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Using sampled-data feedback control and linear feedback synchronization in a new hyperchaotic system
International Nuclear Information System (INIS)
Zhao Junchan; Lu Junan
2008-01-01
This paper investigates control and synchronization of a new hyperchaotic system which was proposed by [Chen A, Lu J-A, Lue J, Yu S. Generating hyperchaotic Lue attractor via state feedback control. Physica A 2006;364:103-10]. Firstly, we give different sampled-data feedback control schemes with the variation of system parameter d. Specifically, we only use one controller to drive the system to the origin when d element of (-0.35, 0), and use two controllers if d element of [0, 1.3]. Next, we combine PC method with linear feedback approach to realize synchronization, and derive similar conclusions with varying d. Numerical simulations are also given to validate the proposed approaches
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Instabilities simulations with wideband feedback systems: CMAD, HEADTAIL, WARP
International Nuclear Information System (INIS)
Li, Kevin; Cesaratto, J; Fox, J D; Pivi, M; Rivetta, C; Rumolo, G
2013-01-01
Transverse mode coupling (TMCI) and electron cloud instabilities (ECI) pose fundamental limitations on the acceptable beam intensities in the SPS at CERN. This in turn limits the ultimate achievable luminosity in the LHC. Therefore, future luminosity upgrades foresee methods for evading TMCI as well as ECI. Proposed approaches within the LHC Injector Upgrade (LIU) project include new optics with reduced transition energy as well as vacuum chamber coating techniques. As a complementary option, high bandwidth feedback systems may provide instability mitigation by actively damping the intra-bunch motion of unstable modes. In an effort to evaluate the potentials and limitations of such feedback systems and to characterise some of the specifications, a numerical model of a realistic feedback system has been developed and integrated into available instabilities simulation codes. Together with the implementation of this new feedback system model, CMAD and HEADTAIL have been used to investigate the impact of different wideband feedback systems on ECI in the SPS. In this paper, we present some details on the numerical model of the realistic feedback system and its implementation as well as the results obtained from the simulation study using this model together with the instability codes. (author)
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Directory of Open Access Journals (Sweden)
Poramate eManoonpong
2013-02-01
Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
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PEP-II RF feedback system simulation
Energy Technology Data Exchange (ETDEWEB)
Tighe, R [Stanford Linear Accelerator Center, Menlo Park, CA (United States)
1996-08-01
A model containing the fundamental impedance of the PEP-II cavity along with the longitudinal beam dynamics and RF feedback system components is in use. It is prepared in a format allowing time-domain as well as frequency-domain analysis and full graphics capability. Matlab and Simulink are control system design and analysis programs (widely available) with many built-in tools. The model allows the use of compiled C-code modules for compute intensive portions. We desire to represent as nearly as possible the components of the feedback system including all delays, sample rates and applicable nonlinearities. (author)
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Control of beam halo-chaos using neural network self-adaptation method
International Nuclear Information System (INIS)
Fang Jinqing; Huang Guoxian; Luo Xiaoshu
2004-11-01
Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)
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Quasi-period oscillations of relay feedback systems
International Nuclear Information System (INIS)
Wen Guilin; Wang Qingguo; Lee, T.H.
2007-01-01
This paper presents an analytical method for investigation of the existence and stability of quasi-period oscillations (torus solutions) for a class of relay feedback systems. The idea is to analyze Poincare map from one switching surface to the next based on the Hopf bifurcation theory of maps. It is shown that there exist quasi-period oscillations in certain relay feedback systems
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Brain activity elicited by positive and negative feedback in preschool-aged children.
Directory of Open Access Journals (Sweden)
Xiaoqin Mai
2011-04-01
Full Text Available To investigate the processing of positive vs. negative feedback in children aged 4-5 years, we devised a prize-guessing game that is analogous to gambling tasks used to measure feedback-related brain responses in adult studies. Unlike adult studies, the feedback-related negativity (FRN elicited by positive feedback was as large as that elicited by negative feedback, suggesting that the neural system underlying the FRN may not process feedback valence in early childhood. In addition, positive feedback, compared with negative feedback, evoked a larger P1 over the occipital scalp area and a larger positive slow wave (PSW over the right central-parietal scalp area. We believe that the PSW is related to emotional arousal and the intensive focus on positive feedback that is present in the preschool and early school years has adaptive significance for both cognitive and emotional development during this period.
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Direct output feedback control of discrete-time systems
International Nuclear Information System (INIS)
Lin, C.C.; Chung, L.L.; Lu, K.H.
1993-01-01
An optimal direct output feedback control algorithm is developed for discrete-time systems with the consideration of time delay in control force action. Optimal constant output feedback gains are obtained through variational process such that certain prescribed quadratic performance index is minimized. Discrete-time control forces are then calculated from the multiplication of output measurements by these pre-calculated feedback gains. According to the proposed algorithm, structural system is assured to remain stable even in the presence of time delay. The number of sensors and controllers may be very small as compared with the dimension of states. Numerical results show that direct velocity feedback control is more sensitive to time delay than state feedback but, is still quite effective in reducing the dynamic responses under earthquake excitation. (author)
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Novel Reduced-Feedback Wireless Communication Systems
Shaqfeh, Mohammad Obaidah; Alnuweiri, Hussein; Alouini, Mohamed-Slim
2011-01-01
We have recently contributed to this field and published several journal and conference papers. We are the pioneers to propose a novel reduced-feedback opportunistic scheduling scheme that combines many desired features including fairness in resources distribution across the active terminals and distributed processing at the MAC layer level. In addition our scheme operates close to the upper capacity limits of achievable transmission rates over wireless links. We have also proposed another hybrid scheme that enables adjusting the feedback load flexibly based on rates requirements. We are currently investigating other novel ideas to design reduced-feedback communication systems.
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Feedforward Nonlinear Control Using Neural Gas Network
Directory of Open Access Journals (Sweden)
Iván Machón-González
2017-01-01
Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.
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The neural system of metacognition accompanying decision-making in the prefrontal cortex
Qiu, Lirong; Su, Jie; Ni, Yinmei; Bai, Yang; Zhang, Xuesong; Li, Xiaoli
2018-01-01
Decision-making is usually accompanied by metacognition, through which a decision maker monitors uncertainty regarding a decision and may then consequently revise the decision. These metacognitive processes can occur prior to or in the absence of feedback. However, the neural mechanisms of metacognition remain controversial. One theory proposes an independent neural system for metacognition in the prefrontal cortex (PFC); the other, that metacognitive processes coincide and overlap with the systems used for the decision-making process per se. In this study, we devised a novel “decision–redecision” paradigm to investigate the neural metacognitive processes involved in redecision as compared to the initial decision-making process. The participants underwent a perceptual decision-making task and a rule-based decision-making task during functional magnetic resonance imaging (fMRI). We found that the anterior PFC, including the dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were more extensively activated after the initial decision. The dACC activity in redecision positively scaled with decision uncertainty and correlated with individual metacognitive uncertainty monitoring abilities—commonly occurring in both tasks—indicating that the dACC was specifically involved in decision uncertainty monitoring. In contrast, the lFPC activity seen in redecision processing was scaled with decision uncertainty reduction and correlated with individual accuracy changes—positively in the rule-based decision-making task and negatively in the perceptual decision-making task. Our results show that the lFPC was specifically involved in metacognitive control of decision adjustment and was subject to different control demands of the tasks. Therefore, our findings support that a separate neural system in the PFC is essentially involved in metacognition and further, that functions of the PFC in metacognition are dissociable. PMID:29684004
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Energy Technology Data Exchange (ETDEWEB)
Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao, E-mail: htyu@tju.edu.cn [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Lu, Meili [School of Informational Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China); Che, Yanqiu [School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China)
2015-01-15
This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.
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Wei, Xile; Zhang, Danhong; Lu, Meili; Wang, Jiang; Yu, Haitao; Che, Yanqiu
2015-01-01
This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.
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International Nuclear Information System (INIS)
Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao; Lu, Meili; Che, Yanqiu
2015-01-01
This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance
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Neural activation during imitation with or without performance feedback: An fMRI study.
Zhang, Kaihua; Wang, Hui; Dong, Guangheng; Wang, Mengxing; Zhang, Jilei; Zhang, Hui; Meng, Weixia; Du, Xiaoxia
2016-08-26
In our daily lives, we often receive performance feedback (PF) during imitative learning, and we adjust our behaviors accordingly to improve performance. However, little is known regarding the neural mechanisms underlying this learning process. We hypothesized that appropriate PF would enhance neural activation or recruit additional brain areas during subsequent action imitation. Pictures of 20 different finger gestures without any social meaning were shown to participants from the first-person perspective. Imitation with or without PF was investigated by functional magnetic resonance imaging in 30 healthy subjects. The PF was given by a real person or by a computer. PF from a real person induced hyperactivation of the parietal lobe (precuneus and cuneus), cingulate cortex (posterior and anterior), temporal lobe (superior and transverse temporal gyri), and cerebellum (posterior and anterior lobes) during subsequent imitation. The positive PF and negative PF from a real person, induced the activation of more brain areas during the following imitation. The hyperactivation of the cerebellum, posterior cingulate cortex, precuneus, and cuneus suggests that the subjects exhibited enhanced motor control and visual attention during imitation after PF. Additionally, random PF from a computer had a small effect on the next imitation. We suggest that positive and accurate PF may be helpful for imitation learning. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Longitudinal feedback system for PEP
International Nuclear Information System (INIS)
Allen, M.A.; Cornacchia, M.; Millich, A.
1979-02-01
Whether the wide bandwidth longitudinal feedback system described in this paper is made to act on the individual modes in frequency domain or on the individual bunches in time domain, it represents a clean and efficient way of damping the longitudinal oscillations without influencing other beam parameters such as bunch shape or synchrotron frequency distribution. The frequency domain feedback presents the advantage of providing information on which modes are unstable and on their risetimes, which may be helpful in locating dangerous resonators in the ring
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Effects of voice harmonic complexity on ERP responses to pitch-shifted auditory feedback.
Behroozmand, Roozbeh; Korzyukov, Oleg; Larson, Charles R
2011-12-01
The present study investigated the neural mechanisms of voice pitch control for different levels of harmonic complexity in the auditory feedback. Event-related potentials (ERPs) were recorded in response to+200 cents pitch perturbations in the auditory feedback of self-produced natural human vocalizations, complex and pure tone stimuli during active vocalization and passive listening conditions. During active vocal production, ERP amplitudes were largest in response to pitch shifts in the natural voice, moderately large for non-voice complex stimuli and smallest for the pure tones. However, during passive listening, neural responses were equally large for pitch shifts in voice and non-voice complex stimuli but still larger than that for pure tones. These findings suggest that pitch change detection is facilitated for spectrally rich sounds such as natural human voice and non-voice complex stimuli compared with pure tones. Vocalization-induced increase in neural responses for voice feedback suggests that sensory processing of naturally-produced complex sounds such as human voice is enhanced by means of motor-driven mechanisms (e.g. efference copies) during vocal production. This enhancement may enable the audio-vocal system to more effectively detect and correct for vocal errors in the feedback of natural human vocalizations to maintain an intended vocal output for speaking. Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.
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A neural network based artificial vision system for licence plate recognition.
Draghici, S
1997-02-01
This paper presents a neural network based artificial vision system able to analyze the image of a car given by a camera, locate the registration plate and recognize the registration number of the car. The paper describes in detail various practical problems encountered in implementing this particular application and the solutions used to solve them. The main features of the system presented are: controlled stability-plasticity behavior, controlled reliability threshold, both off-line and on-line learning, self assessment of the output reliability and high reliability based on high level multiple feedback. The system has been designed using a modular approach. Sub-modules can be upgraded and/or substituted independently, thus making the system potentially suitable in a large variety of vision applications. The OCR engine was designed as an interchangeable plug-in module. This allows the user to choose an OCR engine which is suited to the particular application and to upgrade it easily in the future. At present, there are several versions of this OCR engine. One of them is based on a fully connected feedforward artificial neural network with sigmoidal activation functions. This network can be trained with various training algorithms such as error backpropagation. An alternative OCR engine is based on the constraint based decomposition (CBD) training architecture. The system has showed the following performances (on average) on real-world data: successful plate location and segmentation about 99%, successful character recognition about 98% and successful recognition of complete registration plates about 80%.
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Designing new feedback mangement system Långvik
Dang, Hien
2014-01-01
This thesis is a design project on the field of feedback management, conducted for Långvik hotel. The purpose of this thesis is to recommend a new feedback management system for the hotel that can effectively recognize original reasons for customers’ returning decision. The new approach to feedback management is expected to generate a higher number of re- turned customers subsequent to the summer business peak. The literature review focuses on the connection between customer experience and...
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Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system.
Blana, Dimitra; Kirsch, Robert F; Chadwick, Edward K
2009-05-01
A functional electrical stimulation controller is presented that uses a combination of feedforward and feedback for arm control in high-level injury. The feedforward controller generates the muscle activations nominally required for desired movements, and the feedback controller corrects for errors caused by muscle fatigue and external disturbances. The feedforward controller is an artificial neural network (ANN) which approximates the inverse dynamics of the arm. The feedback loop includes a PID controller in series with a second ANN representing the nonlinear properties and biomechanical interactions of muscles and joints. The controller was designed and tested using a two-joint musculoskeletal model of the arm that includes four mono-articular and two bi-articular muscles. Its performance during goal-oriented movements of varying amplitudes and durations showed a tracking error of less than 4 degrees in ideal conditions, and less than 10 degrees even in the case of considerable fatigue and external disturbances.
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Neural Control of the Immune System
Sundman, Eva; Olofsson, Peder S.
2014-01-01
Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…
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Ding, Xiaoshuai; Cao, Jinde; Zhao, Xuan; Alsaadi, Fuad E
2017-08-01
This paper is concerned with the drive-response synchronization for a class of fractional-order bidirectional associative memory neural networks with time delays, as well as in the presence of discontinuous activation functions. The global existence of solution under the framework of Filippov for such networks is firstly obtained based on the fixed-point theorem for condensing map. Then the state feedback and impulsive controllers are, respectively, designed to ensure the Mittag-Leffler synchronization of these neural networks and two new synchronization criteria are obtained, which are expressed in terms of a fractional comparison principle and Razumikhin techniques. Numerical simulations are presented to validate the proposed methodologies.
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Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling
Directory of Open Access Journals (Sweden)
Ferris Daniel P
2010-12-01
Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance
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Delayed feedback control of fractional-order chaotic systems
International Nuclear Information System (INIS)
Gjurchinovski, A; Urumov, V; Sandev, T
2010-01-01
We study the possibility to stabilize unstable steady states and unstable periodic orbits in chaotic fractional-order dynamical systems by the time-delayed feedback method. By performing a linear stability analysis, we establish the parameter ranges for successful stabilization of unstable equilibria in the plane parameterized by the feedback gain and the time delay. An insight into the control mechanism is gained by analyzing the characteristic equation of the controlled system, showing that the control scheme fails to control unstable equilibria having an odd number of positive real eigenvalues. We demonstrate that the method can also stabilize unstable periodic orbits for a suitable choice of the feedback gain, providing that the time delay is chosen to coincide with the period of the target orbit. In addition, it is shown numerically that delayed feedback control with a sinusoidally modulated time delay significantly enlarges the stability region of steady states in comparison to the classical time-delayed feedback scheme with a constant delay.
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Corresponding Angle Feedback in an innovative weighted transportation system
International Nuclear Information System (INIS)
Dong Chuanfei; Ma Xu
2010-01-01
The optimal information feedback has a significant effect on many socioeconomic systems like stock market and traffic systems aiming to make full use of resources. In this Letter, we study dynamics of traffic flow with real-time information. The influence of a feedback strategy named Corresponding Angle Feedback Strategy (CAFS) is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow and simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux.
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Ideal and conventional feedback systems for RWM suppression
International Nuclear Information System (INIS)
Pustovitov, V.D.
2002-01-01
Feedback suppression of resistive wall modes (RWM) is studied analytically using a model based on a standard cylindrical approximation. Two feedback systems are compared: 'ideal', creating only the field necessary for RMW suppression, and 'conventional', like that used in the DIII-D tokamak and considered as a candidate for ITER. The widespread opinion that the feedback with poloidal sensors is better than that with radial sensors is discussed. It is shown that the 'conventional' feedback with radial sensors can be effective only in a limited range, while using the input signal from internal poloidal sensors allows easy fulfilment of the stability criterion. This is a property of the 'conventional' feedback, but the 'ideal' feedback would stabilise RWM in both cases. (author)
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Augmenting Environmental Interaction in Audio Feedback Systems
Directory of Open Access Journals (Sweden)
Seunghun Kim
2016-04-01
Full Text Available Audio feedback is defined as a positive feedback of acoustic signals where an audio input and output form a loop, and may be utilized artistically. This article presents new context-based controls over audio feedback, leading to the generation of desired sonic behaviors by enriching the influence of existing acoustic information such as room response and ambient noise. This ecological approach to audio feedback emphasizes mutual sonic interaction between signal processing and the acoustic environment. Mappings from analyses of the received signal to signal-processing parameters are designed to emphasize this specificity as an aesthetic goal. Our feedback system presents four types of mappings: approximate analyses of room reverberation to tempo-scale characteristics, ambient noise to amplitude and two different approximations of resonances to timbre. These mappings are validated computationally and evaluated experimentally in different acoustic conditions.
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Predictor feedback for delay systems implementations and approximations
Karafyllis, Iasson
2017-01-01
This monograph bridges the gap between the nonlinear predictor as a concept and as a practical tool, presenting a complete theory of the application of predictor feedback to time-invariant, uncertain systems with constant input delays and/or measurement delays. It supplies several methods for generating the necessary real-time solutions to the systems’ nonlinear differential equations, which the authors refer to as approximate predictors. Predictor feedback for linear time-invariant (LTI) systems is presented in Part I to provide a solid foundation on the necessary concepts, as LTI systems pose fewer technical difficulties than nonlinear systems. Part II extends all of the concepts to nonlinear time-invariant systems. Finally, Part III explores extensions of predictor feedback to systems described by integral delay equations and to discrete-time systems. The book’s core is the design of control and observer algorithms with which global stabilization, guaranteed in the previous literature with idealized (b...
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Kobza, Stefan; Ferrea, Stefano; Schnitzler, Alfons; Pollok, Bettina; Südmeyer, Martin; Bellebaum, Christian
2012-01-01
Feedback to both actively performed and observed behaviour allows adaptation of future actions. Positive feedback leads to increased activity of dopamine neurons in the substantia nigra, whereas dopamine neuron activity is decreased following negative feedback. Dopamine level reduction in unmedicated Parkinson's Disease patients has been shown to lead to a negative learning bias, i.e. enhanced learning from negative feedback. Recent findings suggest that the neural mechanisms of active and observational learning from feedback might differ, with the striatum playing a less prominent role in observational learning. Therefore, it was hypothesized that unmedicated Parkinson's Disease patients would show a negative learning bias only in active but not in observational learning. In a between-group design, 19 Parkinson's Disease patients and 40 healthy controls engaged in either an active or an observational probabilistic feedback-learning task. For both tasks, transfer phases aimed to assess the bias to learn better from positive or negative feedback. As expected, actively learning patients showed a negative learning bias, whereas controls learned better from positive feedback. In contrast, no difference between patients and controls emerged for observational learning, with both groups showing better learning from positive feedback. These findings add to neural models of reinforcement-learning by suggesting that dopamine-modulated input to the striatum plays a minor role in observational learning from feedback. Future research will have to elucidate the specific neural underpinnings of observational learning.
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Feedbacks between conservation and social-ecological systems
Miller, Brian W.; Caplow, Susan C.; Leslie, Paul W.
2012-01-01
Robust ways to meet objectives of environmental conservation and social and economic development remain elusive. This struggle may in part be related to insufficient understanding of the feedbacks between conservation initiatives and social-ecological systems, specifically, the ways in which conservation initiatives result in social changes that have secondary effects on the environments targeted by conservation. To explore this idea we sampled peer-reviewed articles addressing the social and environmental dimensions of conservation and coded each paper according to its research focus and characterization of these feedbacks. The majority of articles in our sample focused either on the effect of conservation initiatives on people (e.g., relocation, employment) or the effect of people on the environment (e.g., fragmentation, conservation efficacy of traditional management systems). Few studies in our sample empirically addressed both the social dynamics resulting from conservation initiatives and subsequent environmental effects. In many cases, one was measured and the other was discussed anecdotally. Among the studies that describe feedbacks between social and environmental variables, there was more evidence of positive (amplifying) feedbacks between social and environmental outcomes (i.e., undesirable social outcomes yielded undesirable environmental effects, and desirable social outcomes yielded desirable environmental effects). The major themes within the sampled literature include conflict between humans and wild animals, social movements, adaptive comanagement, loss of traditional management systems, traditional ecological knowledge, human displacement and risks to livelihoods, and conservation and development. The narratives associated with each theme can serve as hypotheses for facilitating further discussion about conservation issues and for catalyzing future studies of the feedbacks between conservation and social-ecological systems. PMID:22443128
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Beam position feedback system for the advanced photon source
International Nuclear Information System (INIS)
Chung, Y.
1994-01-01
The Advanced Photon Source (APS) will implement both global and local beam position feedback systems to stabilize the particle and x-ray beams for the storage ring. The systems consist of 20 VME crates distributed around the ring, each running multiple digital signal processors (DSP) running at 4 kHz sampling rate with a proportional, integral, and derivative (PID) control algorithm. The particle and x-ray beam position data is shared by the distributed processors through networked reflective memory. A theory of closed orbit correction using the technique of singular value decomposition (SVD) of the response matrix and simulation of its application to the APS storage ring will be discussed. This technique combines the global and local feedback systems and resolves the conflict among multiple local feedback systems due to local bump closure error. Maximum correction efficiency is achieved by feeding back to the global orbit data to the local feedback systems. The effect of the eddy current induced in the relatively thick (1/2 in.) vacuum chamber by the ac corrector magnet field for local feedback systems is compensated by digital filters. Results of experiments conducted on the x-ray ring of the National Synchrotron Light Source and the SPEAR at Stanford Synchrotron Radiation Laboratory will also be presented
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Beam position feedback system for the advanced photon source
International Nuclear Information System (INIS)
Chung, Y.
1994-01-01
The Advanced Photon Source (APS) will implement both global and local beam position feedback systems to stabilize the particle and X-ray beams for the storage ring. The systems consist of 20 VME crates distributed around the ring, each running multiple digital signal processors (DSP) running at 4 kHz sampling rate with a proportional, integral, and derivative (PID) control algorithm. The particle and X-ray beam position data is shared by the distributed processors through networked reflective memory. A theory of closed orbit correction using the technique of singular value decomposition (SVD) of the response matrix and simulation of its application to the APS storage ring will be discussed. This technique combines the global and local feedback systems and resolves the conflict among multiple local feedback systems due to local bump closure error. Maximum correction efficiency is achieved by feeding back the global orbit data to the local feedback systems. The effect of the vacuum chamber eddy current induced by the AC corrector magnet field for local feedback systems is compensated by digital filters. Results of experiments conducted on the X-ray ring of the National Synchrotron Light Source and the SPEAR at Stanford Synchrotron Radiation Laboratory will be presented. copyright 1994 American Institute of Physics
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Beam position feedback system for the Advanced Photon Source
International Nuclear Information System (INIS)
Chung, Y.
1993-01-01
The Advanced Photon Source (APS) will implement both global and local beam position feedback systems to stabilize the particle and X-ray beams for the storage ring. The systems consist of 20 VME crates distributed around the ring, each running multiple digital signal processors (DSP) running at 4kHz sampling rate with a proportional, integral, and derivative (PID) control algorithm. The particle and X-ray beam position data is shared by the distributed processors through networked reflective memory. A theory of closed orbit correction using the technique of singular value decomposition (SVD) of the response matrix and simulation of its application to the APS storage ring will be discussed. This technique combines the global and local feedback systems and resolves the conflict among multiple local feedback systems due to local bump closure error. Maximum correction efficiency is achieved by feeding back the global orbit data to the local feedback systems. The effect of the vacuum chamber eddy current induced by the AC corrector magnet field for local feedback systems is compensated by digital filters. Results of experiments conducted on the X-ray ring of the National Synchrotron Light Source and the SPEAR at Stanford Synchrotron Radiation Laboratory will be presented
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Beam position feedback system for the Advanced Photon Source
International Nuclear Information System (INIS)
Chung, Y.
1993-01-01
The Advanced Photon Source (APS) will implement both global and local beam position feedback systems to stabilize the particle and X-ray beams for the storage ring. The systems consist of 20 VME crates distributed around the ring, each running multiple digital signal processors (DSP) running at 4kHz sampling rate with a proportional, integral, and derivative (PID) control algorithm. The particle and X-ray beam position data is shared by the distributed processors through networked reflective memory. A theory of closed orbit correction using the technique of singular value decomposition (SVD) of the response matrix and simulation of its application to the APS storage ring will be discussed. This technique combines the global and local feedback systems and resolves the conflict among multiple local feedback systems due to local bump closure error. Maximum correction efficiency is achieved by feeding back the global orbit data to the local feedback systems. The effect of the eddy current induced in the relatively thick (1/2 inch) vacuum chamber by the AC corrector magnet field for local feedback systems is compensated by digital filters. Results of experiments conducted on the X-ray ring of the National Synchrotron Light Source and the SPEAR at Stanford Synchrotron Radiation Laboratory will also be presented
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Ideomotor feedback control in a recurrent neural network.
Galtier, Mathieu
2015-06-01
The architecture of a neural network controlling an unknown environment is presented. It is based on a randomly connected recurrent neural network from which both perception and action are simultaneously read and fed back. There are two concurrent learning rules implementing a sort of ideomotor control: (i) perception is learned along the principle that the network should predict reliably its incoming stimuli; (ii) action is learned along the principle that the prediction of the network should match a target time series. The coherent behavior of the neural network in its environment is a consequence of the interaction between the two principles. Numerical simulations show a promising performance of the approach, which can be turned into a local and better "biologically plausible" algorithm.
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DEFF Research Database (Denmark)
Zheng, Zane Z.; Vicente-Grabovetsky, Alejandro; MacDonald, Ewen N.
2013-01-01
The everyday act of speaking involves the complex processes of speech motor control. An important component of control is monitoring, detection, and processing of errors when auditory feedback does not correspond to the intended motor gesture. Here we show, using fMRI and converging operations...... within a multivoxel pattern analysis framework, that this sensorimotor process is supported by functionally differentiated brain networks. During scanning, a real-time speech-tracking system was used to deliver two acoustically different types of distorted auditory feedback or unaltered feedback while...... human participants were vocalizing monosyllabic words, and to present the same auditory stimuli while participants were passively listening. Whole-brain analysis of neural-pattern similarity revealed three functional networks that were differentially sensitive to distorted auditory feedback during...
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Ross, Muriel D.
1991-01-01
The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.
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Neural neworks in a management information systems
Directory of Open Access Journals (Sweden)
Jana Weinlichová
2009-01-01
Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.
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Ideal and conventional feedback systems for RWM suppression
Energy Technology Data Exchange (ETDEWEB)
Pustovitov, V.D.
2002-01-01
Feedback suppression of resistive wall modes (RWM) is studied analytically using a model based on a standard cylindrical approximation. Two feedback systems are compared: 'ideal', creating only the field necessary for RMW suppression, and 'conventional', like that used in the DIII-D tokamak and considered as a candidate for ITER. The widespread opinion that the feedback with poloidal sensors is better than that with radial sensors is discussed. It is shown that the 'conventional' feedback with radial sensors can be effective only in a limited range, while using the input signal from internal poloidal sensors allows easy fulfilment of the stability criterion. This is a property of the 'conventional' feedback, but the 'ideal' feedback would stabilise RWM in both cases. (author)
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Wang, Cong; Hill, David J
2006-01-01
One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.
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Theoretical treatment of transverse feedback systems with memory
International Nuclear Information System (INIS)
Cornacchia, M.; Wang, J.M.
1981-01-01
The differential equation of the dipole moment of coherent oscillations in the presence of a feedback system is derived. The analysis, which starts in the time domain, is extended to the frequency domain; this allows a straightforward derivation of the damping rate for both coasting and bunched beams. The damping rate is expressed in terms of the transfer function of the feedback system and in a general form which takes into account the β-function and betatron phase modulation along the beam trajectory, the effect of memory arising from the finiteness of the system bandwidth, the effect of the time delay and of the betatron phase advance between detector and kicker. Some examples of the dependence of the damping rate on the feedback parameters are given
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Hammer, Rubi; Tennekoon, Michael; Cooke, Gillian E; Gayda, Jessica; Stein, Mark A; Booth, James R
2015-08-01
We tested the interactive effect of feedback and reward on visuospatial working memory in children with ADHD. Seventeen boys with ADHD and 17 Normal Control (NC) boys underwent functional magnetic resonance imaging (fMRI) while performing four visuospatial 2-back tasks that required monitoring the spatial location of letters presented on a display. Tasks varied in reward size (large; small) and feedback availability (no-feedback; feedback). While the performance of NC boys was high in all conditions, boys with ADHD exhibited higher performance (similar to those of NC boys) only when they received feedback associated with large-reward. Performance pattern in both groups was mirrored by neural activity in an executive function neural network comprised of few distinct frontal brain regions. Specifically, neural activity in the left and right middle frontal gyri of boys with ADHD became normal-like only when feedback was available, mainly when feedback was associated with large-reward. When feedback was associated with small-reward, or when large-reward was expected but feedback was not available, boys with ADHD exhibited altered neural activity in the medial orbitofrontal cortex and anterior insula. This suggests that contextual support normalizes activity in executive brain regions in children with ADHD, which results in improved working memory. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.
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Collaborative Recurrent Neural Networks forDynamic Recommender Systems
2016-11-22
JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population
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Instabilities simulations with wideband feedback systems: CMAD, HEADTAIL, WARP
Li, Kevin; Fox, J D; Pivi, M; Rivetta, C; Rumolo, G
2013-01-01
Transverse mode coupling (TMCI) and electron cloud instabilities (ECI) pose fundamental limitations on the acceptable beam intensities in the SPS at CERN. This in turn limits the ultimate achievable luminosity in the LHC. Therefore, future luminosity upgrades foresee methods for evading TMCI as well as ECI. Proposed approaches within the LHC Injector Upgrade (LIU) project include new optics with reduced transition energy as well as vacuum chamber coating techniques. As a complementary option, high bandwidth feedback systems may provide instability mitigation by actively damping the intra-bunch motion of unstable modes. In an effort to evaluate the potentials and limitations of such feedback systems and to characterise some of the specifications, a numerical model of a realistic feedback system has been developed and integrated into available instabilities simulation codes. Together with the implementation of this new feedback system model, CMAD and HEADTAIL have been used to investigate the impact of differen...
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Weighted congestion coefficient feedback in intelligent transportation systems
International Nuclear Information System (INIS)
Dong Chuanfei; Ma Xu; Wang Binghong
2010-01-01
In traffic systems, a reasonable information feedback can improve road capacity. In this Letter, we study dynamics of traffic flow with real-time information. And the influence of a feedback strategy named Weighted Congestion Coefficient Feedback Strategy (WCCFS) is introduced, based on a two-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow and simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux.
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Spiking Neural P Systems with Communication on Request.
Pan, Linqiang; Păun, Gheorghe; Zhang, Gexiang; Neri, Ferrante
2017-12-01
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.
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Operation of the transverse feedback system at the CERN SPS
International Nuclear Information System (INIS)
Bossart, R.; Louwerse, R.; Mourier, J.; Vos, L.
1987-01-01
To prevent transverse instabilities at high beam intensity in the SPS, the transverse feedback system for damping the betatron oscillations has been upgraded for larger damping decrements and for increased system's bandwidth. The feedback loop now contains a digital delay line cancellor, so that the damper works with a velocity feedback Δx/Δt, unaffected by the closed orbit position x at the pick-up station. The digital processing of the feedback signal facilitates nonlinear feedback techniques such as antidamping and ''band-bang'' feedback. The ''bang-bang'' feedback provides the maximum possible damping rate of the injection oscillations in the SPS-collider, in order to minimize the emittance increase caused by filamentation. The antidamping nonlinearity provides small continuous beam oscillations of 50 μm amplitude for tracking the machine tune Q with a phase locked loop
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Neural Network with Local Memory for Nuclear Reactor Power Level Control
International Nuclear Information System (INIS)
Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri
2001-01-01
A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions
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Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng
2013-02-01
This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.
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Genetic learning in rule-based and neural systems
Smith, Robert E.
1993-01-01
The design of neural networks and fuzzy systems can involve complex, nonlinear, and ill-conditioned optimization problems. Often, traditional optimization schemes are inadequate or inapplicable for such tasks. Genetic Algorithms (GA's) are a class of optimization procedures whose mechanics are based on those of natural genetics. Mathematical arguments show how GAs bring substantial computational leverage to search problems, without requiring the mathematical characteristics often necessary for traditional optimization schemes (e.g., modality, continuity, availability of derivative information, etc.). GA's have proven effective in a variety of search tasks that arise in neural networks and fuzzy systems. This presentation begins by introducing the mechanism and theoretical underpinnings of GA's. GA's are then related to a class of rule-based machine learning systems called learning classifier systems (LCS's). An LCS implements a low-level production-system that uses a GA as its primary rule discovery mechanism. This presentation illustrates how, despite its rule-based framework, an LCS can be thought of as a competitive neural network. Neural network simulator code for an LCS is presented. In this context, the GA is doing more than optimizing and objective function. It is searching for an ecology of hidden nodes with limited connectivity. The GA attempts to evolve this ecology such that effective neural network performance results. The GA is particularly well adapted to this task, given its naturally-inspired basis. The LCS/neural network analogy extends itself to other, more traditional neural networks. Conclusions to the presentation discuss the implications of using GA's in ecological search problems that arise in neural and fuzzy systems.
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Event-Triggered Output-Feedback Control for Disturbed Linear Systems
Directory of Open Access Journals (Sweden)
Hao Jiang
2018-01-01
Full Text Available In the last few decades, event-triggered control received considerable attention, because of advantages in reducing the resource utilization, such as communication load and processor. In this paper, we propose an event-triggered output-feedback controller for disturbed linear systems, in order to achieve both better resource utilization and disturbance attenuation properties at the same time. Based on our prior work on state-feedback H∞ control for disturbed systems, we propose an approach to design an output-feedback H∞ controller for the system whose states are not completely observable, and a sufficient condition guaranteeing the asymptotic stability and robustness of the system is given in the form of LMIs (Linear Matrix Inequalities.
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Synchronizing strict-feedback and general strict-feedback chaotic systems via a single controller
International Nuclear Information System (INIS)
Chen Shihua; Wang Feng; Wang Changping
2004-01-01
We present a systematic design procedure to synchronize a class of chaotic systems in a so-called strict-feedback form based on back-stepping procedure. This approach needs only a single controller to realize synchronization no matter how many dimensions the chaotic system contains. Furthermore, we point out that the method does not work for general strict-feedback chaotic systems, for instance, Lorenz system. Therefore, we propose three kinds of synchronization schemes for Lorenz system using the Lyapunov function method. All the three schemes avoid including divergence factor as in Ref. [Chaos, Solitons and Fractals 16 (2003) 37]. Especially in the last two schemes, we need only one state variable in controller, which has important significance in chaos synchronization used for communication purposes. Finally numerical simulations are provided to show the effectiveness and feasibility of the developed methods
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Feedback Control of a Class of Nonholonomic Hamiltonian Systems
DEFF Research Database (Denmark)
Sørensen, Mathias Jesper
Feedback control of nonholonomic systems has always been problematic due to the nonholonomic constraints that limit the space of possible system velocities. This property is very basic, and Brockett proved that a nonholonomic system cannot be asymptotically stabilized by a time-invariant smooth...... turns out to be useful when stabilizing the nonholonomic system. If the system is properly actuated it is possible to asymptotically stabilize the primary part of the configuration coordinates via a passive energy shaping and damping injecting feedback. The feedback is smooth and time......-invariant, but since it does not asymptotically stabilize the secondary part of the configuration coordinates, it does not violate Brockett’s obstruction. The results fromthe general class of nonholonomicHamiltonian systems with kinematic inputs are applied to a real implementation of a four wheel steered, four wheel...
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Effects of intrinsic motivation on feedback processing during learning.
DePasque, Samantha; Tricomi, Elizabeth
2015-10-01
Learning commonly requires feedback about the consequences of one's actions, which can drive learners to modify their behavior. Motivation may determine how sensitive an individual might be to such feedback, particularly in educational contexts where some students value academic achievement more than others. Thus, motivation for a task might influence the value placed on performance feedback and how effectively it is used to improve learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional magnetic resonance imaging (fMRI) during feedback-based learning before and after a novel manipulation based on motivational interviewing, a technique for enhancing treatment motivation in mental health settings. Because of its role in the reinforcement learning system, the striatum is situated to play a significant role in the modulation of learning based on motivation. Consistent with this idea, motivation levels during the task were associated with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation following a brief motivational interview was associated with increases in feedback sensitivity in the left medial temporal lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and furthermore that changes in motivation facilitate processing in areas that support learning and memory. Copyright © 2015. Published by Elsevier Inc.
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Effects of Intrinsic Motivation on Feedback Processing During Learning
DePasque, Samantha; Tricomi, Elizabeth
2015-01-01
Learning commonly requires feedback about the consequences of one’s actions, which can drive learners to modify their behavior. Motivation may determine how sensitive an individual might be to such feedback, particularly in educational contexts where some students value academic achievement more than others. Thus, motivation for a task might influence the value placed on performance feedback and how effectively it is used to improve learning. To investigate the interplay between intrinsic motivation and feedback processing, we used functional magnetic resonance imaging (fMRI) during feedback-based learning before and after a novel manipulation based on motivational interviewing, a technique for enhancing treatment motivation in mental health settings. Because of its role in the reinforcement learning system, the striatum is situated to play a significant role in the modulation of learning based on motivation. Consistent with this idea, motivation levels during the task were associated with sensitivity to positive versus negative feedback in the striatum. Additionally, heightened motivation following a brief motivational interview was associated with increases in feedback sensitivity in the left medial temporal lobe. Our results suggest that motivation modulates neural responses to performance-related feedback, and furthermore that changes in motivation facilitates processing in areas that support learning and memory. PMID:26112370
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Feedback authoring possibilities in web-based learning systems
Vasilyeva, E.; De Bra, P.M.E.; Pechenizkiy, M.; Bonk, C.J.; et al., xx
2008-01-01
This paper surveys and analyses the feedback authoring possibilities in online assessment modules of the most popular Learning Management Systems (LMS) including Moodle, Sakai, and Blackboard. We consider the problem of authoring and support of tailored and personalized feedback and demonstrate how
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Nonlinear Time Series Prediction Using Chaotic Neural Networks
Li, Ke-Ping; Chen, Tian-Lun
2001-06-01
A nonlinear feedback term is introduced into the evaluation equation of weights of the backpropagation algorithm for neural network, the network becomes a chaotic one. For the purpose of that we can investigate how the different feedback terms affect the process of learning and forecasting, we use the model to forecast the nonlinear time series which is produced by Makey-Glass equation. By selecting the suitable feedback term, the system can escape from the local minima and converge to the global minimum or its approximate solutions, and the forecasting results are better than those of backpropagation algorithm. The project supported by National Basic Research Project "Nonlinear Science" and National Natural Science Foundation of China under Grant No. 60074020
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Error-dependent modulation of speech-induced auditory suppression for pitch-shifted voice feedback
Directory of Open Access Journals (Sweden)
Larson Charles R
2011-06-01
Full Text Available Abstract Background The motor-driven predictions about expected sensory feedback (efference copies have been proposed to play an important role in recognition of sensory consequences of self-produced motor actions. In the auditory system, this effect was suggested to result in suppression of sensory neural responses to self-produced voices that are predicted by the efference copies during vocal production in comparison with passive listening to the playback of the identical self-vocalizations. In the present study, event-related potentials (ERPs were recorded in response to upward pitch shift stimuli (PSS with five different magnitudes (0, +50, +100, +200 and +400 cents at voice onset during active vocal production and passive listening to the playback. Results Results indicated that the suppression of the N1 component during vocal production was largest for unaltered voice feedback (PSS: 0 cents, became smaller as the magnitude of PSS increased to 200 cents, and was almost completely eliminated in response to 400 cents stimuli. Conclusions Findings of the present study suggest that the brain utilizes the motor predictions (efference copies to determine the source of incoming stimuli and maximally suppresses the auditory responses to unaltered feedback of self-vocalizations. The reduction of suppression for 50, 100 and 200 cents and its elimination for 400 cents pitch-shifted voice auditory feedback support the idea that motor-driven suppression of voice feedback leads to distinctly different sensory neural processing of self vs. non-self vocalizations. This characteristic may enable the audio-vocal system to more effectively detect and correct for unexpected errors in the feedback of self-produced voice pitch compared with externally-generated sounds.
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Error-dependent modulation of speech-induced auditory suppression for pitch-shifted voice feedback.
Behroozmand, Roozbeh; Larson, Charles R
2011-06-06
The motor-driven predictions about expected sensory feedback (efference copies) have been proposed to play an important role in recognition of sensory consequences of self-produced motor actions. In the auditory system, this effect was suggested to result in suppression of sensory neural responses to self-produced voices that are predicted by the efference copies during vocal production in comparison with passive listening to the playback of the identical self-vocalizations. In the present study, event-related potentials (ERPs) were recorded in response to upward pitch shift stimuli (PSS) with five different magnitudes (0, +50, +100, +200 and +400 cents) at voice onset during active vocal production and passive listening to the playback. Results indicated that the suppression of the N1 component during vocal production was largest for unaltered voice feedback (PSS: 0 cents), became smaller as the magnitude of PSS increased to 200 cents, and was almost completely eliminated in response to 400 cents stimuli. Findings of the present study suggest that the brain utilizes the motor predictions (efference copies) to determine the source of incoming stimuli and maximally suppresses the auditory responses to unaltered feedback of self-vocalizations. The reduction of suppression for 50, 100 and 200 cents and its elimination for 400 cents pitch-shifted voice auditory feedback support the idea that motor-driven suppression of voice feedback leads to distinctly different sensory neural processing of self vs. non-self vocalizations. This characteristic may enable the audio-vocal system to more effectively detect and correct for unexpected errors in the feedback of self-produced voice pitch compared with externally-generated sounds.
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The fast correction coil feedback control system
International Nuclear Information System (INIS)
Coffield, F.; Caporaso, G.; Zentler, J.M.
1989-01-01
A model-based feedback control system has been developed to correct beam displacement errors in the Advanced Test Accelerator (ATA) electron beam accelerator. The feedback control system drives an X/Y dipole steering system that has a 40-MHz bandwidth and can produce ±300-Gauss-cm dipole fields. A simulator was used to develop the control algorithm and to quantify the expected performance in the presence of beam position measurement noise and accelerator timing jitter. The major problem to date has been protecting the amplifiers from the voltage that is inductively coupled to the steering bars by the beam. 3 refs., 8 figs
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Neural network for adapting nuclear power plant control for wide-range operation
International Nuclear Information System (INIS)
Ku, C.C.; Lee, K.Y.; Edwards, R.M.
1991-01-01
A new concept of using neural networks has been evaluated for optimal control of a nuclear reactor. The neural network uses the architecture of a standard backpropagation network; however, a new dynamic learning algorithm has been developed to capture the underlying system dynamics. The learning algorithm is based on parameter estimation for dynamic systems. The approach is demonstrated on an optimal reactor temperature controller by adjusting the feedback gains for wide-range operation. Application of optimal control to a reactor has been considered for improving temperature response using a robust fifth-order reactor power controller. Conventional gain scheduling can be employed to extend the range of good performance to accommodate large changes in power where nonlinear characteristics significantly modify the dynamics of the power plant. Gain scheduling is developed based on expected parameter variations, and it may be advantageous to further adapt feedback gains on-line to better match actual plant performance. A neural network approach is used here to adapt the gains to better accommodate plant uncertainties and thereby achieve improved robustness characteristics
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Construction of experience feedback system for equipment supervision in nuclear engineering
International Nuclear Information System (INIS)
Zou Pingguo; Zhang Liying; Zhang Wenzhong
2009-01-01
Based on the analysis of the experience sources on equipment supervision in nuclear engineering, the details of the organization principle, working flow, and report requirement for the experience feedback system are introduced. The function range and its roll in the experience feedback system of the nuclear authority, nuclear power plant owners and equipment supervision organizations are illustrated. The standardization working requirements in the information gathering, analyzing, feedback and tracking process, and the characteristics and form of the incident report and feedback report are proposed. It emphasizes that the method for combined analysis of one significant incident and the whole incidents shall be adopted in the information analysis, and the experience feedback shall be considered in the development of equipment supervision technique and the equipment manufacturing, thus to maximize the use of experience feedback information to improve the pertinency and effectiveness of the experience feedback system. (authors)
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Design of output feedback controller for a unified chaotic system
International Nuclear Information System (INIS)
Li Wenlin; Chen Xiuqin; Shen Zhiping
2008-01-01
In this paper, the synchronization of a unified chaotic system is investigated by the use of output feedback controllers; a two-input single-output feedback controller and single-input single-output feedback controller are presented to synchronize the unified chaotic system when the states are not all measurable. Compared with the existing results, the controllers designed in this paper have some advantages such as small feedback gain, simple structure and less conservation. Finally, numerical simulations results are provided to demonstrate the validity and effectiveness of the proposed method
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Development of bunch by bunch transverse feedback system at Hefei light source
International Nuclear Information System (INIS)
Wang Junhua; Zheng Kai; Li Weimin; Yang Yongliang; Huang Longjun; Chen Yuanbo; Zhou Zeran; Wang Lin; Liu Zeping; Sun Baogen; Ma Li; Cao Jianshe; Yue Junhui; Liu Dekang; Ye Kairong
2008-01-01
This paper has introduced the development of the transverse bunch-by-bunch measurement and feedback system, including the experiment of damping the coupled bunch instability. Some key technologies on the system have been introduced: the vector calculation module as a signal processing module used to adjust the phase of the feedback signals, the feedback kicker cavity and the notch filter used to filter the DC component and revolution frequencies component in a signal and save the feedback power. The result of the feedback experiment is mentioned: the instability oscillation was damped when the feedback system was on. (authors)
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Modeling and simulation of Indus-2 RF feedback control system
International Nuclear Information System (INIS)
Sharma, D.; Bagduwal, P.S.; Tiwari, N.; Lad, M.; Hannurkar, P.R.
2012-01-01
Indus-2 synchrotron radiation source has four RF stations along with their feedback control systems. For higher beam energy and current operation amplitude and phase feedback control systems of Indus-2 are being upgraded. To understand the behaviour of amplitude and phase control loop under different operating conditions, modelling and simulation of RF feedback control system is done. RF cavity baseband I/Q model has been created due to its close correspondence with actual implementation and better computational efficiency which makes the simulation faster. Correspondence between cavity baseband and RF model is confirmed by comparing their simulation results. Low Level RF (LLRF) feedback control system simulation is done using the same cavity baseband I/Q model. Error signals are intentionally generated and response of the closed loop system is observed. Simulation will help us in optimizing parameters of upgraded LLRF system for higher beam energy and current operation. (author)
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The Endogenous Feedback Network
DEFF Research Database (Denmark)
Augustenborg, Claudia Carrara
2010-01-01
proposals, it will first be considered the extents of their reciprocal compatibility, tentatively shaping an integrated, theoretical profile of consciousness. A new theory, the Endogenous Feedback Network (EFN) will consequently be introduced which, beside being able to accommodate the main tenets...... of the reviewed theories, appears able to compensate for the explanatory gaps they leave behind. The EFN proposes consciousness as the phenomenon emerging from a distinct network of neural paths broadcasting the neural changes associated to any mental process. It additionally argues for the need to include a 5th...
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Coherent versus Measurement Feedback: Linear Systems Theory for Quantum Information
Directory of Open Access Journals (Sweden)
Naoki Yamamoto
2014-11-01
Full Text Available To control a quantum system via feedback, we generally have two options in choosing a control scheme. One is the coherent feedback, which feeds the output field of the system, through a fully quantum device, back to manipulate the system without involving any measurement process. The other one is measurement-based feedback, which measures the output field and performs a real-time manipulation on the system based on the measurement results. Both schemes have advantages and disadvantages, depending on the system and the control goal; hence, their comparison in several situations is important. This paper considers a general open linear quantum system with the following specific control goals: backaction evasion, generation of a quantum nondemolished variable, and generation of a decoherence-free subsystem, all of which have important roles in quantum information science. Some no-go theorems are proven, clarifying that those goals cannot be achieved by any measurement-based feedback control. On the other hand, it is shown that, for each control goal there exists a coherent feedback controller accomplishing the task. The key idea to obtain all the results is system theoretic characterizations of the above three notions in terms of controllability and observability properties or transfer functions of linear systems, which are consistent with their standard definitions.
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Dynamics of nonlinear feedback control.
Snippe, H P; van Hateren, J H
2007-05-01
Feedback control in neural systems is ubiquitous. Here we study the mathematics of nonlinear feedback control. We compare models in which the input is multiplied by a dynamic gain (multiplicative control) with models in which the input is divided by a dynamic attenuation (divisive control). The gain signal (resp. the attenuation signal) is obtained through a concatenation of an instantaneous nonlinearity and a linear low-pass filter operating on the output of the feedback loop. For input steps, the dynamics of gain and attenuation can be very different, depending on the mathematical form of the nonlinearity and the ordering of the nonlinearity and the filtering in the feedback loop. Further, the dynamics of feedback control can be strongly asymmetrical for increment versus decrement steps of the input. Nevertheless, for each of the models studied, the nonlinearity in the feedback loop can be chosen such that immediately after an input step, the dynamics of feedback control is symmetric with respect to increments versus decrements. Finally, we study the dynamics of the output of the control loops and find conditions under which overshoots and undershoots of the output relative to the steady-state output occur when the models are stimulated with low-pass filtered steps. For small steps at the input, overshoots and undershoots of the output do not occur when the filtering in the control path is faster than the low-pass filtering at the input. For large steps at the input, however, results depend on the model, and for some of the models, multiple overshoots and undershoots can occur even with a fast control path.
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Diamond Light Source Booster fast orbit feedback system
International Nuclear Information System (INIS)
Gayadeen, S.; Duncan, S.R.; Christou, C.; Heron, M.T.; Rowland, J.
2012-01-01
The Fast Orbit Feedback system that has been installed on the Diamond Light Source Storage ring has been replicated on the Booster synchrotron in order to provide a test bed for the development of the Storage Ring controller design. To realise this the Booster is operated in DC mode. The electron beam is regulated in two planes using the Fast Orbit Feedback system, which takes the beam position from 22 beam position monitors for each plane, and calculates offsets to 44 corrector power supplies at a sample rate of 10 kHz. This paper describes the design and realization of the controller for the Booster Fast Orbit Feedback, presents results from the implementation and considers future development
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SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.
Zenke, Friedemann; Ganguli, Surya
2018-04-13
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.
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Learning receptive fields using predictive feedback.
Jehee, Janneke F M; Rothkopf, Constantin; Beck, Jeffrey M; Ballard, Dana H
2006-01-01
Previously, it was suggested that feedback connections from higher- to lower-level areas carry predictions of lower-level neural activities, whereas feedforward connections carry the residual error between the predictions and the actual lower-level activities [Rao, R.P.N., Ballard, D.H., 1999. Nature Neuroscience 2, 79-87.]. A computational model implementing the hypothesis learned simple cell receptive fields when exposed to natural images. Here, we use predictive feedback to explain tuning properties in medial superior temporal area (MST). We implement the hypothesis using a new, biologically plausible, algorithm based on matching pursuit, which retains all the features of the previous implementation, including its ability to efficiently encode input. When presented with natural images, the model developed receptive field properties as found in primary visual cortex. In addition, when exposed to visual motion input resulting from movements through space, the model learned receptive field properties resembling those in MST. These results corroborate the idea that predictive feedback is a general principle used by the visual system to efficiently encode natural input.
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Nonclassical state generation for linear quantum systems via nonlinear feedback control
International Nuclear Information System (INIS)
Ohki, Kentaro; Tsumura, Koji; Takeuchi, Reiji
2017-01-01
In this paper, we propose a measurement nonlinear feedback control scheme to generate Wigner-function negativity in an optical cavity having dynamics described as a linear quantum system. In general, linear optical quantum systems can be easily constructed with reliable devices; therefore, the idea of constructing the entire system with such an optical system and nonlinear feedback is reasonable for generating Wigner-function negativity. However, existing studies have insufficiently examined the realizability or actual implementation of feedback control, which essentially requires fast responses from the sensors and actuators. In order to solve this problem, we consider the realizable feedback control of the optical phase of a pumping beam supplied to a cavity by using electro-optical modulation, which can be utilized as a fast control actuator. Then, we introduce mathematical models of the feedback-controlled system and evaluate its effect on the generation of the Wigner-function negativity by using numerical simulation. Through various numerical simulations, we show that the proposed feedback control can effectively generate the negativity of the Wigner function. (paper)
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PLS beam position measurement and feedback system
International Nuclear Information System (INIS)
Huang, J.Y.; Lee, J.; Park, M.K.; Kim, J.H.; Won, S.C.
1992-01-01
A real-time orbit correction system is proposed for the stabilization of beam orbit and photon beam positions in Pohang Light Source. PLS beam position monitoring system is designed to be VMEbus compatible to fit the real-time digital orbit feedback system. A VMEbus based subsystem control computer, Mil-1553B communication network and 12 BPM/PS machine interface units constitute digital part of the feedback system. With the super-stable PLS correction magnet power supply, power line frequency noise is almost filtered out and the dominant spectra of beam obtit fluctuations are expected to appear below 15 Hz. Using DSP board in SCC for the computation and using an appropriate compensation circuit for the phase delay by the vacuum chamber, PLS real-time orbit correction system is realizable without changing the basic structure of PLS computer control system. (author)
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Coordinated three-dimensional motion of the head and torso by dynamic neural networks.
Kim, J; Hemami, H
1998-01-01
The problem of trajectory tracking control of a three dimensional (3D) model of the human upper torso and head is considered. The torso and the head are modeled as two rigid bodies connected at one point, and the Newton-Euler method is used to derive the nonlinear differential equations that govern the motion of the system. The two-link system is driven by six pairs of muscle like actuators that possess physiologically inspired alpha like and gamma like inputs, and spindle like and Golgi tendon organ like outputs. These outputs are utilized as reflex feedback for stability and stiffness control, in a long loop feedback for the purpose of estimating the state of the system (somesthesis), and as part of the input to the controller. Ideal delays of different duration are included in the feedforward and feedback paths of the system to emulate such delays encountered in physiological systems. Dynamical neural networks are trained to learn effective control of the desired maneuvers of the system. The feasibility of the controller is demonstrated by computer simulation of the successful execution of the desired maneuvers. This work demonstrates the capabilities of neural circuits in controlling highly nonlinear systems with multidelays in their feedforward and feedback paths. The ultimate long range goal of this research is toward understanding the working of the central nervous system in controlling movement. It is an interdisciplinary effort relying on mechanics, biomechanics, neuroscience, system theory, physiology and anatomy, and its short range relevance to rehabilitation must be noted.
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Neural principles of memory and a neural theory of analogical insight
Lawson, David I.; Lawson, Anton E.
1993-12-01
Grossberg's principles of neural modeling are reviewed and extended to provide a neural level theory to explain how analogies greatly increase the rate of learning and can, in fact, make learning and retention possible. In terms of memory, the key point is that the mind is able to recognize and recall when it is able to match sensory input from new objects, events, or situations with past memory records of similar objects, events, or situations. When a match occurs, an adaptive resonance is set up in which the synaptic strengths of neurons are increased; thus a long term record of the new input is formed in memory. Systems of neurons called outstars and instars are presumably the underlying units that enable this to occur. Analogies can greatly facilitate learning and retention because they activate the outstars (i.e., the cells that are sampling the to-be-learned pattern) and cause the neural activity to grow exponentially by forming feedback loops. This increased activity insures the boost in synaptic strengths of neurons, thus causing storage and retention in long-term memory (i.e., learning).
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Corticocortical feedback increases the spatial extent of normalization.
Nassi, Jonathan J; Gómez-Laberge, Camille; Kreiman, Gabriel; Born, Richard T
2014-01-01
Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a "normalization pool." Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing.
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Neural Correlates of Feedback Processing in Decision Making under Risk
Directory of Open Access Journals (Sweden)
Beate eSchuermann
2012-07-01
Full Text Available Introduction. Event-related brain potentials (ERP provide important information about the sensitivity of the brain to process varying risks. The aim of the present study was to determine how different risk levels are reflected in decision-related ERPs, namely the feedback-related negativity (FRN and the P300. Material and Methods. 20 participants conducted a probabilistic two-choice gambling task while an electroencephalogram was recorded. Choices were provided between a low-risk option yielding low rewards and low losses and a high-risk option yielding high rewards and high losses. While options differed in expected risks, they were equal in expected values and in feedback probabilities. Results. At the behavioral level, participants were generally risk-averse but modulated their risk-taking behavior according to reward history. An early positivity (P200 was enhanced on negative feedbacks in high-risk compared to low-risk options. With regard to the FRN, there were significant amplitude differences between positive and negative feedbacks in high-risk options, but not in low-risk options. While the FRN on negative feedbacks did not vary with decision riskiness, reduced amplitudes were found for positive feedbacks in high-risk relative to low-risk choices. P300 amplitudes were larger in high-risk decisions, and in an additive way, after negative compared to positive feedback. Discussion. The present study revealed significant influences of risk and valence processing on ERPs. FRN findings suggest that the reward prediction error signal is increased after high-risk decisions. The increased P200 on negative feedback in risky decisions suggests that large negative prediction errors are processed as early as in the P200 time range. The later P300 amplitude is sensitive to feedback valence as well as to the risk of a decision. Thus, the P300 carries additional information for reward processing, mainly the enhanced motivational significance of risky
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Sensory feedback in upper limb prosthetics.
Antfolk, Christian; D'Alonzo, Marco; Rosén, Birgitta; Lundborg, Göran; Sebelius, Fredrik; Cipriani, Christian
2013-01-01
One of the challenges facing prosthetic designers and engineers is to restore the missing sensory function inherit to hand amputation. Several different techniques can be employed to provide amputees with sensory feedback: sensory substitution methods where the recorded stimulus is not only transferred to the amputee, but also translated to a different modality (modality-matched feedback), which transfers the stimulus without translation and direct neural stimulation, which interacts directly with peripheral afferent nerves. This paper presents an overview of the principal works and devices employed to provide upper limb amputees with sensory feedback. The focus is on sensory substitution and modality matched feedback; the principal features, advantages and disadvantages of the different methods are presented.
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Operational status of the transverse multibunch feedback system at Diamond
International Nuclear Information System (INIS)
Uzun, I.; Abbott, M.; Heron, M.T.; Morgan, A.F.D.; Rehm, G.
2012-01-01
A transverse multibunch feedback (TMBF) system is in operation at Diamond Light Source to damp coupled-bunch instabilities up to 250 MHz in both the vertical and horizontal planes. It comprises an in-house designed and built analogue front end combined with a Libera Bunch-by-Bunch feedback processor and output stripline kickers. FPGA-based feedback electronics is used to implement several diagnostic features in addition to the basic feedback functionality. This paper reports on the current operational status of the TMBF system along with its characteristics. Also discussed are operational diagnostic functionalities including continuous measurement of the betatron tune and chromaticity. (authors)
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Energy Technology Data Exchange (ETDEWEB)
Tobiyama, M; Kikutani, E [National Lab. for High Energy Physics, Tsukuba, Ibaraki (Japan)
1996-08-01
Design and the present status of the bunch by bunch feedback systems for KEKB rings are shown. The detection of the bunch oscillation are made with the phase detection for longitudinal plane, the AM/PM method for transverse plane. Two GHz component of the bunch signal which is extracted with an analog FIR filter is used for the detection. Hardware two-tap FIR filter systems to shift the phase of the oscillation by 90deg will be used for the longitudinal signal processing. The same system will be used with no filtering but with only digital delay for transverse system. The candidate for the kicker and the required maximum power are also estimated. (author)
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Dynamics and control of a financial system with time-delayed feedbacks
International Nuclear Information System (INIS)
Chen, W.-C.
2008-01-01
Complex behaviors in a financial system with time-delayed feedbacks are discussed in this study via numerical modeling. The system shows complex dynamics such as periodic, quasi-periodic, and chaotic behaviors. Both period doubling and inverse period doubling routes were found in this system. This paper also shows that the attractor merging crisis is a fundamental feature of nonlinear financial systems with time-delayed feedbacks. Control of the deterministic chaos in the financial system can be realized using Pyragas feedbacks
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Diversity in School Performance Feedback Systems
Verhaeghe, Goedele; Schildkamp, Kim; Luyten, Hans; Valcke, Martin
2015-01-01
As data-based decision making is receiving increased attention in education, more and more school performance feedback systems (SPFSs) are being developed and used worldwide. These systems provide schools with data on their functioning. However, little research is available on the characteristics of the different SPFSs. Therefore, this study…
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Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems
Xia, Feng; Ma, Longhua; Peng, Chen; Sun, Youxian; Dong, Jinxiang
2008-01-01
There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN. PMID:27879934
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Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems
Directory of Open Access Journals (Sweden)
Jinxiang Dong
2008-07-01
Full Text Available There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting crosslayer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An eventdriven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.
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Directory of Open Access Journals (Sweden)
Miaolei Zhou
Full Text Available As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.
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Zhou, Miaolei; Zhang, Qi; Wang, Jingyuan
2014-01-01
As a new type of smart material, magnetic shape memory alloy has the advantages of a fast response frequency and outstanding strain capability in the field of microdrive and microposition actuators. The hysteresis nonlinearity in magnetic shape memory alloy actuators, however, limits system performance and further application. Here we propose a feedforward-feedback hybrid control method to improve control precision and mitigate the effects of the hysteresis nonlinearity of magnetic shape memory alloy actuators. First, hysteresis nonlinearity compensation for the magnetic shape memory alloy actuator is implemented by establishing a feedforward controller which is an inverse hysteresis model based on Krasnosel'skii-Pokrovskii operator. Secondly, the paper employs the classical Proportion Integration Differentiation feedback control with feedforward control to comprise the hybrid control system, and for further enhancing the adaptive performance of the system and improving the control accuracy, the Radial Basis Function neural network self-tuning Proportion Integration Differentiation feedback control replaces the classical Proportion Integration Differentiation feedback control. Utilizing self-learning ability of the Radial Basis Function neural network obtains Jacobian information of magnetic shape memory alloy actuator for the on-line adjustment of parameters in Proportion Integration Differentiation controller. Finally, simulation results show that the hybrid control method proposed in this paper can greatly improve the control precision of magnetic shape memory alloy actuator and the maximum tracking error is reduced from 1.1% in the open-loop system to 0.43% in the hybrid control system.
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Minimal-Inversion Feedforward-And-Feedback Control System
Seraji, Homayoun
1990-01-01
Recent developments in theory of control systems support concept of minimal-inversion feedforward-and feedback control system consisting of three independently designable control subsystems. Applicable to the control of linear, time-invariant plant.
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Bu, Xiangwei; Wu, Xiaoyan; Tian, Mingyan; Huang, Jiaqi; Zhang, Rui; Ma, Zhen
2015-09-01
In this paper, an adaptive neural controller is exploited for a constrained flexible air-breathing hypersonic vehicle (FAHV) based on high-order tracking differentiator (HTD). By utilizing functional decomposition methodology, the dynamic model is reasonably decomposed into the respective velocity subsystem and altitude subsystem. For the velocity subsystem, a dynamic inversion based neural controller is constructed. By introducing the HTD to adaptively estimate the newly defined states generated in the process of model transformation, a novel neural based altitude controller that is quite simpler than the ones derived from back-stepping is addressed based on the normal output-feedback form instead of the strict-feedback formulation. Based on minimal-learning parameter scheme, only two neural networks with two adaptive parameters are needed for neural approximation. Especially, a novel auxiliary system is explored to deal with the problem of control inputs constraints. Finally, simulation results are presented to test the effectiveness of the proposed control strategy in the presence of system uncertainties and actuators constraints. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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Adaptive Sliding Mode Control of MEMS Gyroscope Based on Neural Network Approximation
Directory of Open Access Journals (Sweden)
Yuzheng Yang
2014-01-01
Full Text Available An adaptive sliding controller using radial basis function (RBF network to approximate the unknown system dynamics microelectromechanical systems (MEMS gyroscope sensor is proposed. Neural controller is proposed to approximate the unknown system model and sliding controller is employed to eliminate the approximation error and attenuate the model uncertainties and external disturbances. Online neural network (NN weight tuning algorithms, including correction terms, are designed based on Lyapunov stability theory, which can guarantee bounded tracking errors as well as bounded NN weights. The tracking error bound can be made arbitrarily small by increasing a certain feedback gain. Numerical simulation for a MEMS angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive neural control scheme and demonstrate the satisfactory tracking performance and robustness.
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A method for calculating active feedback system to provide vertical
Indian Academy of Sciences (India)
The active feedback system is applied to control slow motions of plasma. The objective of the ... The other problem is connected with the control of plasma vertical position with active feedback system. Calculation of ... Current Issue Volume 90 ...
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Additive Feed Forward Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1999-01-01
This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...
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The efficacy of an automated feedback system for general practitioners
Bindels, Rianne; Hasman, Arie; Kester, Arnold D.; Talmon, Jan L.; de Clercq, Paul A.; Winkens, Ron A. G.
2003-01-01
OBJECTIVE: An automated feedback system that produces comments about the non-adherence of general practitioners (GPs) to accepted practice guidelines for ordering diagnostic tests was developed. Before implementing the automated feedback system in daily practice, we assessed the potential effect of
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Fast digital feedback control systems for accelerator RF system using FPGA
International Nuclear Information System (INIS)
Bagduwal, Pritam Singh; Sharma, Dheeraj; Tiwari, Nitesh; Lad, M.; Hannurkar, P.R.
2012-01-01
Feedback control system plays important role for proper injection and acceleration of beam in particle accelerators by providing the required amplitude and phase stability of RF fields in accelerating structures. Advancement in the field of digital technology enables us to develop fast digital feedback control system for RF applications. Digital Low Level RF (LLRF) system offers the inherent advantages of Digital System like flexibility, adaptability, good repeatability and reduced long time drift errors compared to analog system. To implement the feedback control algorithm, I/Q control scheme is used. By properly sampling the down converted IF signal using fast ADC we get accurate feedback signal and also eliminates the need of two separate detectors for amplitude and phase detection. Controller is implemented in Vertex-4 FPGA. Codes for control algorithms which controls the amplitude and phase in all four quadrants with good accuracy are written in the VHDL. I/Q modulator works as common actuator for both amplitude and phase correction. Synchronization between RF, LO and ADC clock is indispensable and has been achieved by deriving the clock and LO signal from RF signal itself. Control system has been successfully tested in lab with phase and amplitude stability better then ±1% and ±1° respectively. High frequency RF signal is down converted to IF using the super heterodyne technique. Super heterodyne principal not only brings the RF signal to the Low IF frequency at which it can be easily processed but also enables us to use the same hardware and software for other RF frequencies with some minor modification. (author)
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Real time global orbit feedback system for NSLS x-ray ring
International Nuclear Information System (INIS)
Yu, L.H.; Biscardi, R.; Bittner, J.; Fauchet, A.M.; Krinsky, F.S.; Nawrocky, R.J.; Rothman, J.; Singh, O.V.; Yang, K.M.
1991-01-01
We report on the design and commissioning of a real time harmonic global orbit feedback system for the NSLS X-ray ring. This system uses 8 pick-up electrode position monitors and 16 trim dipole magnets to eliminate 3 harmonic components of the orbit fluctuations. Because of the larger number of position monitors and trim magnets, the X-ray ring feedback system differs from the previously reported VUV ring system in that the Fourier analysis and harmonic generation networks are comprised of MDAC boards controlled by computer. The implementation of the global feedback system has resulted in a dramatic improvement of orbit stability, by more than a factor of five everywhere. Simultaneous operation of the global and several local bump feedback systems has been achieved. 4 refs., 5 figs
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Effect of vibrotactile feedback on an EMG-based proportional cursor control system.
Li, Shunchong; Chen, Xingyu; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang
2013-01-01
Surface electromyography (sEMG) has been introduced into the bio-mechatronics systems, however, most of them are lack of the sensory feedback. In this paper, the effect of vibrotactile feedback for a myoelectric cursor control system is investigated quantitatively. Simultaneous and proportional control signals are extracted from EMG using a muscle synergy model. Different types of feedback including vibrotactile feedback and visual feedback are added, assessed and compared with each other. The results show that vibrotactile feedback is capable of improving the performance of EMG-based human machine interface.
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Laser experimental system as teaching aid for demonstrating basic phenomena of laser feedback
International Nuclear Information System (INIS)
Xu, Ling; Zhao, Shijie; Zhang, Shulian
2015-01-01
An experimental laser teaching system is developed to demonstrate laser feedback phenomena, which bring great harm to optical communication and benefits to precision measurement. The system consists of an orthogonally polarized He-Ne laser, a feedback mirror which reflects the laser output light into the laser cavity, and an optical attenuator which changes the intensity of the feedback light. As the feedback mirror is driven by a piezoelectric ceramic, the attenuator is adjusted and the feedback mirror is tilted, the system can demonstrate many basic laser feedback phenomena, including weak, moderate and strong optical feedback, multiple feedback and polarization flipping. Demonstrations of these phenomena can give students a better understanding about the intensity and polarization of lasers. The system is well designed and assembled, simple to operate, and provides a valuable teaching aid at an undergraduate level. (paper)
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System and method for determining stability of a neural system
Curtis, Steven A. (Inventor)
2011-01-01
Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.
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Hamlet, C. L.; Hoffman, K.; Fauci, L.; Tytell, E.
2016-02-01
The lamprey is a model organism for both neurophysiology and locomotion studies. To study the role of sensory feedback as an organism moves through its environment, a 2D, integrative, multi-scale model of an anguilliform swimmer driven by neural activation from a central pattern generator (CPG) is constructed. The CPG in turn drives muscle kinematics and is fully coupled to the surrounding fluid. The system is numerically evolved in time using an immersed boundary framework producing an emergent swimming mode. Proprioceptive feedback to the CPG based on experimental observations adjust the activation signal as the organism interacts with its environment. Effects on the speed, stability and cost (metabolic work) of swimming due to nonlinear dependencies associated with muscle force development combined with proprioceptive feedback to neural activation are estimated and examined.
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PWR system simulation and parameter estimation with neural networks
Energy Technology Data Exchange (ETDEWEB)
Akkurt, Hatice; Colak, Uener E-mail: uc@nuke.hacettepe.edu.tr
2002-11-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within {+-}0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected.
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PWR system simulation and parameter estimation with neural networks
International Nuclear Information System (INIS)
Akkurt, Hatice; Colak, Uener
2002-01-01
A detailed nonlinear model for a typical PWR system has been considered for the development of simulation software. Each component in the system has been represented by appropriate differential equations. The SCILAB software was used for solving nonlinear equations to simulate steady-state and transient operational conditions. Overall system has been constructed by connecting individual components to each other. The validity of models for individual components and overall system has been verified. The system response against given transients have been analyzed. A neural network has been utilized to estimate system parameters during transients. Different transients have been imposed in training and prediction stages with neural networks. Reactor power and system reactivity during the transient event have been predicted by the neural network. Results show that neural networks estimations are in good agreement with the calculated response of the reactor system. The maximum errors are within ±0.254% for power and between -0.146 and 0.353% for reactivity prediction cases. Steam generator parameters, pressure and water level, are also successfully predicted by the neural network employed in this study. The noise imposed on the input parameters of the neural network deteriorates the power estimation capability whereas the reactivity estimation capability is not significantly affected
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Increasing Personal Value Congruence in Computerized Decision Support Using System Feedback
Directory of Open Access Journals (Sweden)
Bryan Hosack
2014-02-01
Full Text Available The Theory of Universals in Values (TUV, a reliable and validated conceptualization of personal values used in psychology, is used to examine the effect of system feedback delivered by a Decision Support System (DSS on personal values. The results indicate that value-based decision-making behavior can be influenced by DSS feedback to address value congruence in decision-making. User behavior was shown to follow the outcomes expected by operant theory when feedback was supportive and to follow the outcomes of reactance theory when feedback was challenging. This result suggests that practitioners and Information System (IS researchers should consider user values when designing computerized decision feedback to adjust a system’s design such that the potential user backlash is avoided or congruence between organizational and personal values is achieved.
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Integrated Neural Flight and Propulsion Control System
Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)
2001-01-01
This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.
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Digital closed orbit feedback system for the Advanced Photon Source storage ring
International Nuclear Information System (INIS)
Chung, Y.; Barr, D.; Decker, G.; Galayda, J.; Lenkszus, F.; Lumpkin, A.; Votaw, A.J.
1995-01-01
Closed orbit feedback for the Advanced Photon Source (APS) storage ring employs unified global an local feedback systems for stabilization of particle and photon beams based on digital signal processing (DSP). Hardware and software aspects of the system will be described. In particular, we will discuss global and local orbit feedback algorithms, PID (proportional, integral, and derivative) control algorithm. application of digital signal processing to compensate for vacuum chamber eddy current effects, resolution of the interaction between global and local systems through decoupling, self-correction of the local bump closure error, user interface through the APS control system, and system performance in the frequency and time domains. The system hardware, including the DSPS, is distributed in 20 VNE crates around the ring, and the entire feedback system runs synchronously at 4-kHz sampling frequency in order to achieve a correction bandwidth exceeding 100 Hz. The required data sharing between the global and local feedback systems is facilitated via the use of fiber-optically-networked reflective memories
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Real-time applications of neural nets
International Nuclear Information System (INIS)
Spencer, J.E.
1989-05-01
Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs
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Real-time applications of neural nets
International Nuclear Information System (INIS)
Spencer, J.E.
1989-01-01
Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas e.g. improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. In this paper, such issues are considered, examples given and possibilities discussed
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Real-time applications of neural nets
Energy Technology Data Exchange (ETDEWEB)
Spencer, J.E.
1989-05-01
Producing, accelerating and colliding very high power, low emittance beams for long periods is a formidable problem in real-time control. As energy has grown exponentially in time so has the complexity of the machines and their control systems. Similar growth rates have occurred in many areas, e.g., improved integrated circuits have been paid for with comparable increases in complexity. However, in this case, reliability, capability and cost have improved due to reduced size, high production and increased integration which allow various kinds of feedback. In contrast, most large complex systems (LCS) are perceived to lack such possibilities because only one copy is made. Neural nets, as a metaphor for LCS, suggest ways to circumvent such limitations. It is argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems. While complimentary to AI, they mesh nicely with characteristics desired for real-time systems. Such issues are considered, examples given and possibilities discussed. 21 refs., 6 figs.
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Directory of Open Access Journals (Sweden)
Wilfredo Blanco
2017-09-01
Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes
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Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary Systems
Ghallab, Ahmed G.
2017-10-19
Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true.
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Lyapunov-based Stability of Feedback Interconnections of Negative Imaginary Systems
Ghallab, Ahmed G.; Mabrok, Mohamed; Petersen, Ian R.
2017-01-01
Feedback control systems using sensors and actuators such as piezoelectric sensors and actuators, micro-electro-mechanical systems (MEMS) sensors and opto-mechanical sensors, are allowing new advances in designing such high precision technologies. The negative imaginary control systems framework allows for robust control design for such high precision systems in the face of uncertainties due to unmodelled dynamics. The stability of the feedback interconnection of negative imaginary systems has been well established in the literature. However, the proofs of stability feedback interconnection which are used in some previous papers have a shortcoming due to a matrix inevitability issue. In this paper, we provide a new and correct Lyapunov-based proof of one such result and show that the result is still true.
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Diagnostic Neural Network Systems for the Electronic Circuits
International Nuclear Information System (INIS)
Mohamed, A.H.
2014-01-01
Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases
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A beam position feedback system for beam lines at the photon factory
International Nuclear Information System (INIS)
Katsura, T.; Kamiya, Y.; Haga, K.; Mitsuhashi, T.
1987-01-01
The beam position of the synchrotron radiation produced from the Storage Ring was stabilized by a twofold position feedback system. A digital feedback system was developed to suppress the diurnal beam movement (one cycle of sin-like drifting motion per day) which became a serious problem in low-emittance operation. The feedback was applied to the closed-orbit-distortion (COD) correction system in order to cancel the position variation at all the beam lines proportionately to the variation monitored at one beam line. An analog feedback system is also used to suppress frequency components faster than the slow diurnal movement
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Corticocortical feedback increases the spatial extent of normalization
Nassi, Jonathan J.; Gómez-Laberge, Camille; Kreiman, Gabriel; Born, Richard T.
2014-01-01
Normalization has been proposed as a canonical computation operating across different brain regions, sensory modalities, and species. It provides a good phenomenological description of non-linear response properties in primary visual cortex (V1), including the contrast response function and surround suppression. Despite its widespread application throughout the visual system, the underlying neural mechanisms remain largely unknown. We recently observed that corticocortical feedback contributes to surround suppression in V1, raising the possibility that feedback acts through normalization. To test this idea, we characterized area summation and contrast response properties in V1 with and without feedback from V2 and V3 in alert macaques and applied a standard normalization model to the data. Area summation properties were well explained by a form of divisive normalization, which computes the ratio between a neuron's driving input and the spatially integrated activity of a “normalization pool.” Feedback inactivation reduced surround suppression by shrinking the spatial extent of the normalization pool. This effect was independent of the gain modulation thought to mediate the influence of contrast on area summation, which remained intact during feedback inactivation. Contrast sensitivity within the receptive field center was also unaffected by feedback inactivation, providing further evidence that feedback participates in normalization independent of the circuit mechanisms involved in modulating contrast gain and saturation. These results suggest that corticocortical feedback contributes to surround suppression by increasing the visuotopic extent of normalization and, via this mechanism, feedback can play a critical role in contextual information processing. PMID:24910596
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The Technology of Measurement Feedback Systems.
Bickman, Leonard; Kelley, Susan Douglas; Athay, Michele
2012-12-01
Usual care in the community is far from optimal. Sufficient evidence exists that dropout rates are significant, treatment is effective for only a small proportion of clients, and that the translation of evidence-based treatments to the real world is problematic. Technology has been shown to be helpful in health care in improving the effectiveness of treatment. A relatively new technology being used in mental health is measurement feedback systems (MFSs). MFSs are particularly applicable to couple and family psychology (CFP) because of its ability to provide information on the multiple perspectives involved in treatment. The Contextualized Feedback Systems tm (CFS®), developed at Vanderbilt University is used as an example of what can be accomplished with an MFS. The advantages and limitations of this technology are described as well as the anticipated reimbursement requirements that mental health services will need.
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Virtual grasping: closed-loop force control using electrotactile feedback.
Jorgovanovic, Nikola; Dosen, Strahinja; Djozic, Damir J; Krajoski, Goran; Farina, Dario
2014-01-01
Closing the control loop by providing somatosensory feedback to the user of a prosthesis is a well-known, long standing challenge in the field of prosthetics. Various approaches have been investigated for feedback restoration, ranging from direct neural stimulation to noninvasive sensory substitution methods. Although there are many studies presenting closed-loop systems, only a few of them objectively evaluated the closed-loop performance, mostly using vibrotactile stimulation. Importantly, the conclusions about the utility of the feedback were partly contradictory. The goal of the current study was to systematically investigate the capability of human subjects to control grasping force in closed loop using electrotactile feedback. We have developed a realistic experimental setup for virtual grasping, which operated in real time, included a set of real life objects, as well as a graphical and dynamical model of the prosthesis. We have used the setup to test 10 healthy, able bodied subjects to investigate the role of training, feedback and feedforward control, robustness of the closed loop, and the ability of the human subjects to generalize the control to previously "unseen" objects. Overall, the outcomes of this study are very optimistic with regard to the benefits of feedback and reveal various, practically relevant, aspects of closed-loop control.
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Virtual Grasping: Closed-Loop Force Control Using Electrotactile Feedback
Directory of Open Access Journals (Sweden)
Nikola Jorgovanovic
2014-01-01
Full Text Available Closing the control loop by providing somatosensory feedback to the user of a prosthesis is a well-known, long standing challenge in the field of prosthetics. Various approaches have been investigated for feedback restoration, ranging from direct neural stimulation to noninvasive sensory substitution methods. Although there are many studies presenting closed-loop systems, only a few of them objectively evaluated the closed-loop performance, mostly using vibrotactile stimulation. Importantly, the conclusions about the utility of the feedback were partly contradictory. The goal of the current study was to systematically investigate the capability of human subjects to control grasping force in closed loop using electrotactile feedback. We have developed a realistic experimental setup for virtual grasping, which operated in real time, included a set of real life objects, as well as a graphical and dynamical model of the prosthesis. We have used the setup to test 10 healthy, able bodied subjects to investigate the role of training, feedback and feedforward control, robustness of the closed loop, and the ability of the human subjects to generalize the control to previously “unseen” objects. Overall, the outcomes of this study are very optimistic with regard to the benefits of feedback and reveal various, practically relevant, aspects of closed-loop control.
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Combined feedforward and feedback control of end milling system
Čuš, Franc; Župerl, Uroš; Balič, Jože
2012-01-01
Purpose: Purpose of this paper. An intelligent control system is presented that uses a combination of feedforward and feedback for cutting force control in end milling.Design/methodology/approach: The network is trained by the feedback output that is minimized during training and most control action for disturbance rejection is finally performed by the rapid feedforward action of the network.Findings: The feedback controller corrects for errors caused by external disturbances. The feedforward...
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Analysis of complex systems using neural networks
International Nuclear Information System (INIS)
Uhrig, R.E.
1992-01-01
The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms), to some of the problems of complex engineering systems has the potential to enhance the safety, reliability, and operability of these systems. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network (e.g., a fast Fourier transformation of the time-series data to produce a spectral plot of the data). Specific applications described include: (1) Diagnostics: State of the Plant (2) Hybrid System for Transient Identification, (3) Sensor Validation, (4) Plant-Wide Monitoring, (5) Monitoring of Performance and Efficiency, and (6) Analysis of Vibrations. Although specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems
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Comparison of Power Generating Systems Using Feedback Effect Modeling
International Nuclear Information System (INIS)
Kim, Seong Ho; Kim, Kil Yoo; Kim, Tae Woon
2005-01-01
Comparative assessment of various power systems can be treated as a multicriteria decision-making (MCDM) problem. In reality, there is interdependence among decision elements (e.g., decision goal, decision criteria, and decision alternatives). In our previous work, using an analytic hierarchy process (AHP) technique, a comprehensive assessment framework for national power systems has been developed. It was assumed in the AHP modeling that there is no interdependence among decision elements. In the present work, one of interdependence phenomena, feedback effect, is investigated in the context of network structures instead of one-way directional tree structures. Moreover, attitudes of decision-makers can be incorporated into the feedback effect modeling. The main objectives of this work are to develop a feedback effect modeling using an analytic network process (ANP) technique and to demonstrate the feedback effect using a numerical example in comparison to the hierarchy model
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NNETS - NEURAL NETWORK ENVIRONMENT ON A TRANSPUTER SYSTEM
Villarreal, J.
1994-01-01
The primary purpose of NNETS (Neural Network Environment on a Transputer System) is to provide users a high degree of flexibility in creating and manipulating a wide variety of neural network topologies at processing speeds not found in conventional computing environments. To accomplish this purpose, NNETS supports back propagation and back propagation related algorithms. The back propagation algorithm used is an implementation of Rumelhart's Generalized Delta Rule. NNETS was developed on the INMOS Transputer. NNETS predefines a Back Propagation Network, a Jordan Network, and a Reinforcement Network to assist users in learning and defining their own networks. The program also allows users to configure other neural network paradigms from the NNETS basic architecture. The Jordan network is basically a feed forward network that has the outputs connected to a pseudo input layer. The state of the network is dependent on the inputs from the environment plus the state of the network. The Reinforcement network learns via a scalar feedback signal called reinforcement. The network propagates forward randomly. The environment looks at the outputs of the network to produce a reinforcement signal that is fed back to the network. NNETS was written for the INMOS C compiler D711B version 1.3 or later (MS-DOS version). A small portion of the software was written in the OCCAM language to perform the communications routing between processors. NNETS is configured to operate on a 4 X 10 array of Transputers in sequence with a Transputer based graphics processor controlled by a master IBM PC 286 (or better) Transputer. A RGB monitor is required which must be capable of 512 X 512 resolution. It must be able to receive red, green, and blue signals via BNC connectors. NNETS is meant for experienced Transputer users only. The program is distributed on 5.25 inch 1.2Mb MS-DOS format diskettes. NNETS was developed in 1991. Transputer and OCCAM are registered trademarks of Inmos Corporation. MS
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Neural network-based model reference adaptive control system.
Patino, H D; Liu, D
2000-01-01
In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.
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General Output Feedback Stabilization for Fractional Order Systems: An LMI Approach
Directory of Open Access Journals (Sweden)
Yiheng Wei
2014-01-01
Full Text Available This paper is concerned with the problem of general output feedback stabilization for fractional order linear time-invariant (FO-LTI systems with the fractional commensurate order 0<α<2. The objective is to design suitable output feedback controllers that guarantee the stability of the resulting closed-loop systems. Based on the slack variable method and our previous stability criteria, some new results in the form of linear matrix inequality (LMI are developed to the static and dynamic output feedback controllers synthesis for the FO-LTI system with 0<α<1. Furthermore, the results are extended to stabilize the FO-LTI systems with 1≤α<2. Finally, robust output feedback control is discussed. Numerical examples are given to illustrate the effectiveness of the proposed design methods.
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Consistency properties of chaotic systems driven by time-delayed feedback
Jüngling, T.; Soriano, M. C.; Oliver, N.; Porte, X.; Fischer, I.
2018-04-01
Consistency refers to the property of an externally driven dynamical system to respond in similar ways to similar inputs. In a delay system, the delayed feedback can be considered as an external drive to the undelayed subsystem. We analyze the degree of consistency in a generic chaotic system with delayed feedback by means of the auxiliary system approach. In this scheme an identical copy of the nonlinear node is driven by exactly the same signal as the original, allowing us to verify complete consistency via complete synchronization. In the past, the phenomenon of synchronization in delay-coupled chaotic systems has been widely studied using correlation functions. Here, we analytically derive relationships between characteristic signatures of the correlation functions in such systems and unequivocally relate them to the degree of consistency. The analytical framework is illustrated and supported by numerical calculations of the logistic map with delayed feedback for different replica configurations. We further apply the formalism to time series from an experiment based on a semiconductor laser with a double fiber-optical feedback loop. The experiment constitutes a high-quality replica scheme for studying consistency of the delay-driven laser and confirms the general theoretical results.
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Impact of biogenic emissions on feedbacks in the climate system
Krüger, Olaf
2017-04-01
Impact of biogenic emissions on feedbacks in the climate system Bio-geophysical feedback between marine or continental ecosystems and the atmosphere potentially can alter climate change. A prominent feedback loop which is under discussion since 1983 bases on the emission of biologically produced gases - molecular oxygen, sulphur containing compounds and possibly isoprene, supersaturated in oceanic waters - into the marine troposphere. These by-products of phytoplankton metabolism lead to aerosol production and procure sustained influence on climate via modulation of cloud optical properties. In this contribution some findings related to the above mentioned climate processes are presented with special emphasis on marine ecosystems. A comparison of marine and continental ecosystems is made and different processes with major impact on feedbacks in the climate system are discussed.
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Bio-inspired spiking neural network for nonlinear systems control.
Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M
2018-08-01
Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.
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Directory of Open Access Journals (Sweden)
Maryam M Shanechi
Full Text Available Real-time brain-machine interfaces (BMI have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.
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Application of neural networks in CRM systems
Directory of Open Access Journals (Sweden)
Bojanowska Agnieszka
2017-01-01
Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.
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Controlling chaotic systems via nonlinear feedback control
International Nuclear Information System (INIS)
Park, Ju H.
2005-01-01
In this article, a new method to control chaotic systems is proposed. Using Lyapunov method, we design a nonlinear feedback controller to make the controlled system be stabilized. A numerical example is given to illuminate the design procedure and advantage of the result derived
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Next Generation Environmentally-Friendly Driving Feedback Systems Research and Development
Energy Technology Data Exchange (ETDEWEB)
Barth, Matthew [Regents Of The University Of California, Riverside, CA (United States); Boriboonsomsin, Kanok [Regents Of The University Of California, Riverside, CA (United States)
2014-12-31
The objective of this project is to design, develop, and demonstrate a next-generation, federal safety- and emission-complaint driving feedback system that can be deployed across the existing vehicle fleet and improve fleet average fuel efficiency by at least 2%. The project objective was achieved with the driving feedback system that encourages fuel-efficient vehicle travel and operation through: 1) Eco-Routing Navigation module that suggests the most fuel-efficient route from one stop to the next, 2) Eco-Driving Feedback module that provides sensible information, recommendation, and warning regarding fuel-efficient vehicle operation, and 3) Eco-Score and Eco-Rank module that provides a means for driving performance tracking, self-evaluation, and peer comparison. The system also collects and stores vehicle travel and operation data, which are used by Algorithm Updating module to customize the other modules for specific vehicles and adapts them to specific drivers over time. The driving feedback system was designed and developed as an aftermarket technology that can be retrofitted to vehicles in the existing fleet. It consists of a mobile application for smart devices running Android operating system, a vehicle on-board diagnostics connector, and a data server. While the system receives and utilizes real-time vehicle and engine data from the vehicle’s controller area network bus through the vehicle’s on-board diagnostic connector, it does not modify or interfere with the vehicle’s controller area network bus, and thus, is in compliance with federal safety and emission regulations. The driving feedback system was demonstrated and then installed on 45 vehicles from three different fleets for field operational test. These include 15 private vehicles of the general public, 15 pickup trucks of the California Department of Transportation that are assigned to individual employees for business use, and 15 shuttle buses of the Riverside Transit Agency that are used
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International Nuclear Information System (INIS)
Thompson, K.A.; Jobe, R.K.; Johnson, R.; Phinney, N.
1987-02-01
Two classes of computer-controlled feedback have been implemented to stabilize parameters in subsystems of the SLC: (1) ''slow'' (time scales ∼ minutes) feedback, and (2) ''fast'', i.e., pulse-to-pulse, feedback. The slow loops run in a single FEEDBACK process in the SLC host VAX, which acquires signals and sets control parameters via communication with the database and the network of normal SLC microprocessors. Slow loops exist to stabilize beam energy and energy spread, beam position and angle, and timing of kicker magnets, and to compensate for changes in the phase length of the rf drive line. The fast loops run in dedicated microprocessors, and may sample and/or feedback on particular parameters as often as every pulse of the SLC beam. The first implementations of fast feedback are to control transverse beam blow-up and to stabilize the energy and energy spread of bunches going into the SLC arcs. The overall architecture of the feedback software and the operator interface for controlling loops are discussed
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Neural Network for Optimization of Existing Control Systems
DEFF Research Database (Denmark)
Madsen, Per Printz
1995-01-01
The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems.......The purpose of this paper is to develop methods to use Neural Network based Controllers (NNC) as an optimization tool for existing control systems....
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Liu, Lingfeng; Miao, Suoxia; Cheng, Mengfan; Gao, Xiaojing
2016-02-01
A coupled system with varying parameters is proposed to improve the security of optoelectronic delayed feedback system. This system is coupled by two parameter-varied optoelectronic delayed feedback systems with chaotic modulation. Dynamics performance results show that this system has a higher complexity compared to the original one. Furthermore, this system can conceal the time delay effectively against the autocorrelation function and delayed mutual information method and can increase the dimension space of secure parameters to resist brute-force attack by introducing the digital chaotic systems.
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Digital closed orbit feedback system for the advanced photon source storage ring
International Nuclear Information System (INIS)
Chung, Y.; Barr, D.; Decker, G.
1995-01-01
The Advanced Photon Source (APS) is a dedicated third-generation synchrotron light source with a nominal energy of 7 GeV and a circumference of 1104 m. The closed orbit feedback system for the APS storage ring employs unified global and local feedback systems for stabilization of particle and photon beams based on digital signal processing (DSP). Hardware and software aspects of the system will be described in this paper. In particular, we will discuss global and local orbit feedback algorithms, PID (proportional, integral, and derivative) control algorithm, application of digital signal processing to compensate for vacuum chamber eddy current effects, resolution of the interaction between global and local systems through decoupling, self-correction of the local bump closure error, user interface through the APS control system, and system performance in the frequency and time domains. The system hardware including the DSPs is distributed in 20 VME crates around the ring, and the entire feedback system runs synchronously at 4-kHz sampling frequency in order to achieve a correction bandwidth exceeding 100 Hz. The required data sharing between the global and local feedback systems is facilitated via the use of fiber-optically-networked reflective memories
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Wideband feedback system prototype validation
Li, K; Bjorsvik, E; Fox, J; Hofle, W; Kotzian, G; Rivetta, C; Salvant, B; Turgut, O
2017-01-01
A wideband feedback demonstrator system has been de-veloped in collaboration with US-LARP under the joint lead-ership of CERN and SLAC. The system includes widebandkicker structures and amplifiers along with a fast digital re-configurable system up to 4 GS/s for single bunch and multibunch control. Most of the components have been installedin recent years and have been put into operation to test bothintra-bunch damping and individual bunch control in a multibunch train. In this note we report on the MD program,procedure and key findings that were made with this systemin the past year.
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Feedback control and beam diagnostic algorithms for a multiprocessor DSP system
International Nuclear Information System (INIS)
Teytelman, D.; Claus, R.; Fox, J.; Hindi, H.; Linscott, I.; Prabhakar, S.
1996-09-01
The multibunch longitudinal feedback system developed for use by PEP-II, ALS and DAΦNE uses a parallel array of digital signal processors to calculate the feedback signals from measurements of beam motion. The system is designed with general-purpose programmable elements which allow many feedback operating modes as well as system diagnostics, calibrations and accelerator measurements. The overall signal processing architecture of the system is illustrated. The real-time DSP algorithms and off-line postprocessing tools are presented. The problems in managing 320 K samples of data collected in one beam transient measurement are discussed and the solutions are presented. Example software structures are presented showing the beam feedback process, techniques for modal analysis of beam motion(used to quantify growth and damping rates of instabilities) and diagnostic functions (such as timing adjustment of beam pick-up and kicker components). These operating techniques are illustrated with example results obtained from the system installed at the Advanced Light Source at LBL
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Short-term synaptic plasticity and heterogeneity in neural systems
Mejias, J. F.; Kappen, H. J.; Longtin, A.; Torres, J. J.
2013-01-01
We review some recent results on neural dynamics and information processing which arise when considering several biophysical factors of interest, in particular, short-term synaptic plasticity and neural heterogeneity. The inclusion of short-term synaptic plasticity leads to enhanced long-term memory capacities, a higher robustness of memory to noise, and irregularity in the duration of the so-called up cortical states. On the other hand, considering some level of neural heterogeneity in neuron models allows neural systems to optimize information transmission in rate coding and temporal coding, two strategies commonly used by neurons to codify information in many brain areas. In all these studies, analytical approximations can be made to explain the underlying dynamics of these neural systems.
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Han, Seong-Ik; Lee, Jang-Myung
2014-01-01
This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.
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Direct adaptive control using feedforward neural networks
Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira
2003-01-01
ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...
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Representation of neural networks as Lotka-Volterra systems
International Nuclear Information System (INIS)
Moreau, Yves; Vandewalle, Joos; Louies, Stephane; Brenig, Leon
1999-01-01
We study changes of coordinates that allow the representation of the ordinary differential equations describing continuous-time recurrent neural networks into differential equations describing predator-prey models--also called Lotka-Volterra systems. We transform the equations for the neural network first into quasi-monomial form, where we express the vector field of the dynamical system as a linear combination of products of powers of the variables. In practice, this transformation is possible only if the activation function is the hyperbolic tangent or the logistic sigmoied. From this quasi-monomial form, we can directly transform the system further into Lotka-Volterra equations. The resulting Lotka-Volterra system is of higher dimension than the original system, but the behavior of its first variables is equivalent to the behavior of the original neural network
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Convergent dynamics for multistable delayed neural networks
International Nuclear Information System (INIS)
Shih, Chih-Wen; Tseng, Jui-Pin
2008-01-01
This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2 n stable equilibria and exactly (3 n − 2 n ) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory
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Design and evaluation of a Flight Envelope Protection haptic feedback system
Ellerbroek, J.; Rodriguez Martin, M.J.M.; Lombaerts, T; van Paassen, M.M.; Mulder, M.
2016-01-01
This paper describes the design and evaluation of a shared control, haptic feedback system to communicate Flight Envelope Protection System intent. The concept uses a combination of stiffness feedback and vibration to communicate proximity of the aircraft state to flight envelope boundaries. In
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Introducing a feedback training system for guided home rehabilitation
Directory of Open Access Journals (Sweden)
Disselhorst-Klug Catherine
2010-01-01
Full Text Available Abstract As the number of people requiring orthopaedic intervention is growing, individualized physiotherapeutic rehabilitation and adequate postoperative care becomes increasingly relevant. The chances of improvement in the patients condition is directly related to the performance and consistency of the physiotherapeutic exercises. In this paper a smart, cost-effective and easy to use Feedback Training System for home rehabilitation based on standard resistive elements is introduced. This ensures high accuracy of the exercises performed and offers guidance and control to the patient by offering direct feedback about the performance of the movements. 46 patients were recruited and performed standard physiotherapeutic training to evaluate the system. The results show a significant increase in the patient's ability to reproduce even simple physiotherapeutic exercises when being supported by the Feedback Training System. Thus physiotherapeutic training can be extended into the home environment whilst ensuring a high quality of training.
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Introducing a feedback training system for guided home rehabilitation.
Kohler, Fabian; Schmitz-Rode, Thomas; Disselhorst-Klug, Catherine
2010-01-15
As the number of people requiring orthopaedic intervention is growing, individualized physiotherapeutic rehabilitation and adequate postoperative care becomes increasingly relevant. The chances of improvement in the patients condition is directly related to the performance and consistency of the physiotherapeutic exercises.In this paper a smart, cost-effective and easy to use Feedback Training System for home rehabilitation based on standard resistive elements is introduced. This ensures high accuracy of the exercises performed and offers guidance and control to the patient by offering direct feedback about the performance of the movements.46 patients were recruited and performed standard physiotherapeutic training to evaluate the system. The results show a significant increase in the patient's ability to reproduce even simple physiotherapeutic exercises when being supported by the Feedback Training System. Thus physiotherapeutic training can be extended into the home environment whilst ensuring a high quality of training.
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Instruction, Feedback and Biometrics: The User Interface for Fingerprint Authentication Systems
Riley, Chris; Johnson, Graham; McCracken, Heather; Al-Saffar, Ahmed
Biometric authentication is the process of establishing an individual’s identity through measurable characteristics of their behaviour, anatomy or physiology. Biometric technologies, such as fingerprint systems, are increasingly being used in a diverse range of contexts from immigration control, to banking and personal computing. As is often the case with emerging technologies, the usability aspects of system design have received less attention than technical aspects. Fingerprint systems pose a number of challenges for users and past research has identified issues with correct finger placement, system feedback and instruction. This paper describes the development of an interface for fingerprint systems using an iterative, participative design approach. During this process, several different methods for the presentation of instruction and feedback were identified. The different types of instruction and feedback were tested in a study involving 82 participants. The results showed that feedback had a statistically significant effect on overall system performance, but instruction did not. The design recommendations emerging from this study, and the use of participatory design in this context, are discussed.
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The neural sociometer: brain mechanisms underlying state self-esteem.
Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R
2011-11-01
On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.
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Neural PID Control Strategy for Networked Process Control
Directory of Open Access Journals (Sweden)
Jianhua Zhang
2013-01-01
Full Text Available A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.
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Feedback control and adaptive control of the energy resource chaotic system
International Nuclear Information System (INIS)
Sun Mei; Tian Lixin; Jiang Shumin; Xu Jun
2007-01-01
In this paper, the problem of control for the energy resource chaotic system is considered. Two different method of control, feedback control (include linear feedback control, non-autonomous feedback control) and adaptive control methods are used to suppress chaos to unstable equilibrium or unstable periodic orbits. The Routh-Hurwitz criteria and Lyapunov direct method are used to study the conditions of the asymptotic stability of the steady states of the controlled system. The designed adaptive controller is robust with respect to certain class of disturbances in the energy resource chaotic system. Numerical simulations are presented to show these results
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Persistent disturbance rejection via state feedback for networked control systems
Energy Technology Data Exchange (ETDEWEB)
Yue Dong [Institute of Information and Control Engineering Technology, Nanjing Normal University, 78 Bancang Street, Nanjing, Jiangsu 210042 (China)], E-mail: medongy@njnu.edu.cn; Lam, James [Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road (Hong Kong); Wang Zidong [Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH (United Kingdom)], E-mail: Zidong.Wang@brunel.ac.uk
2009-04-15
The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.
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Survey of Digital Feedback Systems in High Current Storage Rings
International Nuclear Information System (INIS)
Teytelman, Dmitry
2003-01-01
In the last decade demand for brightness in synchrotron light sources and luminosity in circular colliders led to construction of multiple high current storage rings. Many of these new machines require feedback systems to achieve design stored beam currents. In the same time frame the rapid advances in the technology of digital signal processing allowed the implementation of these complex feedback systems. In this paper I concentrate on three applications of feedback to storage rings: orbit control in light sources, coupled-bunch instability control, and low-level RF control. Each of these applications is challenging in areas of processing bandwidth, algorithm complexity, and control of time-varying beam and system dynamics. I will review existing implementations as well as comment on promising future directions
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Persistent disturbance rejection via state feedback for networked control systems
International Nuclear Information System (INIS)
Yue Dong; Lam, James; Wang Zidong
2009-01-01
The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.
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Operation and performance of a longitudinal feedback system using digital signal processing
International Nuclear Information System (INIS)
Teytelman, D.; Fox, J.; Hindi, H.
1994-01-01
A programmable longitudinal feedback system using a parallel array of AT ampersand T 1610 digital signal processors has been developed as a component of the PEP-II R ampersand D program. This system has been installed at the Advanced Light Source (LBL) and implements full speed bunch by bunch signal processing for storage rings with bunch spacing of 4ns. Open and closed loop results showing the action of the feedback system are presented, and the system is shown to damp coupled-bunch instabilities in the ALS. A unified PC-based software environment for the feedback system operation is also described
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Tutorial on beam-based feedback systems for linacs
International Nuclear Information System (INIS)
Hendrickson, L.; Allison, S.; Gromme, T.; Grossberg, P.; Himel, T.; Krauter, K.; MacKenzie, R.; Ross, M.; Sass, R.; Shoaee, H.
1994-08-01
A generalized fast feedback system stabilizes beams in the SLC. It performs measurements and modifies actuator settings to control beam states such as position, angle, energy and intensity on a pulse to pulse basis. An adaptive cascade feature allows communication between a series of linac loops, avoiding overcorrection problems. The system is based on the state space formalism of digital control theory. Due to the database-driven design, new loops are added without requiring software modifications. Recent enhancements support the monitoring and control of nonlinear states such as beam phase using excitation techniques. In over three years of operation, the feedback system has grown from its original eight loops to more than fifty loops, and it has been invaluable in stabilizing the machine
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Spiking neural P systems with multiple channels.
Peng, Hong; Yang, Jinyu; Wang, Jun; Wang, Tao; Sun, Zhang; Song, Xiaoxiao; Luo, Xiaohui; Huang, Xiangnian
2017-11-01
Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Analysis, Design, and Evaluation of Acoustic Feedback Cancellation Systems for Hearing Aids
DEFF Research Database (Denmark)
Guo, Meng
2013-01-01
Acoustic feedback problems occur when the output loudspeaker signal of an audio system is partly returned to the input microphone via an acoustic coupling through the air. This problem often causes significant performance degradations in applications such as public address systems and hearing aids....... In the worst case, the audio system becomes unstable and howling occurs. In this work, first we analyze a general multiple microphone audio processing system, where a cancellation system using adaptive filters is used to cancel the effect of acoustic feedback. We introduce and derive an accurate approximation...... in acoustic feedback cancellation for hearing aids. It utilizes a probe noise signal which is generated with a specific characteristic so that it can facilitate an unbiased adaptive filter estimation with fast tracking of feedback path variations/changes despite its low signal level. We show in a hearing aid...
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Theoretical and experimental study of Chen chaotic system with notch filter feedback control
International Nuclear Information System (INIS)
Ming, Zhang Xiao; Jian-Hua, Peng; Ju-Fang, Chen
2010-01-01
Since the past two decades, the time delay feedback control method has attracted more and more attention in chaos control studies because of its simplicity and efficiency compared with other chaos control schemes. Recently, it has been proposed to suppress low-dimensional chaos with the notch filter feedback control method, which can be implemented in a laser system. In this work, we have analytically determined the controllable conditions for notch filter feedback controlling of Chen chaotic system in terms of the Hopf bifurcation theory. The conditions for notch filter feedback controlled Chen chaoitc system having a stable limit cycle solution are given. Meanwhile, we also analysed the Hopf bifurcation direction, which is very important for parameter settings in notch filter feedback control applications. Finally, we apply the notch filter feedback control methods to the electronic circuit experiments and numerical simulations based on the theoretical analysis. The controlling results of notch filter feedback control method well prove the feasibility and reliability of the theoretical analysis. (general)
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Directory of Open Access Journals (Sweden)
Zhe Dong
2014-02-01
Full Text Available Small modular reactors (SMRs could be beneficial in providing electricity power safely and also be viable for applications such as seawater desalination and heat production. Due to its inherent safety features, the modular high temperature gas-cooled reactor (MHTGR has been seen as one of the best candidates for building SMR-based nuclear power plants. Since the MHTGR dynamics display high nonlinearity and parameter uncertainty, it is necessary to develop a nonlinear adaptive power-level control law which is not only beneficial to the safe, stable, efficient and autonomous operation of the MHTGR, but also easy to implement practically. In this paper, based on the concept of shifted-ectropy and the physically-based control design approach, it is proved theoretically that the simple proportional-differential (PD output-feedback power-level control can provide asymptotic closed-loop stability. Then, based on the strong approximation capability of the multi-layer perceptron (MLP artificial neural network (ANN, a compensator is established to suppress the negative influence caused by system parameter uncertainty. It is also proved that the MLP-compensated PD power-level control law constituted by an experientially-tuned PD regulator and this MLP-based compensator can guarantee bounded closed-loop stability. Numerical simulation results not only verify the theoretical results, but also illustrate the high performance of this MLP-compensated PD power-level controller in suppressing the oscillation of process variables caused by system parameter uncertainty.
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A wirelessly powered microspectrometer for neural probe-pin device
Choi, Sang H.; Kim, Min H.; Song, Kyo D.; Yoon, Hargsoon; Lee, Uhn
2015-12-01
Treatment of neurological anomalies, whether done invasively or not, places stringent demands on device functionality and size. We have developed a micro-spectrometer for use as an implantable neural probe to monitor neuro-chemistry in synapses. The micro-spectrometer, based on a NASA-invented miniature Fresnel grating, is capable of differentiating the emission spectra from various brain tissues. The micro-spectrometer meets the size requirements, and is able to probe the neuro-chemistry and suppression voltage typically associated with a neural anomaly. This neural probe-pin device (PPD) is equipped with wireless power technology (WPT) to enable operation in a continuous manner without requiring an implanted battery. The implanted neural PPD, together with a neural electronics interface and WPT, enable real-time measurement and control/feedback for remediation of neural anomalies. The design and performance of the combined PPD/WPT device for monitoring dopamine in a rat brain will be presented to demonstrate the current level of development. Future work on this device will involve the addition of an embedded expert system capable of performing semi-autonomous management of neural functions through a routine of sensing, processing, and control.
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Quantifying the relative contributions of divisive and subtractive feedback to rhythm generation.
Directory of Open Access Journals (Sweden)
Joël Tabak
2011-04-01
Full Text Available Biological systems are characterized by a high number of interacting components. Determining the role of each component is difficult, addressed here in the context of biological oscillations. Rhythmic behavior can result from the interplay of positive feedback that promotes bistability between high and low activity, and slow negative feedback that switches the system between the high and low activity states. Many biological oscillators include two types of negative feedback processes: divisive (decreases the gain of the positive feedback loop and subtractive (increases the input threshold that both contribute to slowly move the system between the high- and low-activity states. Can we determine the relative contribution of each type of negative feedback process to the rhythmic activity? Does one dominate? Do they control the active and silent phase equally? To answer these questions we use a neural network model with excitatory coupling, regulated by synaptic depression (divisive and cellular adaptation (subtractive feedback. We first attempt to apply standard experimental methodologies: either passive observation to correlate the variations of a variable of interest to system behavior, or deletion of a component to establish whether a component is critical for the system. We find that these two strategies can lead to contradictory conclusions, and at best their interpretive power is limited. We instead develop a computational measure of the contribution of a process, by evaluating the sensitivity of the active (high activity and silent (low activity phase durations to the time constant of the process. The measure shows that both processes control the active phase, in proportion to their speed and relative weight. However, only the subtractive process plays a major role in setting the duration of the silent phase. This computational method can be used to analyze the role of negative feedback processes in a wide range of biological rhythms.
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Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control
Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan
2003-01-01
An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.
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Positivity effect in healthy aging in observational but not active feedback-learning.
Bellebaum, Christian; Rustemeier, Martina; Daum, Irene
2012-01-01
The present study investigated the impact of healthy aging on the bias to learn from positive or negative performance feedback in observational and active feedback learning. In active learning, a previous study had already shown a negative learning bias in healthy seniors older than 75 years, while no bias was found for younger seniors. However, healthy aging is accompanied by a 'positivity effect', a tendency to primarily attend to stimuli with positive valence. Based on recent findings of dissociable neural mechanisms in active and observational feedback learning, the positivity effect was hypothesized to influence older participants' observational feedback learning in particular. In two separate experiments, groups of young (mean age 27) and older participants (mean age 60 years) completed an observational or active learning task designed to differentially assess positive and negative learning. Older but not younger observational learners showed a significant bias to learn better from positive than negative feedback. In accordance with previous findings, no bias was found for active learning. This pattern of results is discussed in terms of differences in the neural underpinnings of active and observational learning from performance feedback.
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On a new time-delayed feedback control of chaotic systems
International Nuclear Information System (INIS)
Tian Lixin; Xu Jun; Sun Mei; Li Xiuming
2009-01-01
In this paper, using the idea of the successive dislocation feedback method, a new time-delayed feedback control method called the successive dislocation time-delayed feedback control (SDTDFC) is designed. Firstly, the idea of SDTDFC is introduced. Then some analytic sufficient conditions of the chaos control from the SDTDFC approach are derived for stabilization. Finally, some established results are further clarified via a case study of the Lorenz system with the numerical simulations.
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Hemmen, J; Schulten, Klaus
1994-01-01
Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same "object" of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to "bind" sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregatio...
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Approximate solutions of dual fuzzy polynomials by feed-back neural networks
Directory of Open Access Journals (Sweden)
Ahmad Jafarian
2012-11-01
Full Text Available Recently, artificial neural networks (ANNs have been extensively studied and used in different areas such as pattern recognition, associative memory, combinatorial optimization, etc. In this paper, we investigate the ability of fuzzy neural networks to approximate solution of a dual fuzzy polynomial of the form $a_{1}x+ ...+a_{n}x^n =b_{1}x+ ...+b_{n}x^n+d,$ where $a_{j},b_{j},d epsilon E^1 (for j=1,...,n.$ Since the operation of fuzzy neural networks is based on Zadeh's extension principle. For this scope we train a fuzzified neural network by back-propagation-type learning algorithm which has five layer where connection weights are crisp numbers. This neural network can get a crisp input signal and then calculates its corresponding fuzzy output. Presented method can give a real approximate solution for given polynomial by using a cost function which is defined for the level sets of fuzzy output and target output. The simulation results are presented to demonstrate the efficiency and effectiveness of the proposed approach.
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van Schie, C C; Chiu, C D; Rombouts, S A R B; Heiser, W J; Elzinga, B M
2018-02-27
The way we view ourselves may play an important role in our responses to interpersonal interactions. In this study, we investigate how feedback valence, consistency of feedback with self-knowledge and global self-esteem influence affective and neural responses to social feedback. Participants (N = 46) with a high range of self-esteem levels performed the social feedback task in an MRI scanner. Negative, intermediate and positive feedback was provided, supposedly by another person based on a personal interview. Participants rated their mood and applicability of feedback to the self. Analyses on trial basis on neural and affective responses are used to incorporate applicability of individual feedback words. Lower self-esteem related to low mood especially after receiving non-applicable negative feedback. Higher self-esteem related to increased PCC and precuneus activation (i.e., self-referential processing) for applicable negative feedback. Lower self-esteem related to decreased mPFC, insula, ACC and PCC activation (i.e, self-referential processing) during positive feedback and decreased TPJ activation (i.e., other referential processing) for applicable positive feedback. Self-esteem and consistency of feedback with self-knowledge appear to guide our affective and neural responses to social feedback. This may be highly relevant for the interpersonal problems that individuals face with low self-esteem and negative self-views.
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Design of EAST LHCD high power supply feedback control system based on PLC
International Nuclear Information System (INIS)
Hu Huaichuan; Shan Jiafang
2009-01-01
Design of EAST LHCD -35kV/5.6MW high power supply feedback control system based on PLC is described. Industrial computer and PLC are used to control high power supply in the system. PID arithmetic is adopted to achieve the feedback control of voltage of high power supply. Operating system is base on real-time operating system of QNX. Good controlling properties and reliable protective properties of the feedback control system are proved by the experiment results. (authors)
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Effect of biased feedback on motor imagery learning in BCI-teleoperation system
Directory of Open Access Journals (Sweden)
Maryam eAlimardani
2014-04-01
Full Text Available Feedback design is an important issue in motor imagery BCI systems. Regardless, to date it has not been reported how feedback presentation can optimize co-adaptation between a human brain and such systems. This paper assesses the effect of realistic visual feedback on users’ BC performance and motor imagery skills. We previously developed a tele-operation system for a pair of humanlike robotic hands and showed that BCI control of such hands along with first-person perspective visual feedback of movements can arouse a sense of embodiment in the operators. In the first stage of this study, we found that the intensity of this ownership illusion was associated with feedback presentation and subjects’ performance during BCI motion control. In the second stage, we probed the effect of positive and negative feedback bias on subjects’ BCI performance and motor imagery skills. Although the subject specific classifier, which was set up at the beginning of experiment, detected no significant change in the subjects’ online performance, evaluation of brain activity patterns revealed that subjects’ self-regulation of motor imagery features improved due to a positive bias of feedback and a possible occurrence of ownership illusion. Our findings suggest that in general training protocols for BCIs, manipulation of feedback can play an important role in the optimization of subjects’ motor imagery skills.
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Fast digital transverse feedback system for bunch train operation in CESR
International Nuclear Information System (INIS)
Rogers, J.T.; Billing, M.G.; Dobbins, J.A.
1996-01-01
We have developed a time domain transverse feedback system with the high bandwidth needed to control transverse instabilities when the CESR e + e - collider is filled with trains of closely spaced bunches. This system is based on parallel digital processors and a stripline driver. It is capable of acting on arbitrary patterns of bunches having a minimum spacing of 14 ns. Several simplifying features have been introduced. A single shorted stripline kicker driven by one power amplifier is used to control both counter-rotating beams. The desired feedback phase is achieved by sampling the bunch position at a single location on two independently selectable beam revolutions. The system adapts to changes in the betatron tune, bunch pattern, or desired damping rate through the loading of new parameters into the digital processors via the CESR control system. The feedback system also functions as a fast gated bunch current monitor. Both vertical and horizontal loops are now used in CESR operation. The measured betatron damping rates with the transverse feedback system in operation are in agreement with the analytical prediction and a computer simulation developed in connection with this work. (author)
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Fast digital transverse feedback system for bunch train operation in CESR
Energy Technology Data Exchange (ETDEWEB)
Rogers, J T; Billing, M G; Dobbins, J A [Cornell Univ., Ithaca, NY (United States). Lab. of Nuclear Studies; and others
1996-08-01
We have developed a time domain transverse feedback system with the high bandwidth needed to control transverse instabilities when the CESR e{sup +}e{sup -} collider is filled with trains of closely spaced bunches. This system is based on parallel digital processors and a stripline driver. It is capable of acting on arbitrary patterns of bunches having a minimum spacing of 14 ns. Several simplifying features have been introduced. A single shorted stripline kicker driven by one power amplifier is used to control both counter-rotating beams. The desired feedback phase is achieved by sampling the bunch position at a single location on two independently selectable beam revolutions. The system adapts to changes in the betatron tune, bunch pattern, or desired damping rate through the loading of new parameters into the digital processors via the CESR control system. The feedback system also functions as a fast gated bunch current monitor. Both vertical and horizontal loops are now used in CESR operation. The measured betatron damping rates with the transverse feedback system in operation are in agreement with the analytical prediction and a computer simulation developed in connection with this work. (author)
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The effects of driver identity on driving safety in a retrospective feedback system.
Zhao, Guozhen; Wu, Changxu
2012-03-01
Retrospective feedback that provides detailed information on a driver's performance in critical driving situations at the end of a trip enhances his/her driving behaviors and safe driving habits. Although this has been demonstrated by a previous study, retrospective feedback can be further improved and applied to non-critical driving situations, which is needed for transportation safety. To propose a new retrospective feedback system that uses driver identity (i.e., a driver's name) and to experimentally study its effects on measures of driving performance and safety in a driving simulator. We conducted a behavioral experimental study with 30 participants. "Feedback type" was a between-subject variable with three conditions: no feedback (control group), feedback without driver identity, and feedback with driver identity. We measured multiple aspects of participants' driving behavior. To control for potential confounds, factors that were significantly correlated with driving behavior (e.g., age and driving experience) were all entered as covariates into a multivariate analysis of variance. To examine the effects of speeding on collision severity in driving simulation studies, we also developed a new index - momentum of potential collision - with a set of equations. Subjects who used a feedback system with driver identity had the fewest speeding violations and central-line crossings, spent the least amount of time speeding and crossing the central line, had the lowest speeding and central-line crossing magnitude, ran the fewest red lights, and had the smallest momentum of potential collision compared to the groups with feedback without driver identity and without feedback (control group). The new retrospective feedback system with driver identity has the potential to enhance a person's driving safety (e.g., speeding, central-line crossing, momentum of potential collision), which is an indication of the valence of one's name in a feedback system design. Copyright
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Mohammadzadeh, Ardashir; Ghaemi, Sehraneh
2015-09-01
This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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Measuring Customer Behavior with Deep Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Veaceslav Albu
2016-03-01
Full Text Available In this paper, we propose a neural network model for human emotion and gesture classification. We demonstrate that the proposed architecture represents an effective tool for real-time processing of customer's behavior for distributed on-land systems, such as information kiosks, automated cashiers and ATMs. The proposed approach combines most recent biometric techniques with the neural network approach for real-time emotion and behavioral analysis. In the series of experiments, emotions of human subjects were recorded, recognized, and analyzed to give statistical feedback of the overall emotions of a number of targets within a certain time frame. The result of the study allows automatic tracking of user’s behavior based on a limited set of observations.
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[Real-time feedback systems for improvement of resuscitation quality].
Lukas, R P; Van Aken, H; Engel, P; Bohn, A
2011-07-01
The quality of chest compression is a determinant of survival after cardiac arrest. Therefore, the European Resuscitation Council (ERC) 2010 guidelines on resuscitation strongly focus on compression quality. Despite its impact on survival, observational studies have shown that chest compression quality is not reached by professional rescue teams. Real-time feedback devices for resuscitation are able to measure chest compression during an ongoing resuscitation attempt through a sternal sensor equipped with a motion and pressure detection system. In addition to the electrocardiograph (ECG) ventilation can be detected by transthoracic impedance monitoring. In cases of quality deviation, such as shallow chest compression depth or hyperventilation, feedback systems produce visual or acoustic alarms. Rescuers can thereby be supported and guided to the requested quality in chest compression and ventilation. Feedback technology is currently available both as a so-called stand-alone device and as an integrated feature in a monitor/defibrillator unit. Multiple studies have demonstrated sustainable enhancement in the education of resuscitation due to the use of real-time feedback technology. There is evidence that real-time feedback for resuscitation combined with training and debriefing strategies can improve both resuscitation quality and patient survival. Chest compression quality is an independent predictor for survival in resuscitation and should therefore be measured and documented in further clinical multicenter trials.
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Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks.
Wang, Leimin; Shen, Yi; Zhang, Guodong
Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.
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Push-Pull and Feedback Mechanisms Can Align Signaling System Outputs with Inputs.
Andrews, Steven S; Peria, William J; Yu, Richard C; Colman-Lerner, Alejandro; Brent, Roger
2016-11-23
Many cell signaling systems, including the yeast pheromone response system, exhibit "dose-response alignment" (DoRA), in which output of one or more downstream steps closely matches the fraction of occupied receptors. DoRA can improve the fidelity of transmitted dose information. Here, we searched systematically for biochemical network topologies that produced DoRA. Most networks, including many containing feedback and feedforward loops, could not produce DoRA. However, networks including "push-pull" mechanisms, in which the active form of a signaling species stimulates downstream activity and the nominally inactive form reduces downstream activity, enabled perfect DoRA. Networks containing feedbacks enabled DoRA, but only if they also compared feedback to input and adjusted output to match. Our results establish push-pull as a non-feedback mechanism to align output with variable input and maximize information transfer in signaling systems. They also suggest genetic approaches to determine whether particular signaling systems use feedback or push-pull control. Copyright © 2016 Elsevier Inc. All rights reserved.
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Object discrimination using optimized multi-frequency auditory cross-modal haptic feedback.
Gibson, Alison; Artemiadis, Panagiotis
2014-01-01
As the field of brain-machine interfaces and neuro-prosthetics continues to grow, there is a high need for sensor and actuation mechanisms that can provide haptic feedback to the user. Current technologies employ expensive, invasive and often inefficient force feedback methods, resulting in an unrealistic solution for individuals who rely on these devices. This paper responds through the development, integration and analysis of a novel feedback architecture where haptic information during the neural control of a prosthetic hand is perceived through multi-frequency auditory signals. Through representing force magnitude with volume and force location with frequency, the feedback architecture can translate the haptic experiences of a robotic end effector into the alternative sensory modality of sound. Previous research with the proposed cross-modal feedback method confirmed its learnability, so the current work aimed to investigate which frequency map (i.e. frequency-specific locations on the hand) is optimal in helping users distinguish between hand-held objects and tasks associated with them. After short use with the cross-modal feedback during the electromyographic (EMG) control of a prosthetic hand, testing results show that users are able to use audial feedback alone to discriminate between everyday objects. While users showed adaptation to three different frequency maps, the simplest map containing only two frequencies was found to be the most useful in discriminating between objects. This outcome provides support for the feasibility and practicality of the cross-modal feedback method during the neural control of prosthetics.
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H∞ synchronization of chaotic systems via dynamic feedback approach
International Nuclear Information System (INIS)
Lee, S.M.; Ji, D.H.; Park, Ju H.; Won, S.C.
2008-01-01
This Letter considers H ∞ synchronization of a general class of chaotic systems with external disturbance. Based on Lyapunov theory and linear matrix inequality (LMI) formulation, the novel feedback controller is established to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance to an H ∞ norm constraint. A dynamic feedback control scheme is proposed for H ∞ synchronization in chaotic systems for the first time. Then, a criterion for existence of the controller is given in terms of LMIs. Finally, a numerical simulation is presented to show the effectiveness of the proposed chaos synchronization scheme
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Review: the role of neural crest cells in the endocrine system.
Adams, Meghan Sara; Bronner-Fraser, Marianne
2009-01-01
The neural crest is a pluripotent population of cells that arises at the junction of the neural tube and the dorsal ectoderm. These highly migratory cells form diverse derivatives including neurons and glia of the sensory, sympathetic, and enteric nervous systems, melanocytes, and the bones, cartilage, and connective tissues of the face. The neural crest has long been associated with the endocrine system, although not always correctly. According to current understanding, neural crest cells give rise to the chromaffin cells of the adrenal medulla, chief cells of the extra-adrenal paraganglia, and thyroid C cells. The endocrine tumors that correspond to these cell types are pheochromocytomas, extra-adrenal paragangliomas, and medullary thyroid carcinomas. Although controversies concerning embryological origin appear to have mostly been resolved, questions persist concerning the pathobiology of each tumor type and its basis in neural crest embryology. Here we present a brief history of the work on neural crest development, both in general and in application to the endocrine system. In particular, we present findings related to the plasticity and pluripotency of neural crest cells as well as a discussion of several different neural crest tumors in the endocrine system.
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Feedback Design Patterns for Math Online Learning Systems
Inventado, Paul Salvador; Scupelli, Peter; Heffernan, Cristina; Heffernan, Neil
2017-01-01
Increasingly, computer-based learning systems are used by educators to facilitate learning. Evaluations of several math learning systems show that they result in significant student learning improvements. Feedback provision is one of the key features in math learning systems that contribute to its success. We have recently been uncovering feedback…
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A study of reactor monitoring method with neural network
Energy Technology Data Exchange (ETDEWEB)
Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment
2001-03-01
The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)
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Integrating Artificial Immune, Neural and Endrocine Systems in Autonomous Sailing Robots
2010-09-24
system - Development of an adaptive hormone system capable of changing operation and control of the neural network depending on changing enviromental ...and control of the neural network depending on changing enviromental conditions • First basic design of the MOOP and a simple neural-endocrine based
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Frequency-difference-dependent stochastic resonance in neural systems
Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong
2017-08-01
Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.
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Modeling mutual feedback between users and recommender systems
Zeng, An; Yeung, Chi Ho; Medo, Matúš; Zhang, Yi-Cheng
2015-07-01
Recommender systems daily influence our decisions on the Internet. While considerable attention has been given to issues such as recommendation accuracy and user privacy, the long-term mutual feedback between a recommender system and the decisions of its users has been neglected so far. We propose here a model of network evolution which allows us to study the complex dynamics induced by this feedback, including the hysteresis effect which is typical for systems with non-linear dynamics. Despite the popular belief that recommendation helps users to discover new things, we find that the long-term use of recommendation can contribute to the rise of extremely popular items and thus ultimately narrow the user choice. These results are supported by measurements of the time evolution of item popularity inequality in real systems. We show that this adverse effect of recommendation can be tamed by sacrificing part of short-term recommendation accuracy.
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Effects of realistic force feedback in a robotic assisted minimally invasive surgery system.
Moradi Dalvand, Mohsen; Shirinzadeh, Bijan; Nahavandi, Saeid; Smith, Julian
2014-06-01
Robotic assisted minimally invasive surgery systems not only have the advantages of traditional laparoscopic procedures but also restore the surgeon's hand-eye coordination and improve the surgeon's precision by filtering hand tremors. Unfortunately, these benefits have come at the expense of the surgeon's ability to feel. Several research efforts have already attempted to restore this feature and study the effects of force feedback in robotic systems. The proposed methods and studies have some shortcomings. The main focus of this research is to overcome some of these limitations and to study the effects of force feedback in palpation in a more realistic fashion. A parallel robot assisted minimally invasive surgery system (PRAMiSS) with force feedback capabilities was employed to study the effects of realistic force feedback in palpation of artificial tissue samples. PRAMiSS is capable of actually measuring the tip/tissue interaction forces directly from the surgery site. Four sets of experiments using only vision feedback, only force feedback, simultaneous force and vision feedback and direct manipulation were conducted to evaluate the role of sensory feedback from sideways tip/tissue interaction forces with a scale factor of 100% in characterising tissues of varying stiffness. Twenty human subjects were involved in the experiments for at least 1440 trials. Friedman and Wilcoxon signed-rank tests were employed to statistically analyse the experimental results. Providing realistic force feedback in robotic assisted surgery systems improves the quality of tissue characterization procedures. Force feedback capability also increases the certainty of characterizing soft tissues compared with direct palpation using the lateral sides of index fingers. The force feedback capability can improve the quality of palpation and characterization of soft tissues of varying stiffness by restoring sense of touch in robotic assisted minimally invasive surgery operations.
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COMMISSIONING OF THE DIGITAL TRANSVERSE BUNCH-BY-BUNCH FEEDBACK SYSTEM FOR THE TLS
International Nuclear Information System (INIS)
HU, K.H.; KUO, C.H.; CHOU, P.J.; LEE, D.; HSU, S.Y.; CHEN, J.; WANG, C.J.; HSU, K.T.; KOBAYASHI, K.; NAKAMURA, T.; CHAO, A.W.; WENG, W.T.
2006-01-01
Multi-bunch instabilities degrade beam quality through increased beam emittance, energy spread and even beam loss. Feedback systems are used to suppress multi-bunch instabilities associated with the resistive wall of the beam ducts, cavity-like structures, and trapped ions. A new digital transverse bunch-by-bunch feedback system has recently been commissioned at the Taiwan Light Source, and has replaced the previous analog system. The new system has the advantages that it enlarges the tune acceptance and improves damping for transverse instability at high currents, such that top-up operation is achieved. After a coupled-bunch transverse instability was suppressed, more than 350 mA was successfully stored during preliminary commissioning. In this new system, a single feedback loop simultaneously suppresses both horizontal and vertical multi-bunch instabilities. Investigating the characteristics of the feedback loop and further improving the system performances are the next short-term goals. The feedback system employs the latest generation of field-programmable gate array (FPGA) processor to process bunch signals. Memory has been installed to capture up to 250 msec of bunch oscillation signal, considering system diagnostics suitable to support various beam physics studies
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Neurophysiological correlates of anhedonia in feedback processing
G.W. Mies (Gabry); I. van den Berg (Ivo); I.H.A. Franken (Ingmar); M. Smits (Marion); M.W. Molen, van der (Maurits); F.M. van der Veen (Frederik)
2013-01-01
textabstractDisturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a key role have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study
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Neurophysiological correlates of anhedonia in feedback processing
Mies, G.W.; Berg, I. van den; Franken, I.H.A.; Smits, M.; Molen, M.W. van der; Veen, F.M. van der
2013-01-01
Disturbances in feedback processing and a dysregulation of the neural circuit in which the cingulate cortex plays a keyrole have been frequently observed in depression. Since depression is a heterogeneous disease, instead of focusing on the depressive state in general, this study investigated the
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Design of the ILC Prototype FONT4 Digital Intra-Train Beam-Based Feedback System
International Nuclear Information System (INIS)
Burrows, P.; Queen Mary, U. of London; Christian, G.B.; Hartin, A.F.; Dabiri Khah, H.; White, G.R.; Oxford U.; Clarke, C.C.; Perry, C.; Oxford Instruments; Kalinin, A.; Daresbury; McCormick, D.J.; Molloy, S.; Ross, M.C.; SLAC
2007-01-01
We present the design of the FONT4 digital intra-train beam position feedback system prototype and preliminary results of initial beam tests at the Accelerator Test Facility (ATF) at KEK. The feedback system incorporates a fast analogue beam position monitor (BPM) front-end signal processor, a digital feedback board, and a kicker driver amplifier. The short bunchtrain, comprising 3 electron bunches separated by c. 150ns, in the ATF extraction line was used to test components of the prototype feedback system
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Use of neural networks in the analysis of complex systems
International Nuclear Information System (INIS)
Uhrig, R.E.
1992-01-01
The application of neural networks, alone or in conjunction with other advanced technologies (expert systems, fuzzy logic, and/or genetic algorithms) to some of the problems of complex engineering systems has the potential to enhance the safety reliability and operability of these systems. The work described here deals with complex systems or parts of such systems that can be isolated from the total system. Typically, the measured variables from the systems are analog variables that must be sampled and normalized to expected peak values before they are introduced into neural networks. Often data must be processed to put it into a form more acceptable to the neural network. The neural networks are usually simulated on modern high-speed computers that carry out the calculations serially. However, it is possible to implement neural networks using specially designed microchips where the network calculations are truly carried out in parallel, thereby providing virtually instantaneous outputs for each set of inputs. Specific applications described include: Diagnostics: State of the Plant; Hybrid System for Transient Identification; Detection of Change of Mode in Complex Systems; Sensor Validation; Plant-Wide Monitoring; Monitoring of Performance and Efficiency; and Analysis of Vibrations. Although the specific examples described deal with nuclear power plants or their subsystems, the techniques described can be applied to a wide variety of complex engineering systems
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Non-linear feedback neural networks VLSI implementations and applications
Ansari, Mohd Samar
2014-01-01
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.
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The neural and behavioral correlates of social evaluation in childhood
Directory of Open Access Journals (Sweden)
Michelle Achterberg
2017-04-01
Full Text Available Being accepted or rejected by peers is highly salient for developing social relations in childhood. We investigated the behavioral and neural correlates of social feedback and subsequent aggression in 7–10-year-old children, using the Social Network Aggression Task (SNAT. Participants viewed pictures of peers that gave positive, neutral or negative feedback to the participant’s profile. Next, participants could blast a loud noise towards the peer, as an index of aggression. We included three groups (N = 19, N = 28 and N = 27 and combined the results meta-analytically. Negative social feedback resulted in the most behavioral aggression, with large combined effect-sizes. Whole brain condition effects for each separate sample failed to show robust effects, possibly due to the small samples. Exploratory analyses over the combined test and replication samples confirmed heightened activation in the medial prefrontal cortex (mPFC after negative social feedback. Moreover, meta-analyses of activity in predefined regions of interest showed that negative social feedback resulted in more neural activation in the amygdala, anterior insula and the mPFC/anterior cingulate cortex. Together, the results show that social motivation is already highly salient in middle childhood, and indicate that the SNAT is a valid paradigm for assessing the neural and behavioral correlates of social evaluation in children.
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Microfluidic systems for stem cell-based neural tissue engineering.
Karimi, Mahdi; Bahrami, Sajad; Mirshekari, Hamed; Basri, Seyed Masoud Moosavi; Nik, Amirala Bakhshian; Aref, Amir R; Akbari, Mohsen; Hamblin, Michael R
2016-07-05
Neural tissue engineering aims at developing novel approaches for the treatment of diseases of the nervous system, by providing a permissive environment for the growth and differentiation of neural cells. Three-dimensional (3D) cell culture systems provide a closer biomimetic environment, and promote better cell differentiation and improved cell function, than could be achieved by conventional two-dimensional (2D) culture systems. With the recent advances in the discovery and introduction of different types of stem cells for tissue engineering, microfluidic platforms have provided an improved microenvironment for the 3D-culture of stem cells. Microfluidic systems can provide more precise control over the spatiotemporal distribution of chemical and physical cues at the cellular level compared to traditional systems. Various microsystems have been designed and fabricated for the purpose of neural tissue engineering. Enhanced neural migration and differentiation, and monitoring of these processes, as well as understanding the behavior of stem cells and their microenvironment have been obtained through application of different microfluidic-based stem cell culture and tissue engineering techniques. As the technology advances it may be possible to construct a "brain-on-a-chip". In this review, we describe the basics of stem cells and tissue engineering as well as microfluidics-based tissue engineering approaches. We review recent testing of various microfluidic approaches for stem cell-based neural tissue engineering.
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van Schie, Charlotte C; Chiu, Chui-De; Rombouts, Serge A R B; Heiser, Willem J; Elzinga, Bernet M
2018-01-01
Abstract The way we view ourselves may play an important role in our responses to interpersonal interactions. In this study, we investigate how feedback valence, consistency of feedback with self-knowledge and global self-esteem influence affective and neural responses to social feedback. Participants (N = 46) with a high range of self-esteem levels performed the social feedback task in an MRI scanner. Negative, intermediate and positive feedback was provided, supposedly by another person based on a personal interview. Participants rated their mood and applicability of feedback to the self. Analyses on trial basis on neural and affective responses are used to incorporate applicability of individual feedback words. Lower self-esteem related to low mood especially after receiving non-applicable negative feedback. Higher self-esteem related to increased posterior cingulate cortex and precuneus activation (i.e. self-referential processing) for applicable negative feedback. Lower self-esteem related to decreased medial prefrontal cortex, insula, anterior cingulate cortex and posterior cingulate cortex activation (i.e. self-referential processing) during positive feedback and decreased temporoparietal junction activation (i.e. other referential processing) for applicable positive feedback. Self-esteem and consistency of feedback with self-knowledge appear to guide our affective and neural responses to social feedback. This may be highly relevant for the interpersonal problems that individuals face with low self-esteem and negative self-views. PMID:29490088
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Thermal photovoltaic solar integrated system analysis using neural networks
Energy Technology Data Exchange (ETDEWEB)
Ashhab, S. [Hashemite Univ., Zarqa (Jordan). Dept. of Mechanical Engineering
2007-07-01
The energy demand in Jordan is primarily met by petroleum products. As such, the development of renewable energy systems is quite attractive. In particular, solar energy is a promising renewable energy source in Jordan and has been used for food canning, paper production, air-conditioning and sterilization. Artificial neural networks (ANNs) have received significant attention due to their capabilities in forecasting, modelling of complex nonlinear systems and control. ANNs have been used for forecasting solar energy. This paper presented a study that examined a thermal photovoltaic solar integrated system that was built in Jordan. Historical input-output system data that was collected experimentally was used to train an ANN that predicted the collector, PV module, pump and total efficiencies. The model predicted the efficiencies well and can therefore be utilized to find the operating conditions of the system that will produce the maximum system efficiencies. The paper provided a description of the photovoltaic solar system including equations for PV module efficiency; pump efficiency; and total efficiency. The paper also presented data relevant to the system performance and neural networks. The results of a neural net model were also presented based on the thermal PV solar integrated system data that was collected. It was concluded that the neural net model of the thermal photovoltaic solar integrated system set the background for achieving the best system performance. 10 refs., 6 figs.
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DEFF Research Database (Denmark)
Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin
2013-01-01
such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...... allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show...
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Commissioning of FPGA-based Transverse and Longitudinal Bunch-by-Bunch Feedback System for the TLS
International Nuclear Information System (INIS)
Hu, K. H.; Kuo, C. H.; Lau, W. K.; Yeh, M. S.; Hsu, S. Y.; Chou, P. J.; Wang, M. H.; Lee, Demi; Chen, Jenny; Wang, C. J.; Hsu, K. T.; Kobayashi, K.; Nakamura, T.; Dehler, M.
2006-01-01
Multi-bunch instabilities deteriorate beam quality, increasing beam emittance, or even causing beam loss in the synchrotron light source. The feedback system is essential to suppress multi-bunch instabilities caused by the impedances of beam ducts, and trapped ions. A new FPGA based transverse and longitudinal bunch-by-bunch feedback system have been commissioned at the Taiwan Light Source recently, A single feedback loop is used to simultaneously suppress the horizontal and the vertical multi-bunch instabilities. Longitudinal instabilities caused by cavity-like structures are suppressed by the longitudinal feedback loop. The same FPGA processor is employed in the transverse feedback and the longitudinal feedback system respectively. Diagnostic memory is included in the system to capture the bunch oscillation signal, which supports various studies
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A multipoint feedback control system for scanned focussed ultrasound hyperthermia
International Nuclear Information System (INIS)
Johnson, C.; Kress, R.; Roemer, R.; Hynynen, K.
1987-01-01
A multipoint feedback control system has been developed and tested for use with a scanned focussed ultrasound hyperthermia system. Extensive in-vivo tests (using a perfused organ model) have been made to evaluate the basic performance characteristics of the feedback control scheme for control of temperature in perfused media. The results of these tests are presented and compared with the predictions of a simulation routine. The control scheme was also tested in vivo using dogs' thighs and kidneys. Thigh experiments show the control scheme responds well to the affects of vasodilation and is able to maintain the targeted temperatures. In kidney experiments, where the rate of perfusion was controllable, the power adjusting algorithm successfully maintained uniform temperature distributions across regions of varying rates of perfusion. As a conclusion, the results show that this multipoint feedback controller scheme induces uniform temperature distributions when used with scanned focussed ultrasound systems
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Evaluating neural networks and artificial intelligence systems
Alberts, David S.
1994-02-01
Systems have no intrinsic value in and of themselves, but rather derive value from the contributions they make to the missions, decisions, and tasks they are intended to support. The estimation of the cost-effectiveness of systems is a prerequisite for rational planning, budgeting, and investment documents. Neural network and expert system applications, although similar in their incorporation of a significant amount of decision-making capability, differ from each other in ways that affect the manner in which they can be evaluated. Both these types of systems are, by definition, evolutionary systems, which also impacts their evaluation. This paper discusses key aspects of neural network and expert system applications and their impact on the evaluation process. A practical approach or methodology for evaluating a certain class of expert systems that are particularly difficult to measure using traditional evaluation approaches is presented.
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Simulations of the TESLA Linear Collider with a Fast Feedback System
Schulte, Daniel; White, G
2003-01-01
The tolerances on the beams as they collide at the interaction point of the TESLA linear collider are very tight due to the nano-metre scale final vertical bunch spot sizes. Ground motion causes the beams to increase in emittance and drift out of collision leading to dramatic degradation of luminosity performance. To combat this, both slow orbit and fast intra-train feedback systems will be used. The design of these feedback systems depends critically on how component misalignment effects the beam throughout the whole accelerator. A simulation has been set up to study in detail the accelerator performance under such conditions by merging the codes of PLACET, MERLIN and GUINEA-PIG together with Simulink code to model feedback systems, all under a Matlab environment.
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Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model
Directory of Open Access Journals (Sweden)
Lorenzo Vannucci
2017-06-01
Full Text Available Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.
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Automatic Thermal Control System with Temperature Difference or Derivation Feedback
Directory of Open Access Journals (Sweden)
Darina Matiskova
2016-02-01
Full Text Available Automatic thermal control systems seem to be non-linear systems with thermal inertias and time delay. A controller is also non-linear because its information and power signals are limited. The application of methods that are available to on-linear systems together with computer simulation and mathematical modelling creates a possibility to acquire important information about the researched system. This paper provides a new look at the heated system model and also designs the structure of the thermal system with temperature derivation feedback. The designed system was simulated by using a special software in Turbo Pascal. Time responses of this system are compared to responses of a conventional thermal system. The thermal system with temperature derivation feedback provides better transients, better quality of regulation and better dynamical properties.
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Brain activation upon ideal-body media exposure and peer feedback in late adolescent girls.
van der Meulen, Mara; Veldhuis, Jolanda; Braams, Barbara R; Peters, Sabine; Konijn, Elly A; Crone, Eveline A
2017-08-01
Media's prevailing thin-body ideal plays a vital role in adolescent girls' body image development, but the co-occurring impact of peer feedback is understudied. The present study used functional magnetic resonance imaging (fMRI) to test media imagery and peer feedback combinations on neural activity related to thin-body ideals. Twenty-four healthy female late adolescents rated precategorized body sizes of bikini models (too thin or normal), directly followed by ostensible peer feedback (too thin or normal). Consistent with prior studies on social feedback processing, results showed increased brain activity in the dorsal medial prefrontal cortex (dmPFC)/anterior cingulate cortex (ACC) and bilateral insula in incongruent situations: when participants rated media models' body size as normal while peer feedback indicated the models as too thin (or vice versa). This effect was stronger for girls with lower self-esteem. A subsequent behavioral study (N = 34 female late adolescents, separate sample) demonstrated that participants changed behavior in the direction of the peer feedback: precategorized normal sized models were rated as too thin more often after receiving too thin peer feedback. This suggests that the neural responses upon peer feedback may influence subsequent choice. Our results show that media-by-peer interactions have pronounced effects on girls' body ideals.
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Uplink Contention-based CSI Feedback with Prioritized Layers for a Multi-Carrier System
DEFF Research Database (Denmark)
Kaneko, Megumi; Hayashi, Kazunori; Popovski, Petar
2012-01-01
, several works have considered contention-based CSI feedback in the UL control channel. We propose such a feedback scheme for a generic MC system, based on the idea of variable collision protection, where the probability that a feedback information experiences a collision depends on its importance......Optimized resource allocation of the Downlink (DL) in wireless systems utilizing Multi-Carrier (MC) transmission requires Channel State Information (CSI) feedback for each user/subchannel to the Base Station (BS), consuming a high amount of Uplink (UL) radio resources. To alleviate this problem...
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Bayesian feedback versus Markovian feedback in a two-level atom
International Nuclear Information System (INIS)
Wiseman, H.M.; Mancini, Stefano; Wang Jin
2002-01-01
We compare two different approaches to the control of the dynamics of a continuously monitored open quantum system. The first is Markovian feedback, as introduced in quantum optics by Wiseman and Milburn [Phys. Rev. Lett. 70, 548 (1993)]. The second is feedback based on an estimate of the system state, developed recently by Doherty and Jacobs [Phys. Rev. A 60, 2700 (1999)]. Here we choose to call it, for brevity, Bayesian feedback. For systems with nonlinear dynamics, we expect these two methods of feedback control to give markedly different results. The simplest possible nonlinear system is a driven and damped two-level atom, so we choose this as our model system. The monitoring is taken to be homodyne detection of the atomic fluorescence, and the control is by modulating the driving. The aim of the feedback in both cases is to stabilize the internal state of the atom as close as possible to an arbitrarily chosen pure state, in the presence of inefficient detection and other forms of decoherence. Our results (obtained without recourse to stochastic simulations) prove that Bayesian feedback is never inferior, and is usually superior, to Markovian feedback. However, it would be far more difficult to implement than Markovian feedback and it loses its superiority when obvious simplifying approximations are made. It is thus not clear which form of feedback would be better in the face of inevitable experimental imperfections
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Chen, Sheng; Hong, Xia; Khalaf, Emad F; Alsaadi, Fuad E; Harris, Chris J
2017-12-01
Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.
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Brain activation upon ideal-body media exposure and peer feedback in adolescent girls
Veldhuis, J.; van der Meulen, Mara; Braams, Barbara; Peters, Sabine; Konijn, E.A.; Crone, Eveline A.
2017-01-01
Media’s prevailing thin-body ideal plays a vital role in adolescent girls’ body image development, but the co-occurring impact of peer feedback is understudied. The present study used functional Magnetic Resonance Imaging (fMRI) to test media imagery and peer feedback combinations on neural activity
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Electrocortical reactivity to social feedback in youth: A pilot study of the Island Getaway task
Directory of Open Access Journals (Sweden)
Autumn Kujawa
2014-10-01
Full Text Available Peer relationships become a major concern in adolescence, yet event-related potential (ERP measures of reactivity to social feedback in adolescence are limited. In this pilot study, we tested a novel task to elicit reactivity to social feedback in youth. Participants (10–15 years old; 57.9% male; N = 19 played a game that involved exchanging personal information with peers, voting to remove players from the game, and receiving rejection and acceptance feedback from peers. Results indicated that participants modified their voting behavior in response to peer feedback, and rejection feedback was associated with a negativity in the ERP wave compared to acceptance (i.e., the feedback negativity, FN. The FN predicted behavioral patterns, such that participants who showed greater neural reactivity to social feedback were less likely to reject co-players. Preliminary analyses suggest that the task may be a useful measure of individual differences: adolescents higher in social anxiety symptoms were less likely to reject peers and showed an enhanced FN to rejection vs. acceptance feedback, and higher depressive symptoms predicted an increased FN to rejection specifically. Results suggest that the FN elicited by social feedback may be a useful, economical neural measure of social processing across development and in clinical research.
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International Nuclear Information System (INIS)
Wu Kaiping
2014-01-01
The construction of experience feedback system is an important part and breakthrough point of institutes' knowledge management. It is significant for institutes' design, management, development and innovation. This article introduces the concept of experience feedback for institutes. It also goes details of the content of experience feedback system construction for institutes, including the founding of experience feedback organizational mechanism, the development of experience feedback system, construction of knowledge database system, the construction of knowledge resources, and the appraisal of experience feedback's performance. Furthermore, the recognition and support of leaders, understanding and cooperation of relative departments, and corporation's culture of encouraging knowledge sharing, also are the important guarantees for the good effects of institutes' experience feedback work. (author)
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Robust synchronization of chaotic systems via feedback
Energy Technology Data Exchange (ETDEWEB)
Femat, Ricardo [IPICYT, San Luis Potosi (Mexico). Dept. de Matematicas Aplicadas; Solis-Perales, Gualberto [Universidad de Guadalajara, Centro Univ. de Ciencias Exactas e Ingenierias (Mexico). Div. de Electronica y Computacion
2008-07-01
This volume includes the results derived during last ten years about both suppression and synchronization of chaotic -continuous time- systems. Along this time, the concept was to study how the intrinsic properties of dynamical systems can be exploited to suppress and to synchronize the chaotic behaviour and what synchronization phenomena can be found under feedback interconnection. A compilation of these findings is described in this book. This book shows a perspective on synchronization of chaotic systems. (orig.)
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Methane Feedbacks to the Global Climate System in a Warmer World
Dean, Joshua F.; Middelburg, Jack J.; Röckmann, Thomas; Aerts, Rien; Blauw, Luke G.; Egger, Matthias; Jetten, Mike S. M.; de Jong, Anniek E. E.; Meisel, Ove H.; Rasigraf, Olivia; Slomp, Caroline P.; in't Zandt, Michiel H.; Dolman, A. J.
2018-03-01
Methane (CH4) is produced in many natural systems that are vulnerable to change under a warming climate, yet current CH4 budgets, as well as future shifts in CH4 emissions, have high uncertainties. Climate change has the potential to increase CH4 emissions from critical systems such as wetlands, marine and freshwater systems, permafrost, and methane hydrates, through shifts in temperature, hydrology, vegetation, landscape disturbance, and sea level rise. Increased CH4 emissions from these systems would in turn induce further climate change, resulting in a positive climate feedback. Here we synthesize biological, geochemical, and physically focused CH4 climate feedback literature, bringing together the key findings of these disciplines. We discuss environment-specific feedback processes, including the microbial, physical, and geochemical interlinkages and the timescales on which they operate, and present the current state of knowledge of CH4 climate feedbacks in the immediate and distant future. The important linkages between microbial activity and climate warming are discussed with the aim to better constrain the sensitivity of the CH4 cycle to future climate predictions. We determine that wetlands will form the majority of the CH4 climate feedback up to 2100. Beyond this timescale, CH4 emissions from marine and freshwater systems and permafrost environments could become more important. Significant CH4 emissions to the atmosphere from the dissociation of methane hydrates are not expected in the near future. Our key findings highlight the importance of quantifying whether CH4 consumption can counterbalance CH4 production under future climate scenarios.
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Practical Loop-Shaping Design of Feedback Control Systems
Kopasakis, George
2010-01-01
An improved methodology for designing feedback control systems has been developed based on systematically shaping the loop gain of the system to meet performance requirements such as stability margins, disturbance attenuation, and transient response, while taking into account the actuation system limitations such as actuation rates and range. Loop-shaping for controls design is not new, but past techniques do not directly address how to systematically design the controller to maximize its performance. As a result, classical feedback control systems are designed predominantly using ad hoc control design approaches such as proportional integral derivative (PID), normally satisfied when a workable solution is achieved, without a good understanding of how to maximize the effectiveness of the control design in terms of competing performance requirements, in relation to the limitations of the plant design. The conception of this improved methodology was motivated by challenges in designing control systems of the types needed for supersonic propulsion. But the methodology is generally applicable to any classical control-system design where the transfer function of the plant is known or can be evaluated. In the case of a supersonic aerospace vehicle, a major challenge is to design the system to attenuate anticipated external and internal disturbances, using such actuators as fuel injectors and valves, bypass doors, and ramps, all of which are subject to limitations in actuator response, rates, and ranges. Also, for supersonic vehicles, with long slim type of structures, coupling between the engine and the structural dynamics can produce undesirable effects that could adversely affect vehicle stability and ride quality. In order to design distributed controls that can suppress these potential adverse effects, within the full capabilities of the actuation system, it is important to employ a systematic control design methodology such as this that can maximize the
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Swing Damping for Helicopter Slung Load Systems using Delayed Feedback
DEFF Research Database (Denmark)
Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon
2009-01-01
of swing. The design of the delayed feedback controller is presented as an optimization problem which gives the possibility of an automated design process. Simulations and flight test verifications of the control system on two different autonomous helicopters are presented and it is shown how a significant......This paper presents the design and verification of a swing reducing controller for helicopter slung load systems using intentional delayed feedback. It is intended for augmenting a trajectory tracking helicopter controller and thereby improving the slung load handing capabilities for autonomous...... helicopters. The delayed feedback controller is added to actively reduce oscillations of the slung load by improving the damping of the slung load pendulum modes. Furthermore, it is intended for integration with a feedforward control scheme based on input shaping for concurrent avoidance and dampening...
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Decoherence control in open quantum systems via classical feedback
International Nuclear Information System (INIS)
Ganesan, Narayan; Tarn, Tzyh-Jong
2007-01-01
In this work we propose a strategy using techniques from systems theory to completely eliminate decoherence and also provide conditions under which it can be done. A construction employing an auxiliary system, the bait, which is instrumental to decoupling the system from the environment is presented. Our approach to decoherence control in contrast to other approaches in the literature involves the bilinear input affine model of quantum control system which lends itself to various techniques from classical control theory, but with nontrivial modifications to the quantum regime. The elegance of this approach yields interesting results on open loop decouplability and decoherence free subspaces. Additionally, the feedback control of decoherence may be related to disturbance decoupling for classical input affine systems, which entails careful application of the methods by avoiding all the quantum mechanical pitfalls. In the process of calculating a suitable feedback the system must be restructured due to its tensorial nature of interaction with the environment, which is unique to quantum systems. In the subsequent section we discuss a general information extraction scheme to gain knowledge of the state and the amount of decoherence based on indirect continuous measurement. The analysis of continuous measurement on a decohering quantum system has not been extensively studied before. Finally, a methodology to synthesize feedback parameters itself is given, that technology permitting, could be implemented for practical 2-qubit systems to perform decoherence free quantum computing. The results obtained are qualitatively different and superior to the ones obtained via master equations
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Introducing BF++: AC++ framework for cognitive bio-feedback systems design.
Bianchi, L; Babiloni, F; Cincotti, F; Salinari, S; Marciani, M G
2003-01-01
This paper addressed the issue of building-up a framework for the realization of several cognitive bio-feedback (CBF) systems. It minimizes the programming effort and maximizes the efficiency and the cross-platform portability so that it can be used with many platforms (either software or hardware). A generic CBF system was decomposed into six modules: acquisition, kernel, feedback rule, patient feedback, operator user interface and persistent storage. The way in which these modules interact was defined by immutable software interfaces in a way that allows to completely substitute a module without the need to modify the others. Three Brain Computer Interface engines were developed with less than 40 lines of C++ code each. They can also be used under virtually any platform that supports an ANSI C++ compiler. A framework for the implementation of a wide range of CBF systems was developed. Compared to the other approaches that are described in the literature, the proposed one is the most efficient, the most protable across different platforms, the most generic and the one that allows the realization of the cheapest final systems.
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Effects of error feedback on a nonlinear bistable system with stochastic resonance
International Nuclear Information System (INIS)
Li Jian-Long; Zhou Hui
2012-01-01
In this paper, we discuss the effects of error feedback on the output of a nonlinear bistable system with stochastic resonance. The bit error rate is employed to quantify the performance of the system. The theoretical analysis and the numerical simulation are presented. By investigating the performances of the nonlinear systems with different strengths of error feedback, we argue that the presented system may provide guidance for practical nonlinear signal processing
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Optimal feedback control of the forced van der Pol system
International Nuclear Information System (INIS)
Chagas, T.P.; Toledo, B.A.; Rempel, E.L.; Chian, A.C.-L.; Valdivia, J.A.
2012-01-01
A simple feedback control strategy for chaotic systems is investigated using the forced van der Pol system as an example. The strategy regards chaos control as an optimization problem, where the maximum magnitude Floquet multiplier of a target unstable periodic orbit (UPO) is used as a cost function that needs to be minimized. Thus, the method obtains the optimal control gain in terms of the stability of the target UPO. This strategy was recently proposed for the proportional feedback control (PFC) method. Here, it is extended to the highly popular delayed feedback control (DFC) method. Since the DFC method treats the system as a delay-differential equation whose phase space is infinite-dimensional, the characteristic multipliers are found through a truncation in the number of delayed states. Control of a target UPO is achieved for several values of the forcing amplitude. We compare the DFC and PFC methods in terms of stability of the controlled orbit, steady state error and control effort.
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Fast feedback system for energy and beam stabilization
International Nuclear Information System (INIS)
R. Dickson; V. Lebedev
1999-01-01
The electron beams being delivered to targets of the Continuous Electron Beam Accelerator Facility (CEBAF) at Thomas Jefferson National Accelerator Facility (Jefferson Lab) are plagued with undesirable positional and energy fluctuations. These fluctuations primarily occur at harmonics of the power line frequency (60, 120, 180, etc. hertz), and their cause is rooted in electromagnetic fields generated by accelerator electronic equipment. It is possible to largely nullify these deviations by applying real time corrections to electromagnets and RF verniers along the beam line. This concept has been successfully applied at Jefferson Lab by extensively modifying the existing Beam Position Monitor (BPM) system with the integration of an algorithm that computes correction signals targeted at the power line harmonics. Many of the modifications required were due to the existing CEBAF BPM system not having the data acquisition bandwidth needed for this type of feedback system. This paper will describe the techniques required to transform the CEBAF standard BPM system into a high speed practical fast feedback system that coexists with the large scale control system--the Experimental Physics and Industrial Control System (EPICS)--that runs the CEBAF accelerator in daily operation
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Hybrid energy system evaluation in water supply system energy production: neural network approach
Energy Technology Data Exchange (ETDEWEB)
Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)
2010-07-01
Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.
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Anomaly detection in an automated safeguards system using neural networks
International Nuclear Information System (INIS)
Whiteson, R.; Howell, J.A.
1992-01-01
An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs
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Directory of Open Access Journals (Sweden)
Zahra Bahramian
2017-01-01
Full Text Available Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning with k-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.
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Chaos control for the family of Roessler systems using feedback controllers
International Nuclear Information System (INIS)
Liao Xiaoxin; Yu Pei
2006-01-01
This paper presents a new method for controlling chaos in several classical chaotic Roessler systems using feedback control strategy. In particular, for an arbitrarily given equilibrium point of a Roessler system, we design explicit and simple feedback control laws by which the equilibrium point is globally and exponentially stabilized. Six typical Roessler systems are studied, and explicit formulas are derived for estimating the convergence rate of these systems. Numerical examples are presented to illustrate the theoretical results. A mistake has been found in the existing literature, and a correct result is given
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An analog integrated front-end amplifier for neural applications
Zhou, Zhijun; Warr, Paul
2017-01-01
The front-end amplifier forms the critical element for signal detection and pre-processing within neural monitoring systems. It determines not only the fidelity of the biosignal, but also impacts power consumption and detector size. In this paper, a combined feedback loop-controlled approach is proposed to neutralize for the input leakage currents generated by low noise amplifiers when in integrated circuit form, alongside signal leakage into the input bias network. Significantly, this loop t...
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Bunch-by-bunch longitudinal feedback system for PEP-II
International Nuclear Information System (INIS)
Oxoby, G.; Claus, R.; Fox, J.
1994-06-01
This paper describes the implementation of the bunch-by-bunch longitudinal feedback system for the PEP-II B Factory. Bunch spacing down to 2 ns is achieved using 500 Megasamples per second A/D and D/A converters, and AT ampersand T 1610 Digital Signal Processors are integrated to run a downsampled feedback algorithm for each bunch in parallel. This general purpose programmable system, packaged in VXI and VME, is modular and scalable to offer portability to other accelerator rings. The control and monitoring hardware and software architecture have been developed to provide ease of operation as well as diagnostic tools for machine physics
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Event-triggered output feedback control for distributed networked systems.
Mahmoud, Magdi S; Sabih, Muhammad; Elshafei, Moustafa
2016-01-01
This paper addresses the problem of output-feedback communication and control with event-triggered framework in the context of distributed networked control systems. The design problem of the event-triggered output-feedback control is proposed as a linear matrix inequality (LMI) feasibility problem. The scheme is developed for the distributed system where only partial states are available. In this scheme, a subsystem uses local observers and share its information to its neighbors only when the subsystem's local error exceeds a specified threshold. The developed method is illustrated by using a coupled cart example from the literature. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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Directory of Open Access Journals (Sweden)
M. Grus
2015-08-01
Full Text Available Since Topographical Key Register has become an open data the amount of users increased enormously. The highest grow was in the private users group. The increasing number of users and their growing demand for high actuality of the topographic data sets motivates the Dutch Kadaster to innovate and improve the Topographical Key Register (BRT. One of the initiatives was to provide a voluntary geographical information project aiming at providing a user-friendly feedback system adjusted to all kinds of user groups. The feedback system is a compulsory element of the Topographical Key Register in the Netherlands. The Dutch Kadaster is obliged to deliver a feedback system and the key-users are obliged to use it. The aim of the feedback system is to improve the quality and stimulate the usage of the data. The results of the pilot shows that the user-friendly and open to everyone feedback system contributes enormously to improve the quality of the topographic dataset.
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The pointer basis and the feedback stabilization of quantum systems
International Nuclear Information System (INIS)
Li, L; Chia, A; Wiseman, H M
2014-01-01
The dynamics for an open quantum system can be ‘unravelled’ in infinitely many ways, depending on how the environment is monitored, yielding different sorts of conditioned states, evolving stochastically. In the case of ideal monitoring these states are pure, and the set of states for a given monitoring forms a basis (which is overcomplete in general) for the system. It has been argued elsewhere (Atkins et al 2005 Europhys. Lett. 69 163) that the ‘pointer basis’ as introduced by Zurek et al (1993 Phys. Rev. Lett. 70 1187), should be identified with the unravelling-induced basis which decoheres most slowly. Here we show the applicability of this concept of pointer basis to the problem of state stabilization for quantum systems. In particular we prove that for linear Gaussian quantum systems, if the feedback control is assumed to be strong compared to the decoherence of the pointer basis, then the system can be stabilized in one of the pointer basis states with a fidelity close to one (the infidelity varies inversely with the control strength). Moreover, if the aim of the feedback is to maximize the fidelity of the unconditioned system state with a pure state that is one of its conditioned states, then the optimal unravelling for stabilizing the system in this way is that which induces the pointer basis for the conditioned states. We illustrate these results with a model system: quantum Brownian motion. We show that even if the feedback control strength is comparable to the decoherence, the optimal unravelling still induces a basis very close to the pointer basis. However if the feedback control is weak compared to the decoherence, this is not the case. (paper)
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Performance Analysis of Simple Channel Feedback Schemes for a Practical OFDMA System
DEFF Research Database (Denmark)
Pedersen, Klaus, I.; Kolding, Troels; Kovacs, Istvan
2009-01-01
In this paper, we evaluate the tradeoff between the amount of uplink channel feedback information and the orthogonal frequency-division multiple access (OFDMA) downlink performance with opportunistic frequency-domain packet scheduling. Three candidate channel feedback schemes are investigated......, including practical aspects, such as the effects of terminal measurement errors, bandwidth measurement granularity, quantization, and uplink signaling delays. The performance is evaluated by means of system-level simulations with detailed modeling of various radio resource-management algorithms, etc. Our...... results show that the optimal tradeoff between the channel feedback and the downlink OFDMA system performance depends on the radio channel frequency coherence bandwidth. We conclude that the so-called average best-M scheme is the most attractive channel feedback solution, where only the average channel...
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Feedback Loop Gains and Feedback Behavior (1996)
DEFF Research Database (Denmark)
Kampmann, Christian Erik
2012-01-01
Linking feedback loops and system behavior is part of the foundation of system dynamics, yet the lack of formal tools has so far prevented a systematic application of the concept, except for very simple systems. Having such tools at their disposal would be a great help to analysts in understanding...... large, complicated simulation models. The paper applies tools from graph theory formally linking individual feedback loop strengths to the system eigenvalues. The significance of a link or a loop gain and an eigenvalue can be expressed in the eigenvalue elasticity, i.e., the relative change...... of an eigenvalue resulting from a relative change in the gain. The elasticities of individual links and loops may be found through simple matrix operations on the linearized system. Even though the number of feedback loops can grow rapidly with system size, reaching astronomical proportions even for modest systems...
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The Vibe of Skating; Design and Testing of a Vibro-Tactile Feedback System
Directory of Open Access Journals (Sweden)
Arjen J. Jansen
2018-03-01
Full Text Available Providing athletes with real-time feedback on their performance is becoming common in many sports, also in speed skating. This research-by-design project aims at finding a tool that allows the speed skater to get real-time feedback on his performance. Speed skaters often mention a so-called “good feeling” when skating behind a better skater. It is the feeling nearly every speed skater is after when skating alone; skate with less power while maintaining the same speed and feeling of ease. A longer push-off phase at a constant cadence has proven to contribute to this ideal situation but is hard for the coach alone to influence this. Therefore, a system was designed that measures the skating cadence and challenges the skater to change his skating stroke by means of vibro-tactile feedback. Four subjects have tested the feedback system. From this test, we concluded that the system provides meaningful feedback towards changing the skating cycle.
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Feedback linearizing control of a MIMO power system
Ilyes, Laszlo
Prior research has demonstrated that either the mechanical or electrical subsystem of a synchronous electric generator may be controlled using single-input single-output (SISO) nonlinear feedback linearization. This research suggests a new approach which applies nonlinear feedback linearization to a multi-input multi-output (MIMO) model of the synchronous electric generator connected to an infinite bus load model. In this way, the electrical and mechanical subsystems may be linearized and simultaneously decoupled through the introduction of a pair of auxiliary inputs. This allows well known, linear, SISO control methods to be effectively applied to the resulting systems. The derivation of the feedback linearizing control law is presented in detail, including a discussion on the use of symbolic math processing as a development tool. The linearizing and decoupling properties of the control law are validated through simulation. And finally, the robustness of the control law is demonstrated.
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Hardware design and implementation of the closed-orbit feedback system at APS
International Nuclear Information System (INIS)
Barr, D.; Chung, Youngjoo.
1996-01-01
The Advanced Photon Source (APS) storage ring will utilize a closed-orbit feedback system in order to produce a more stable beam. The specified orbit measurement resolution is 25 microns for global feedback and 1 micron for local feedback. The system will sample at 4 kHz and provide a correction bandwidth of 100 Hz. At this bandwidth, standard rf BPMs will provide a resolution of 0.7 micron, while specialized miniature BPMs positioned on either side of the insertion devices for local feedback will provide a resolution of 0.2 micron (1). The measured BPM noise floor for standard BPMs is 0.06 micron per root hertz mA. Such a system has been designed, simulated, and tested on a small scale (2). This paper covers the actual hardware design and layout of the entire closed-loop system. This includes commercial hardware components, in addition to many components designed and built in-house. The paper will investigate the large-scale workings of all these devices, as well as an overall view of each piece of hardware used
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Experimental Demonstrations of Optical Neural Computers
Hsu, Ken; Brady, David; Psaltis, Demetri
1988-01-01
We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography.
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Mining Feedback in Ranking and Recommendation Systems
Zhuang, Ziming
2009-01-01
The amount of online information has grown exponentially over the past few decades, and users become more and more dependent on ranking and recommendation systems to address their information seeking needs. The advance in information technologies has enabled users to provide feedback on the utilities of the underlying ranking and recommendation…
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Identification of Complex Dynamical Systems with Neural Networks (2/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
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Identification of Complex Dynamical Systems with Neural Networks (1/2)
CERN. Geneva
2016-01-01
The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...
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Energy Technology Data Exchange (ETDEWEB)
Gentile, Ann C.; Brandt, James M.; Tucker, Thomas (Open Grid Computing, Inc., Austin, TX); Thompson, David
2011-09-01
This report provides documentation for the completion of the Sandia Level II milestone 'Develop feedback system for intelligent dynamic resource allocation to improve application performance'. This milestone demonstrates the use of a scalable data collection analysis and feedback system that enables insight into how an application is utilizing the hardware resources of a high performance computing (HPC) platform in a lightweight fashion. Further we demonstrate utilizing the same mechanisms used for transporting data for remote analysis and visualization to provide low latency run-time feedback to applications. The ultimate goal of this body of work is performance optimization in the face of the ever increasing size and complexity of HPC systems.
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Advantages of externally powered prosthesis with feedback system using pseudo-cineplasty.
Nambu, Seiji; Ikebuchi, Mitsuhiko; Taniguchi, Masashi; Park, Choong Sik; Kitagawa, Takahiro; Nakajima, Shigeyoshi; Koike, Tatsuya
2014-01-01
Externally powered upper-limb prostheses are difficult to use because of the lack of sensory feedback. Neuroprostheses have recently been developed for people with upper-limb amputation but are complicated, expensive, and still developing. We therefore designed a simple system by combining pseudo-cineplasty with extended physiological proprioception to provide sensory feedback to the body. We penetrated the palmaris longus tendon percutaneously with a metal ring, similar to that used in body piercing, in a nondisabled subject as a pseudo-cineplasty. The tendon and ring were connected to the system, and a sensory feedback experiment was performed. We investigated the ability of the user to determine the size of an object grasped by the prosthetic hand without visual information. The subject could distinguish between large and small objects with 100% accuracy and between small, medium, and large objects with 80% accuracy. In pseudo-cineplasty, control and sensory feedback are natural because the prosthetic hand is controlled by muscle contraction. Tension transmitted from the prosthetic hand is sensed via muscle spindles and skin sensors. This technique allows only partial sensory feedback but appears to offer several advantages over other human-machine interfaces.
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Neural computing thermal comfort index for HVAC systems
International Nuclear Information System (INIS)
Atthajariyakul, S.; Leephakpreeda, T.
2005-01-01
The primary purpose of a heating, ventilating and air conditioning (HVAC) system within a building is to make occupants comfortable. Without real time determination of human thermal comfort, it is not feasible for the HVAC system to yield controlled conditions of the air for human comfort all the time. This paper presents a practical approach to determine human thermal comfort quantitatively via neural computing. The neural network model allows real time determination of the thermal comfort index, where it is not practical to compute the conventional predicted mean vote (PMV) index itself in real time. The feed forward neural network model is proposed as an explicit function of the relation of the PMV index to accessible variables, i.e. the air temperature, wet bulb temperature, globe temperature, air velocity, clothing insulation and human activity. An experiment in an air conditioned office room was done to demonstrate the effectiveness of the proposed methodology. The results show good agreement between the thermal comfort index calculated from the neural network model in real time and those calculated from the conventional PMV model
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Banis, Stella; Geerligs, Linda; Lorist, Monicque M.; Banis, Hendrika
2014-01-01
Sex-specific prevalence rates in mental and physical disorders may be partly explained by sex differences in physiological stress responses. Neural networks that might be involved are those underlying feedback processing. Aim of the present EEG study was to investigate whether acute stress alters
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DEFF Research Database (Denmark)
Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin
2015-01-01
correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...
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A New Controller to Enhance PV System Performance Based on Neural Network
Directory of Open Access Journals (Sweden)
Roshdy A AbdelRassoul
2017-06-01
Full Text Available In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.In recent years, a radical increase of photovoltaic (PV power generators installation took place because of increased efficiency of solar cells, as well as the growth of manufacturing technology of solar panels. This paper shows the operation and modeling of photovoltaic systems, particularly designing neural controller to control the system. Neural controller is optimized using particle swarm optimization (PSO leads to getting the best performance of the designed PV system. Using neural network the maximum overshoot and rise time obtained become 0.00001% and 0.1798 seconds, respectively also this paper introduce a comparison between some kind of controller for PV system.
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Synchronization of spatiotemporal chaotic systems by feedback control
International Nuclear Information System (INIS)
Lai, Y.; Grebogi, C.
1994-01-01
We demonstrate that two identical spatiotemporal chaotic systems can be synchronized by (1) linking one or a few of their dynamical variables, and (2) applying a small feedback control to one of the systems. Numerical examples using the diffusively coupled logistic map lattice are given. The effect of noise and the limitation of the technique are discussed
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Computational neural network regression model for Host based Intrusion Detection System
Directory of Open Access Journals (Sweden)
Sunil Kumar Gautam
2016-09-01
Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.
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Synchronization of coupled nonidentical multidelay feedback systems
International Nuclear Information System (INIS)
Hoang, Thang Manh; Nakagawa, Masahiro
2007-01-01
We present the lag synchronization of coupled nonidentical multidelay feedback systems, in which the synchronization signal is the sum of nonlinearly transformed components of delayed state variable. The sufficient condition for synchronization is considered by the Krasovskii-Lyapunov theory. The specific examples will demonstrate and verify the effectiveness of the proposed model
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Adaptive neural network/expert system that learns fault diagnosis for different structures
Simon, Solomon H.
1992-08-01
Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.
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Commissioning of the iGp Feedback System at DAΦNE
International Nuclear Information System (INIS)
Drago, A.; Fox, J.D.; Teytelman, D.; Tobiyama, M.
2011-01-01
The iGp (Integrated Gigasample Processor) is an innovative digital bunch-by-bunch feedback system developed by a KEK / SLAC / INFN-LNF joint collaboration. The processing unit can sample at 500 MHz and compute the bunch-by-bunch output signal for up to ∼5000 bunches. The feedback gateware code is implemented inside just one FPGA (Field Programmable Gate Array) chip, a Xilinx Virtex-II. The FPGA implements two banks of 16-tap FIR (Finite Impulse Response) filters. Each filter is realtime programmable through the operator interface. At DAΦNE, the Frascati Φ-Factory, two iGp units have been commissioned in the April 2007. The iGp systems have substituted the previous betatron feedback systems. This insertion has been very fast and has shown no problems involving just a substitution of the old, less flexible, digital systems, letting unchanged the baseband analog frontend and backend. The commissioning has been very simple, due to the complete and powerful EPICS operator interface, working well in local and remote operations. The software includes also tools for analyzing post processor data. A description of the commissioning with the operations done is reported.
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Commissioning of the APS real-time orbit feedback system
International Nuclear Information System (INIS)
Carwardine, J.; Decker, G.; Evans, K. Jr.; Hillman, A.; Lenkszus, F.; Merl, R.; Pietryla, A.
1997-01-01
A unified global and local closed-orbit feedback system has been implemented at the Advanced Photon Source in order to stabilize both particle and photon beams. Beam stability requirements in the band up to 50 Hz are 17 microm in the horizontal plane and 4.4 microm vertically. Orbit feedback algorithms are implemented digitally using multiple digital signal processors, with computing power distributed in 20 VME crates around the storage ring. Each crate communicates with all others via a fast reflective memory network. The system has access to 320 rf beam position monitors together with x-ray beam position monitors in both insertion device and bending magnet beamlines. Up to 317 corrector magnets are available to the system. The global system reduces horizontal rms beam motion at the x-ray source points by more than a factor of two in the frequency band from 10 mHz to 50 Hz
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Investigation of control system of traction electric drive with feedbacks on load
Kuznetsov, N. K.; Iov, I. A.; Iov, A. A.
2018-03-01
In the article, by the example of a walking excavator, the results of a study of a control system of traction electric drive with a rigid and flexible feedback on the load are mentioned. Based on the analysis of known works, the calculation scheme has been chosen; the equations of motion of the electromechanical system have been obtained, taking into account the elasticity of the rope and feedbacks on the load in the elastic element. A simulation model of this system has been developed and mathematical modeling of the transient processes to evaluate the influence of feedback on the dynamic characteristics of the mechanism and its efficiency of work was carried out. It is shown that the use of rigid and flexible feedbacks makes it possible to reduce dynamic loads in the traction mechanism and to limit the elastic oscillation of the executive mechanism in transient operating modes in comparison with the standard control system; however, there is some decrease in productivity. It has been also established that the sign-variable of the loading of the electric drive, connected with the opening of the backlashes in the gearbox due to the action of feedbacks on the load in the elastic element, under certain conditions, can lead to undesirable phenomena in the operation of the drive and a decrease in the reliability of its operation.
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Study on real-time force feedback for a master-slave interventional surgical robotic system.
Guo, Shuxiang; Wang, Yuan; Xiao, Nan; Li, Youxiang; Jiang, Yuhua
2018-04-13
In robot-assisted catheterization, haptic feedback is important, but is currently lacking. In addition, conventional interventional surgical robotic systems typically employ a master-slave architecture with an open-loop force feedback, which results in inaccurate control. We develop herein a novel real-time master-slave (RTMS) interventional surgical robotic system with a closed-loop force feedback that allows a surgeon to sense the true force during remote operation, provide adequate haptic feedback, and improve control accuracy in robot-assisted catheterization. As part of this system, we also design a unique master control handle that measures the true force felt by a surgeon, providing the basis for the closed-loop control of the entire system. We use theoretical and empirical methods to demonstrate that the proposed RTMS system provides a surgeon (using the master control handle) with a more accurate and realistic force sensation, which subsequently improves the precision of the master-slave manipulation. The experimental results show a substantial increase in the control accuracy of the force feedback and an increase in operational efficiency during surgery.
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Feedback control policies employed by people using intracortical brain-computer interfaces
Willett, Francis R.; Pandarinath, Chethan; Jarosiewicz, Beata; Murphy, Brian A.; Memberg, William D.; Blabe, Christine H.; Saab, Jad; Walter, Benjamin L.; Sweet, Jennifer A.; Miller, Jonathan P.; Henderson, Jaimie M.; Shenoy, Krishna V.; Simeral, John D.; Hochberg, Leigh R.; Kirsch, Robert F.; Bolu Ajiboye, A.
2017-02-01
Objective. When using an intracortical BCI (iBCI), users modulate their neural population activity to move an effector towards a target, stop accurately, and correct for movement errors. We call the rules that govern this modulation a ‘feedback control policy’. A better understanding of these policies may inform the design of higher-performing neural decoders. Approach. We studied how three participants in the BrainGate2 pilot clinical trial used an iBCI to control a cursor in a 2D target acquisition task. Participants used a velocity decoder with exponential smoothing dynamics. Through offline analyses, we characterized the users’ feedback control policies by modeling their neural activity as a function of cursor state and target position. We also tested whether users could adapt their policy to different decoder dynamics by varying the gain (speed scaling) and temporal smoothing parameters of the iBCI. Main results. We demonstrate that control policy assumptions made in previous studies do not fully describe the policies of our participants. To account for these discrepancies, we propose a new model that captures (1) how the user’s neural population activity gradually declines as the cursor approaches the target from afar, then decreases more sharply as the cursor comes into contact with the target, (2) how the user makes constant feedback corrections even when the cursor is on top of the target, and (3) how the user actively accounts for the cursor’s current velocity to avoid overshooting the target. Further, we show that users can adapt their control policy to decoder dynamics by attenuating neural modulation when the cursor gain is high and by damping the cursor velocity more strongly when the smoothing dynamics are high. Significance. Our control policy model may help to build better decoders, understand how neural activity varies during active iBCI control, and produce better simulations of closed-loop iBCI movements.
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Variance decomposition shows the importance of human-climate feedbacks in the Earth system
Calvin, K. V.; Bond-Lamberty, B. P.; Jones, A. D.; Shi, X.; Di Vittorio, A. V.; Thornton, P. E.
2017-12-01
The human and Earth systems are intricately linked: climate influences agricultural production, renewable energy potential, and water availability, for example, while anthropogenic emissions from industry and land use change alter temperature and precipitation. Such feedbacks have the potential to significantly alter future climate change. Current climate change projections contain significant uncertainties, however, and because Earth System Models do not generally include dynamic human (demography, economy, energy, water, land use) components, little is known about how climate feedbacks contribute to that uncertainty. Here we use variance decomposition of a novel coupled human-earth system model to show that the influence of human-climate feedbacks can be as large as 17% of the total variance in the near term for global mean temperature rise, and 11% in the long term for cropland area. The near-term contribution of energy and land use feedbacks to the climate on global mean temperature rise is as large as that from model internal variability, a factor typically considered in modeling studies. Conversely, the contribution of climate feedbacks to cropland extent, while non-negligible, is less than that from socioeconomics, policy, or model. Previous assessments have largely excluded these feedbacks, with the climate community focusing on uncertainty due to internal variability, scenario, and model and the integrated assessment community focusing on uncertainty due to socioeconomics, technology, policy, and model. Our results set the stage for a new generation of models and hypothesis testing to determine when and how bidirectional feedbacks between human and Earth systems should be considered in future assessments of climate change.
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Stability and Bifurcation in Magnetic Flux Feedback Maglev Control System
Directory of Open Access Journals (Sweden)
Wen-Qing Zhang
2013-01-01
Full Text Available Nonlinear properties of magnetic flux feedback control system have been investigated mainly in this paper. We analyzed the influence of magnetic flux feedback control system on control property by time delay and interfering signal of acceleration. First of all, we have established maglev nonlinear model based on magnetic flux feedback and then discussed hopf bifurcation’s condition caused by the acceleration’s time delay. The critical value of delayed time is obtained. It is proved that the period solution exists in maglev control system and the stable condition has been got. We obtained the characteristic values by employing center manifold reduction theory and normal form method, which represent separately the direction of hopf bifurcation, the stability of the period solution, and the period of the period motion. Subsequently, we discussed the influence maglev system on stability of by acceleration’s interfering signal and obtained the stable domain of interfering signal. Some experiments have been done on CMS04 maglev vehicle of National University of Defense Technology (NUDT in Tangshan city. The results of experiments demonstrate that viewpoints of this paper are correct and scientific. When time lag reaches the critical value, maglev system will produce a supercritical hopf bifurcation which may cause unstable period motion.
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Stability of Nonlinear Systems with Unknown Time-varying Feedback Delay
Chunodkar, Apurva A.; Akella, Maruthi R.
2013-12-01
This paper considers the problem of stabilizing a class of nonlinear systems with unknown bounded delayed feedback wherein the time-varying delay is 1) piecewise constant 2) continuous with a bounded rate. We also consider application of these results to the stabilization of rigid-body attitude dynamics. In the first case, the time-delay in feedback is modeled specifically as a switch among an arbitrarily large set of unknown constant values with a known strict upper bound. The feedback is a linear function of the delayed states. In the case of linear systems with switched delay feedback, a new sufficiency condition for average dwell time result is presented using a complete type Lyapunov-Krasovskii (L-K) functional approach. Further, the corresponding switched system with nonlinear perturbations is proven to be exponentially stable inside a well characterized region of attraction for an appropriately chosen average dwell time. In the second case, the concept of the complete type L-K functional is extended to a class of nonlinear time-delay systems with unknown time-varying time-delay. This extension ensures stability robustness to time-delay in the control design for all values of time-delay less than the known upper bound. Model-transformation is used in order to partition the nonlinear system into a nominal linear part that is exponentially stable with a bounded perturbation. We obtain sufficient conditions which ensure exponential stability inside a region of attraction estimate. A constructive method to evaluate the sufficient conditions is presented together with comparison with the corresponding constant and piecewise constant delay. Numerical simulations are performed to illustrate the theoretical results of this paper.
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Neural neworks in a management information systems
Jana Weinlichová; Michael Štencl
2009-01-01
For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...
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Feedback enhances feedforward figure-ground segmentation by changing firing mode.
Supèr, Hans; Romeo, August
2011-01-01
In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.
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Feedback enhances feedforward figure-ground segmentation by changing firing mode.
Directory of Open Access Journals (Sweden)
Hans Supèr
Full Text Available In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforward spiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses with the responses to a homogenous texture. We propose that feedback controls figure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons.
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Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode
Supèr, Hans; Romeo, August
2011-01-01
In the visual cortex, feedback projections are conjectured to be crucial in figure-ground segregation. However, the precise function of feedback herein is unclear. Here we tested a hypothetical model of reentrant feedback. We used a previous developed 2-layered feedforwardspiking network that is able to segregate figure from ground and included feedback connections. Our computer model data show that without feedback, neurons respond with regular low-frequency (∼9 Hz) bursting to a figure-ground stimulus. After including feedback the firing pattern changed into a regular (tonic) spiking pattern. In this state, we found an extra enhancement of figure responses and a further suppression of background responses resulting in a stronger figure-ground signal. Such push-pull effect was confirmed by comparing the figure-ground responses withthe responses to a homogenous texture. We propose that feedback controlsfigure-ground segregation by influencing the neural firing patterns of feedforward projecting neurons. PMID:21738747
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Controlling the dynamics of multi-state neural networks
International Nuclear Information System (INIS)
Jin, Tao; Zhao, Hong
2008-01-01
In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks
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Control system of hexacopter using color histogram footprint and convolutional neural network
Ruliputra, R. N.; Darma, S.
2017-07-01
The development of unmanned aerial vehicles (UAV) has been growing rapidly in recent years. The use of logic thinking which is implemented into the program algorithms is needed to make a smart system. By using visual input from a camera, UAV is able to fly autonomously by detecting a target. However, some weaknesses arose as usage in the outdoor environment might change the target's color intensity. Color histogram footprint overcomes the problem because it divides color intensity into separate bins that make the detection tolerant to the slight change of color intensity. Template matching compare its detection result with a template of the reference image to determine the target position and use it to position the vehicle in the middle of the target with visual feedback control based on Proportional-Integral-Derivative (PID) controller. Color histogram footprint method localizes the target by calculating the back projection of its histogram. It has an average success rate of 77 % from a distance of 1 meter. It can position itself in the middle of the target by using visual feedback control with an average positioning time of 73 seconds. After the hexacopter is in the middle of the target, Convolutional Neural Networks (CNN) classifies a number contained in the target image to determine a task depending on the classified number, either landing, yawing, or return to launch. The recognition result shows an optimum success rate of 99.2 %.
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Vein matching using artificial neural network in vein authentication systems
Noori Hoshyar, Azadeh; Sulaiman, Riza
2011-10-01
Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.
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Utility of an app-based system to improve feedback following workplace-based assessment.
Lefroy, Janet; Roberts, Nicola; Molyneux, Adrian; Bartlett, Maggie; Gay, Simon; McKinley, Robert
2017-05-31
To determine whether an app-based software system to support production and storage of assessment feedback summaries makes workplace-based assessment easier for clinical tutors and enhances the educational impact on medical students. We monitored our workplace assessor app's usage by Year 3 to 5 medical students in 2014-15 and conducted focus groups with Year 4 medical students and interviews with clinical tutors who had used the apps. Analysis was by constant comparison using a framework based on elements of van der Vleuten's utility index. The app may enhance the content of feedback for students. Using a screen may be distracting if the app is used during feedback discussions. Educational impact was reduced by students' perceptions that an easy-to-produce feedback summary is less valuable than one requiring more tutor time and effort. Tutors' typing, dictation skills and their familiarity with mobile devices varied. This influenced their willingness to use the assessment and feedback mobile app rather than the equivalent web app. Electronic feedback summaries had more real and perceived uses than anticipated both for tutors and students including perceptions that they were for the school rather than the student. Electronic workplace-based assessment systems can be acceptable to tutors and can make giving detailed written feedback more practical but can interrupt the social interaction required for the feedback conversation. Tutor training and flexible systems will be required to minimise unwanted consequences. The educational impact on both tutors and students of providing pre-formulated advice within the app is worth further study.
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Adaptive Synchronization of Memristor-based Chaotic Neural Systems
Directory of Open Access Journals (Sweden)
Xiaofang Hu
2014-11-01
Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.
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Spike Bursts from an Excitable Optical System
Rios Leite, Jose R.; Rosero, Edison J.; Barbosa, Wendson A. S.; Tredicce, Jorge R.
Diode Lasers with double optical feedback are shown to present power drop spikes with statistical distribution controllable by the ratio of the two feedback times. The average time between spikes and the variance within long time series are studied. The system is shown to be excitable and present bursting of spikes created with specific feedback time ratios and strength. A rate equation model, extending the Lang-Kobayashi single feedback for semiconductor lasers proves to match the experimental observations. Potential applications to construct network to mimic neural systems having controlled bursting properties in each unit will be discussed. Brazilian Agency CNPQ.
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Perceived stress predicts altered reward and loss feedback processing in medial prefrontal cortex
Directory of Open Access Journals (Sweden)
Michael T Treadway
2013-05-01
Full Text Available Stress is significant risk factor for the development of psychopathology, particularly symptoms related to reward processing. Importantly, individuals display marked variation in how they perceive and cope with stressful events, and such differences are strongly linked to risk for developing psychiatric symptoms following stress exposure. However, many questions remain regarding the neural architecture that underlies inter-subject variability in perceptions of stressors. Using functional magnetic resonance imaging (fMRI during a monetary incentive delay paradigm, we examined the effects of self-reported perceived stress levels on neural activity during reward anticipation and feedback in a sample of healthy individuals. We found that subjects reporting more uncontrollable and overwhelming stressors displayed blunted neural responses in medial prefrontal cortex (mPFC following feedback related to monetary gains as well monetary losses. This is consistent with preclinical models that implicate the mPFC as a key site of vulnerability to the noxious effects of uncontrollable stressors. Our data help translate these findings to humans, and elucidate some of the neural mechanisms that may underlie stress-linked risk for developing reward-related psychiatric symptoms.
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A switched state feedback law for the stabilization of LTI systems.
Energy Technology Data Exchange (ETDEWEB)
Santarelli, Keith R.
2009-09-01
Inspired by prior work in the design of switched feedback controllers for second order systems, we develop a switched state feedback control law for the stabilization of LTI systems of arbitrary dimension. The control law operates by switching between two static gain vectors in such a way that the state trajectory is driven onto a stable n - 1 dimensional hyperplane (where n represents the system dimension). We begin by briefly examining relevant geometric properties of the phase portraits in the case of two-dimensional systems to develop intuition, and we then show how these geometric properties can be expressed as algebraic constraints on the switched vector fields that are applicable to LTI systems of arbitrary dimension. We then derive necessary and sufficient conditions to ensure stabilizability of the resulting switched system (characterized primarily by simple conditions on eigenvalues), and describe an explicit procedure for designing stabilizing controllers. We then show how the newly developed control law can be applied to the problem of minimizing the maximal Lyapunov exponent of the corresponding closed-loop state trajectories, and we illustrate the closed-loop transient performance of these switched state feedback controllers via multiple examples.
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Swing Damping for Helicopter Slung Load Systems using Delayed Feedback
Bisgaard, Morten; la Cour-Harbo, Anders; Bendtsen, Jan Dimon
2009-01-01
This paper presents the design and verification of a swing reducing controller for helicopter slung load systems usingintentional delayed feedback. It is intended for augmenting a trajectory tracking helicopter controller and thereby improving the slung load handing capabilities for autonomous helicopters. The delayed feedback controller is added to actively reduce oscillations of the slung load by improving the damping of the slung load pendulum modes. Furthermore, it is intended for integra...
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Radial basis function neural network for power system load-flow
International Nuclear Information System (INIS)
Karami, A.; Mohammadi, M.S.
2008-01-01
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
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An Artificial Neural Network Controller for Intelligent Transportation Systems Applications
1996-01-01
An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...
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Phase and amplitude feedback control system for the Los Alamos free-electron laser
International Nuclear Information System (INIS)
Lynch, M.T.; Tallerico, P.J.; Higgins, E.F.
1985-01-01
Phase and amplitude feedback control systems for the Los Alamos free-electron laser (FEL) are described. Beam-driven voltages are very high in the buncher cavity because the electron gun is pulsed at the fifth subharmonic of the buncher resonant frequency. The high beam loading necessitated a novel feedback and drive configuration for the buncher. A compensation cirucit has been added to the gun/driver system to reduce observed drift. Extremely small variations in the accelerator gradients had dramatic effects on the laser output power. These problems and how they were solved are described and plans for improvements in the feedback control system are discussed. 5 refs., 7 figs
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International Nuclear Information System (INIS)
Chao, A.W.; Morton, P.L.; Rees, J.R.
1979-03-01
To describe the horizontal motion of the bunch, we need four coordinates, x and z are the horizontal and longitudinal displacements of the bunch center relative to the ideal trajectory; x' is the angle between the bunch's direction of motion and the ideal trajectory; and δ=ΔE/E is relative energy error of the bunch. Among the four variables, x and z are easy to measure by position monitors, while x' and δ are easy to change by electromagnetic devices. In combination, this suggests four possible types of feedback systems. In the following, we will present a complete analysis of the Type (x, δ) feedback system, using a matrix method. The analyses of other types are similar to that of Type (x, δ) and only the results are included. We then include some comparisons of these types of feedback schemes in terms of power consumptions and the effectiveness in damping the horizontal-betatron and synchrotron oscillations. We will also discuss some effects of position measuring errors on the performance of the feedback system. 2 refs., 3 tabs
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On Optimal Feedback Control for Stationary Linear Systems
International Nuclear Information System (INIS)
Russell, David L.
2010-01-01
We study linear-quadratic optimal control problems for finite dimensional stationary linear systems AX+BU=Z with output Y=CX+DU from the viewpoint of linear feedback solution. We interpret solutions in relation to system robustness with respect to disturbances Z and relate them to nonlinear matrix equations of Riccati type and eigenvalue-eigenvector problems for the corresponding Hamiltonian system. Examples are included along with an indication of extensions to continuous, i.e., infinite dimensional, systems, primarily of elliptic type.
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Thoracic ROM measurement system with visual bio-feedback: system design and biofeedback evaluation.
Ando, Takeshi; Kawamura, Kazuya; Fujitani, Junko; Koike, Tomokazu; Fujimoto, Masashi; Fujie, Masakatsu G
2011-01-01
Patients with diseases such as chronic obstructive pulmonary disease (COPD) need to improve their thorax mobility. Thoracic ROM is one of the simplest and most useful indexes to evaluate the respiratory function. In this paper, we have proposed the prototype of a simple thoracic ROM measurement system with real-time visual bio-feedback in the chest expansion test. In this system, the thoracic ROM is measured using a wire-type linear encoder whose wire is wrapped around the thorax. In this paper, firstly, the repeatability and reliability of measured thoracic ROM was confirmed as a first report of the developed prototype. Secondly, we analyzed the effect of the bio-feedback system on the respiratory function. The result of the experiment showed that it was easier to maintain a large and stable thoracic ROM during deep breathing by using the real-time visual biofeedback system of the thoracic ROM.
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Real-time feedback for spatiotemporal field stabilization in MR systems.
Duerst, Yolanda; Wilm, Bertram J; Dietrich, Benjamin E; Vannesjo, S Johanna; Barmet, Christoph; Schmid, Thomas; Brunner, David O; Pruessmann, Klaas P
2015-02-01
MR imaging and spectroscopy require a highly stable, uniform background field. The field stability is typically limited by hardware imperfections, external perturbations, or field fluctuations of physiological origin. The purpose of the present work is to address these issues by introducing spatiotemporal field stabilization based on real-time sensing and feedback control. An array of NMR field probes is used to sense the field evolution in a whole-body MR system concurrently with regular system operation. The field observations serve as inputs to a proportional-integral controller that governs correction currents in gradient and higher-order shim coils such as to keep the field stable in a volume of interest. The feedback system was successfully set up, currently reaching a minimum latency of 20 ms. Its utility is first demonstrated by countering thermal field drift during an EPI protocol. It is then used to address respiratory field fluctuations in a T2 *-weighted brain exam, resulting in substantially improved image quality. Feedback field control is an effective means of eliminating dynamic field distortions in MR systems. Third-order spatial control at an update time of 100 ms has proven sufficient to largely eliminate thermal and breathing effects in brain imaging at 7 Tesla. © 2014 Wiley Periodicals, Inc.
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Catic, Aida; Gurbeta, Lejla; Kurtovic-Kozaric, Amina; Mehmedbasic, Senad; Badnjevic, Almir
2018-02-13
feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.
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High speed digital interfacing for a neural data acquisition system
Directory of Open Access Journals (Sweden)
Bahr Andreas
2016-09-01
Full Text Available Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1: 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.
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Feedback control of nonlinear quantum systems: a rule of thumb.
Jacobs, Kurt; Lund, Austin P
2007-07-13
We show that in the regime in which feedback control is most effective - when measurements are relatively efficient, and feedback is relatively strong - then, in the absence of any sharp inhomogeneity in the noise, it is always best to measure in a basis that does not commute with the system density matrix than one that does. That is, it is optimal to make measurements that disturb the state one is attempting to stabilize.
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Optical neuron by use of a laser diode with injection seeding and external optical feedback
Mos, E.C.; Hoppenbrouwers, J.J.L.; Hill, M.T.; Blum, M.W.; Schleipen, J.J.H.B.; Waardt, de H.
2000-01-01
We present an all-optical neuron by use of a multimode laser diode that is subjected to external optical feedback and light injection. The shape of the threshold function, that is needed for neural operation, is controlled by adjusting the external feedback level for two longitudinal cavity modes of
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Yang, X; Ding, H; Lu, J
2016-01-15
To investigate the feedback effect from area 7 to areas 17 and 18, intrinsic signal optical imaging combined with pharmacological, morphological methods and functional magnetic resonance imaging (fMRI) was employed. A spatial frequency-dependent decrease in response amplitude of orientation maps was observed in areas 17 and 18 when area 7 was inactivated by a local injection of GABA, or by a lesion induced by liquid nitrogen freezing. The pattern of orientation maps of areas 17 and 18 after the inactivation of area 7, if they were not totally blurred, paralleled the normal one. In morphological experiments, after one point at the shallow layers within the center of the cat's orientation column of area 17 was injected electrophoretically with HRP (horseradish peroxidase), three sequential patches in layers 1, 2 and 3 of area 7 were observed. Employing fMRI it was found that area 7 feedbacks mainly to areas 17 and 18 on ipsilateral hemisphere. Therefore, our conclusions are: (1) feedback from area 7 to areas 17 and 18 is spatial frequency modulated; (2) feedback from area 7 to areas 17 and 18 occurs mainly ipsilaterally; (3) histological feedback pattern from area 7 to area 17 is weblike. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.
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Delay-range-dependent exponential H∞ synchronization of a class of delayed neural networks
International Nuclear Information System (INIS)
Karimi, Hamid Reza; Maass, Peter
2009-01-01
This article aims to present a multiple delayed state-feedback control design for exponential H ∞ synchronization problem of a class of delayed neural networks with multiple time-varying discrete delays. On the basis of the drive-response concept and by introducing a descriptor technique and using Lyapunov-Krasovskii functional, new delay-range-dependent sufficient conditions for exponential H ∞ synchronization of the drive-response structure of neural networks are driven in terms of linear matrix inequalities (LMIs). The explicit expression of the controller gain matrices are parameterized based on the solvability conditions such that the drive system and the response system can be exponentially synchronized. A numerical example is included to illustrate the applicability of the proposed design method.
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ETSON proposal on the European operational experience feedback system
International Nuclear Information System (INIS)
Maqua, Michael; Bertrand, Remy; Gelder, Pieter de
2007-01-01
The new IAEA Safety Fundamentals states regarding the operating experience feedback: The feedback of operating experience from facilities and activities - and, where relevant, from elsewhere - is a key means of enhancing safety. Processes must be put in place for the feedback and analysis of operating experience, including initiating events, accident precursors, near misses, accidents and unauthorized acts, so that lessons may be learned, shared and acted upon. This presentation deals with the proposal of the ETSON (European TSO Network) to optimize the European operating experiences feedback (OEF). It is generally recognized that the efficiency of nuclear safety supervision by public authorities is based on two key requirements: - the existence of a competent authority at national level, benefiting from an appropriate legislative and regulatory basis, from adequate (quantitatively and qualitatively) human resources, particularly for inspection purposes, - the availability of resources devoted to highly specialised independent technical expertise, in order to provide competent authorities with pertinent technical opinions on: -- the safety files provided by operators, for the purpose of licensing corresponding activities, -- the exploitation for regulatory purposes of the operating experience feed back from licensed nuclear installations. There are two worldwide systems intended to learn lessons from experience: the WANO (World Association of Nuclear Operators) system established by the licensees with access restricted to operating organizations and the IRS system jointly operated by IAEA and OECD/NEA accessible to regulators and to some other users nominated by the regulators in their countries. The IRS itself is dedicated to the analysis of safety significant operating events. NEA/CNRA runs a permanent working group on operating experience (WGOE). WGOE provides among other things also generic reports on safety concerns related to operating experiences and
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Akiba, M
2015-09-01
A photodetection system with an optical-feedback circuit accompanied by current amplification was fabricated to minimize the drawbacks associated with a transimpedance amplifier (TIA) with a very high resistance feedback resistor. Current amplification was implemented by extracting an output light from the same light source that emitted the feedback light. The current gain corresponds to the ratio of the photocurrent created by the output light to that created by the feedback light because the feedback current value is identical to the input photocurrent value generated by an input light to be measured. The current gain has no theoretical limit. The output light was detected by a photodiode with a TIA having a small feedback resistance. The expression for the input-referred noise current of the optical-feedback photodetection system was derived, and the trade-off between sensitivity and response, which is a characteristic of TIA, was found to considerably improve. An optical-feedback photodetection system with an InGaAs pin photodiode was fabricated. The measured noise equivalent power of the system was 1.7 fW/Hz(1/2) at 10 Hz and 1.3 μm, which is consistent with the derived expression. The time response of the system was found to deteriorate with decreasing photocurrent. The 50% rise time for a light pulse input increased from 3.1 μs at a photocurrent of 10 nA to 15 μs at photocurrents below 10 pA. The bandwidth of the input-referred noise current was 7 kHz, which is consistent with rise times below 10 pA.
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Web/smart phone based control and feedback systems for irrigation systems
The role of the internet and mobile devices in the control and feedback of irrigation systems is reviewed. This role is placed in the larger context of four distinct components required for irrigation management, including 1. the control panel; 2. remote control; 3. soil, plant, and weather (SPW) se...
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Gao, Fangzheng; Wu, Yuqiang; Zhang, Zhongcai
2015-11-01
This paper investigates the problem of finite-time stabilization by output feedback for a class of nonholonomic systems in chained form with uncertainties. Comparing with the existing relevant literature, a distinguishing feature of the systems under investigation is that the x-subsystem is a feedforward-like rather than feedback-like system. This renders the existing control methods inapplicable to the control problems of the systems. A constructive design procedure for output feedback control is given. The designed controller renders that the states of closed-loop system are regulated to zero in a finite time. Two simulation examples are provided to illustrate the effectiveness of the proposed approach. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
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Adaptive H∞ synchronization of chaotic systems via linear and nonlinear feedback control
International Nuclear Information System (INIS)
Fu Shi-Hui; Lu Qi-Shao; Du Ying
2012-01-01
Adaptive H ∞ synchronization of chaotic systems via linear and nonlinear feedback control is investigated. The chaotic systems are redesigned by using the generalized Hamiltonian systems and observer approach. Based on Lyapunov's stability theory, linear and nonlinear feedback control of adaptive H ∞ synchronization is established in order to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance on an H ∞ -norm constraint. Adaptive H ∞ synchronization of chaotic systems via three kinds of control is investigated with applications to Lorenz and Chen systems. Numerical simulations are also given to identify the effectiveness of the theoretical analysis. (general)
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International Nuclear Information System (INIS)
Wang Jing; Gao Jinfeng; Ma Xikui
2007-01-01
This Letter presents a novel cross active backstepping design method for synchronization control of cross-strict feedback hyperchaotic system, in which the ordinary backstepping design is unavailable. The proposed control method, combining backstepping design and active control approach, extends the application of backstepping technique in chaos control. Based on this method, different combinations of controllers can be designed to meet the needs of different applications. The proposed method is applied to achieve chaos synchronization of two identical cross-strict feedback hyperchaotic systems. Also it is used to implement synchronization between cross-strict feedback hyperchaotic system and Roessler hyperchaotic system. Numerical examples illustrate the validity of the control method
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Directory of Open Access Journals (Sweden)
Chiu Choi
2017-02-01
Full Text Available Transient response such as ringing in a control system can be reduced or removed by velocity feedback. It is a useful control technique that should be covered in the relevant engineering laboratory courses. We developed velocity feedback experiments using two different low cost technologies, viz., operational amplifiers and microcontrollers. These experiments can be easily integrated into laboratory courses on feedback control systems or microcontroller applications. The intent of developing these experiments was to illustrate the ringing problem and to offer effective, low cost solutions for removing such problem. In this paper the pedagogical approach for these velocity feedback experiments was described. The advantages and disadvantages of the two different implementation of velocity feedback were discussed also.
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Research of a New 6-Dof Force Feedback Hand Controller System
Directory of Open Access Journals (Sweden)
Xin Gao
2014-01-01
Full Text Available The field of teleoperation with force telepresence has expanded its scope to include manipulation at different scales and in virtual worlds, and the key component of which is force feedback hand controller. This paper presents a novel force feedback hand controller system, including a 3-dof translational and 3-dof rotational hand controllers, respectively, to implement position and posture teleoperation of the robot end effector. The 3-dof translational hand controller adopts innovative three-axes decoupling structure based on the linear motor; the 3-dof rotational hand controller adopts serial mechanism based on three-axes intersecting at one point, improving its overall stiffness. Based on the kinematics, statics, and dynamics analyses for two platforms separately, the system applies big closed-loop force control method based on the zero force/torque, improving the feedback force/torque accuracy effectively. Experimental results show that self-developed 6-dof force feedback hand controller has good mechanical properties. The translational hand controller has the following advantages: simple kinematics solver, fast dynamic response, and better than 0.05 mm accuracy of three-axis end positioning, while the advantages of the rotational hand controller are wide turning space, larger than 1 Nm feedback, greater than 180 degrees of operating space of three axes, respectively, and high operation precision.
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Synthesis of human-nature feedbacks
Directory of Open Access Journals (Sweden)
Vanessa Hull
2015-09-01
Full Text Available In today's globalized world, humans and nature are inextricably linked. The coupled human and natural systems (CHANS framework provides a lens with which to understand such complex interactions. One of the central components of the CHANS framework involves examining feedbacks among human and natural systems, which form when effects from one system on another system feed back to affect the first system. Despite developments in understanding feedbacks in single disciplines, interdisciplinary research on CHANS feedbacks to date is scant and often site-specific, a shortcoming that prevents complex coupled systems from being fully understood. The special feature "Exploring Feedbacks in Coupled Human and Natural Systems (CHANS" makes strides to fill this critical gap. Here, as an introduction to the special feature, we provide an overview of CHANS feedbacks. In addition, we synthesize key CHANS feedbacks that emerged in the papers of this special feature across agricultural, forest, and urban landscapes. We also examine emerging themes explored across the papers, including multilevel feedbacks, time lags, and surprises as a result of feedbacks. We conclude with recommendations for future research that can build upon the foundation provided in the special feature.
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Feedback controlled dephasing and population relaxation in a two-level system
International Nuclear Information System (INIS)
Wang Jin
2009-01-01
This Letter presents the maximum achievable stability and purity that can be obtained in a two-level system with both dephasing and population relaxation processes by using homodyne-mediated feedback control. An analytic formula giving the optimal amplitudes of the driving and feedback for the steady-state is also presented. Experimental examples are used to show the importance of controlling the dephasing process.
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Equilibrium of a two-route system with delayed information feedback strategies
International Nuclear Information System (INIS)
Zhao, Xiao-mei; Xie, Dong-fan; Gao, Zi-you; Gao, Liang
2013-01-01
In intelligent transport system, some advanced information feedback strategies have been developed to reduce the oscillations and enhance the capacity on the road level. However, seldom strategies have considered the information delay and user equilibrium (UE) objective. Here, a derivative cost feedback strategy (DCFS) is proposed to reduce the influence of the delay, based on the UE principle. The simulation results show that in both no-delay and delay information cases, DCFS are the best and can make the system reaching the UE. Because DCFS can predict the trend of the travel cost.
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Equilibrium of a two-route system with delayed information feedback strategies
Energy Technology Data Exchange (ETDEWEB)
Zhao, Xiao-mei, E-mail: xmzhao@bjtu.edu.cn [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044 (China); Xie, Dong-fan, E-mail: dfxie@bjtu.edu.cn [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044 (China); Gao, Zi-you, E-mail: zygao@bjtu.edu.cn [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044 (China); Gao, Liang, E-mail: lianggao@bjtu.edu.cn [School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044 (China); MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044 (China)
2013-12-09
In intelligent transport system, some advanced information feedback strategies have been developed to reduce the oscillations and enhance the capacity on the road level. However, seldom strategies have considered the information delay and user equilibrium (UE) objective. Here, a derivative cost feedback strategy (DCFS) is proposed to reduce the influence of the delay, based on the UE principle. The simulation results show that in both no-delay and delay information cases, DCFS are the best and can make the system reaching the UE. Because DCFS can predict the trend of the travel cost.
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Finite-time output feedback stabilization of high-order uncertain nonlinear systems
Jiang, Meng-Meng; Xie, Xue-Jun; Zhang, Kemei
2018-06-01
This paper studies the problem of finite-time output feedback stabilization for a class of high-order nonlinear systems with the unknown output function and control coefficients. Under the weaker assumption that output function is only continuous, by using homogeneous domination method together with adding a power integrator method, introducing a new analysis method, the maximal open sector Ω of output function is given. As long as output function belongs to any closed sector included in Ω, an output feedback controller can be developed to guarantee global finite-time stability of the closed-loop system.
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Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung
2005-12-01
The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.
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ℋ∞ constant gain state feedback stabilization of stochastic hybrid systems with Wiener process
Directory of Open Access Journals (Sweden)
E. K. Boukas
2004-01-01
Full Text Available This paper considers the stabilization problem of the class of continuous-time linear stochastic hybrid systems with Wiener process. The ℋ∞ state feedback stabilization problem is treated. A state feedback controller with constant gain that does not require access to the system mode is designed. LMI-based conditions are developed to design the state feedback controller with constant gain that stochastically stabilizes the studied class of systems and, at the same time, achieve the disturbance rejection of a desired level. The minimum disturbance rejection is also determined. Numerical examples are given to show the usefulness of the proposed results.
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Brain activation upon ideal-body media exposure and peer feedback in late adolescent girls
van der Meulen, Mara; Veldhuis, Jolanda; Braams, Barbara R.; Peters, Sabine; Konijn, Elly A.; Crone, Eveline A.
2017-01-01
Media?s prevailing thin-body ideal plays a vital role in adolescent girls? body image development, but the co-occurring impact of peer feedback is understudied. The present study used functional magnetic resonance imaging (fMRI) to test media imagery and peer feedback combinations on neural activity related to thin-body ideals. Twenty-four healthy female late adolescents rated precategorized body sizes of bikini models (too thin or normal), directly followed by ostensible peer feedback (too t...
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Directory of Open Access Journals (Sweden)
R.Maheswari
2008-06-01
Full Text Available This paper presents the design of periodic output feedback control using state feedback gain to control the vibration of piezo actuated cantilever beam. The effectiveness of the controller is evaluated through simulation and experimentally by exciting the structure at resonance. Real time implementation of the controller is done using microcontroller. The closed loop eigen values of the system with periodic output feedback and state feedback are identical.
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Auditory display as feedback for a novel eye-tracking system for sterile operating room interaction.
Black, David; Unger, Michael; Fischer, Nele; Kikinis, Ron; Hahn, Horst; Neumuth, Thomas; Glaser, Bernhard
2018-01-01
The growing number of technical systems in the operating room has increased attention on developing touchless interaction methods for sterile conditions. However, touchless interaction paradigms lack the tactile feedback found in common input devices such as mice and keyboards. We propose a novel touchless eye-tracking interaction system with auditory display as a feedback method for completing typical operating room tasks. Auditory display provides feedback concerning the selected input into the eye-tracking system as well as a confirmation of the system response. An eye-tracking system with a novel auditory display using both earcons and parameter-mapping sonification was developed to allow touchless interaction for six typical scrub nurse tasks. An evaluation with novice participants compared auditory display with visual display with respect to reaction time and a series of subjective measures. When using auditory display to substitute for the lost tactile feedback during eye-tracking interaction, participants exhibit reduced reaction time compared to using visual-only display. In addition, the auditory feedback led to lower subjective workload and higher usefulness and system acceptance ratings. Due to the absence of tactile feedback for eye-tracking and other touchless interaction methods, auditory display is shown to be a useful and necessary addition to new interaction concepts for the sterile operating room, reducing reaction times while improving subjective measures, including usefulness, user satisfaction, and cognitive workload.
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Fault diagnosis system of electromagnetic valve using neural network filter
International Nuclear Information System (INIS)
Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu
2008-01-01
This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)
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Neural signatures of experience-based improvements in deterministic decision-making.
Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A
2016-12-15
Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.
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Can fMRI help optimise lifestyle behaviour change feedback from wearable technologies?
Directory of Open Access Journals (Sweden)
Maxine Whelan
2015-10-01
Full Text Available Background Non-communicable diseases (NCDs place severe financial strain on global health resources. Diabetes mellitus, the second most prevalent NCD, has been attributed to 8.4% of deaths worldwide for adults aged 20-79 years (International Diabetes Federation, 2013 with physical inactivity attributable to 7% of cases (Lee et al., 2012. The recent surge in commercially available wearable technology has begun to allow individuals to self-monitor their physical activity and sedentary behaviour as well as the physiological response to these behaviours (e.g., health markers such as glucose levels. Equipped with feedback obtained from such wearables, individuals are better able to understand the relationship between the lifestyle behaviours they take (e.g. going for a walk after dinner and health consequences (e.g. less glucose excursions (area under the curve. However, in order to achieve true behaviour change, the feedback must be optimised. Innovative communications research suggest that health messages (and in our case feedback that activates brain regions such as the medial prefrontal cortex (Falk, Berkman, Mann, Harrison & Lieberman, 2010 can predict and are associated with successful behaviour change. Fortunately, functional magnetic resonance imaging (fMRI can map this neural activity whilst individuals receive various forms of personalised feedback. Such insight into the optimisation of feedback can improve the design and delivery of future behaviour change interventions. Aim Examine neural activity in response to personalised feedback in order to identify health messages most potent for behaviour change. Methods A mixed gender sample of 30 adults (aged 30-65 years will be recruited through campus advertisements at Loughborough University, UK. Physical activity and sedentary behaviour will be assessed using waist-worn ActiGraph GT3x-BT accelerometer (100Hz and LUMO posture sensor (30Hz, respectively. Both devices will be removed for sleep
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International Nuclear Information System (INIS)
Lebedev, V.A.
1993-01-01
A super collider transverse feedback system designed to suppress injection errors, emittance growth due to external noises, and beam instabilities is considered. It is supposed that the feedback system should consist of two circuits: an injection damper operating just after injection and a super damper. To damp the emittance growth, the superdamper has to operate with the ultimate decrement close to the revolution frequency. The physics of such a feedback system and its main limitations are discussed. 9 refs.; 21 figs.; 1 tab
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Zaher, Ashraf A
2008-03-01
A technique is introduced for identifying uncertain and/or unknown parameters of chaotic dynamical systems via using simple state feedback. The proposed technique is based on bringing the system into a stable steady state and then solving for the unknown parameters using a simple algebraic method that requires access to the complete or partial states of the system depending on the dynamical model of the chaotic system. The choice of the state feedback is optimized in terms of practicality and causality via employing a single feedback signal and tuning the feedback gain to ensure both stability and identifiability. The case when only a single scalar time series of one of the states is available is also considered and it is demonstrated that a synchronization-based state observer can be augmented to the state feedback to address this problem. A detailed case study using the Lorenz system is used to exemplify the suggested technique. In addition, both the Rössler and Chua systems are examined as possible candidates for utilizing the proposed methodology when partial identification of the unknown parameters is considered. Finally, the dependence of the proposed technique on the structure of the chaotic dynamical model and the operating conditions is discussed and its advantages and limitations are highlighted via comparing it with other methods reported in the literature.
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Development of an accident diagnosis system using a dynamic neural network for nuclear power plants
International Nuclear Information System (INIS)
Lee, Seung Jun; Kim, Jong Hyun; Seong, Poong Hyun
2004-01-01
In this work, an accident diagnosis system using the dynamic neural network is developed. In order to help the plant operators to quickly identify the problem, perform diagnosis and initiate recovery actions ensuring the safety of the plant, many operator support system and accident diagnosis systems have been developed. Neural networks have been recognized as a good method to implement an accident diagnosis system. However, conventional accident diagnosis systems that used neural networks did not consider a time factor sufficiently. If the neural network could be trained according to time, it is possible to perform more efficient and detailed accidents analysis. Therefore, this work suggests a dynamic neural network which has different features from existing dynamic neural networks. And a simple accident diagnosis system is implemented in order to validate the dynamic neural network. After training of the prototype, several accident diagnoses were performed. The results show that the prototype can detect the accidents correctly with good performances
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Three neural network based sensor systems for environmental monitoring
International Nuclear Information System (INIS)
Keller, P.E.; Kouzes, R.T.; Kangas, L.J.
1994-05-01
Compact, portable systems capable of quickly identifying contaminants in the field are of great importance when monitoring the environment. One of the missions of the Pacific Northwest Laboratory is to examine and develop new technologies for environmental restoration and waste management at the Hanford Site. In this paper, three prototype sensing systems are discussed. These prototypes are composed of sensing elements, data acquisition system, computer, and neural network implemented in software, and are capable of automatically identifying contaminants. The first system employs an array of tin-oxide gas sensors and is used to identify chemical vapors. The second system employs an array of optical sensors and is used to identify the composition of chemical dyes in liquids. The third system contains a portable gamma-ray spectrometer and is used to identify radioactive isotopes. In these systems, the neural network is used to identify the composition of the sensed contaminant. With a neural network, the intense computation takes place during the training process. Once the network is trained, operation consists of propagating the data through the network. Since the computation involved during operation consists of vector-matrix multiplication and application of look-up tables unknown samples can be rapidly identified in the field
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Directory of Open Access Journals (Sweden)
Yang Fang
2014-01-01
Full Text Available This paper investigates the robust adaptive exponential synchronization in mean square of stochastic perturbed chaotic delayed neural networks with nonidentical parametric uncertainties. A robust adaptive feedback controller is proposed based on Gronwally’s inequality, drive-response concept, and adaptive feedback control technique with the update laws of nonidentical parametric uncertainties as well as linear matrix inequality (LMI approach. The sufficient conditions for robust adaptive exponential synchronization in mean square of uncoupled uncertain stochastic chaotic delayed neural networks are derived in terms of linear matrix inequalities (LMIs. The effect of nonidentical uncertain parameter uncertainties is suppressed by the designed robust adaptive feedback controller rapidly. A numerical example is provided to validate the effectiveness of the proposed method.
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Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems
Directory of Open Access Journals (Sweden)
Lili Zhang
2014-01-01
Full Text Available A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach.
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Keller, James M; Fogel, David B
2016-01-01
This book covers the three fundamental topics that form the basis of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basi function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzz...
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Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
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Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study
Directory of Open Access Journals (Sweden)
Soha Saleh
2017-01-01
Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with
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Banis, Stella; Geerligs, Linda; Lorist, Monicque M.
2014-01-01
Sex-specific prevalence rates in mental and physical disorders may be partly explained by sex differences in physiological stress responses. Neural networks that might be involved are those underlying feedback processing. Aim of the present EEG study was to investigate whether acute stress alters feedback processing, and whether stress effects differ between men and women. Male and female participants performed a gambling task, in a control and a stress condition. Stress was induced by exposing participants to a noise stressor. Brain activity was analyzed using both event-related potential and time-frequency analyses, measuring the feedback-related negativity (FRN) and feedback-related changes in theta and beta oscillatory power, respectively. While the FRN and feedback-related theta power were similarly affected by stress induction in both sexes, feedback-related beta power depended on the combination of stress induction condition and sex. FRN amplitude and theta power increases were smaller in the stress relative to the control condition in both sexes, demonstrating that acute noise stress impairs performance monitoring irrespective of sex. However, in the stress but not in the control condition, early lower beta-band power increases were larger for men than women, indicating that stress effects on feedback processing are partly sex-dependent. Our findings suggest that sex-specific effects on feedback processing may comprise a factor underlying sex-specific stress responses. PMID:24755943
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Directory of Open Access Journals (Sweden)
Stella Banis
Full Text Available Sex-specific prevalence rates in mental and physical disorders may be partly explained by sex differences in physiological stress responses. Neural networks that might be involved are those underlying feedback processing. Aim of the present EEG study was to investigate whether acute stress alters feedback processing, and whether stress effects differ between men and women. Male and female participants performed a gambling task, in a control and a stress condition. Stress was induced by exposing participants to a noise stressor. Brain activity was analyzed using both event-related potential and time-frequency analyses, measuring the feedback-related negativity (FRN and feedback-related changes in theta and beta oscillatory power, respectively. While the FRN and feedback-related theta power were similarly affected by stress induction in both sexes, feedback-related beta power depended on the combination of stress induction condition and sex. FRN amplitude and theta power increases were smaller in the stress relative to the control condition in both sexes, demonstrating that acute noise stress impairs performance monitoring irrespective of sex. However, in the stress but not in the control condition, early lower beta-band power increases were larger for men than women, indicating that stress effects on feedback processing are partly sex-dependent. Our findings suggest that sex-specific effects on feedback processing may comprise a factor underlying sex-specific stress responses.
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Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert
2017-08-01
Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a
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IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA
Directory of Open Access Journals (Sweden)
KARAM M. Z. OTHMAN
2011-08-01
Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.
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PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET
Directory of Open Access Journals (Sweden)
S. Devaraju
2014-04-01
Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.
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Ramamoorthy, P. A.; Huang, Song; Govind, Girish
1991-01-01
In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.
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Neural systems analysis of decision making during goal-directed navigation.
Penner, Marsha R; Mizumori, Sheri J Y
2012-01-01
The ability to make adaptive decisions during goal-directed navigation is a fundamental and highly evolved behavior that requires continual coordination of perceptions, learning and memory processes, and the planning of behaviors. Here, a neurobiological account for such coordination is provided by integrating current literatures on spatial context analysis and decision-making. This integration includes discussions of our current understanding of the role of the hippocampal system in experience-dependent navigation, how hippocampal information comes to impact midbrain and striatal decision making systems, and finally the role of the striatum in the implementation of behaviors based on recent decisions. These discussions extend across cellular to neural systems levels of analysis. Not only are key findings described, but also fundamental organizing principles within and across neural systems, as well as between neural systems functions and behavior, are emphasized. It is suggested that studying decision making during goal-directed navigation is a powerful model for studying interactive brain systems and their mediation of complex behaviors. Copyright © 2011. Published by Elsevier Ltd.
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Intelligent neural network and fuzzy logic control of industrial and power systems
Kuljaca, Ognjen
The main role played by neural network and fuzzy logic intelligent control algorithms today is to identify and compensate unknown nonlinear system dynamics. There are a number of methods developed, but often the stability analysis of neural network and fuzzy control systems was not provided. This work will meet those problems for the several algorithms. Some more complicated control algorithms included backstepping and adaptive critics will be designed. Nonlinear fuzzy control with nonadaptive fuzzy controllers is also analyzed. An experimental method for determining describing function of SISO fuzzy controller is given. The adaptive neural network tracking controller for an autonomous underwater vehicle is analyzed. A novel stability proof is provided. The implementation of the backstepping neural network controller for the coupled motor drives is described. Analysis and synthesis of adaptive critic neural network control is also provided in the work. Novel tuning laws for the system with action generating neural network and adaptive fuzzy critic are given. Stability proofs are derived for all those control methods. It is shown how these control algorithms and approaches can be used in practical engineering control. Stability proofs are given. Adaptive fuzzy logic control is analyzed. Simulation study is conducted to analyze the behavior of the adaptive fuzzy system on the different environment changes. A novel stability proof for adaptive fuzzy logic systems is given. Also, adaptive elastic fuzzy logic control architecture is described and analyzed. A novel membership function is used for elastic fuzzy logic system. The stability proof is proffered. Adaptive elastic fuzzy logic control is compared with the adaptive nonelastic fuzzy logic control. The work described in this dissertation serves as foundation on which analysis of particular representative industrial systems will be conducted. Also, it gives a good starting point for analysis of learning abilities of
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Integrated evolutionary computation neural network quality controller for automated systems
Energy Technology Data Exchange (ETDEWEB)
Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering
1999-06-01
With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.
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Integrated regional changes in arctic climate feedbacks: Implications for the global climate system
McGuire, A.D.; Chapin, F. S.; Walsh, J.E.; Wirth, C.; ,
2006-01-01
The Arctic is a key part of the global climate system because the net positive energy input to the tropics must ultimately be resolved through substantial energy losses in high-latitude regions. The Arctic influences the global climate system through both positive and negative feedbacks that involve physical, ecological, and human systems of the Arctic. The balance of evidence suggests that positive feedbacks to global warming will likely dominate in the Arctic during the next 50 to 100 years. However, the negative feedbacks associated with changing the freshwater balance of the Arctic Ocean might abruptly launch the planet into another glacial period on longer timescales. In light of uncertainties and the vulnerabilities of the climate system to responses in the Arctic, it is important that we improve our understanding of how integrated regional changes in the Arctic will likely influence the evolution of the global climate system. Copyright ?? 2006 by Annual Reviews. All rights reserved.
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Feedback as Real-Time Constructions
Keiding, Tina Bering; Qvortrup, Ane
2014-01-01
This article offers a re-description of feedback and the significance of time in feedback constructions based on systems theory. It describes feedback as internal, real-time constructions in a learning system. From this perspective, feedback is neither immediate nor delayed, but occurs in the very moment it takes place. This article argues for a…
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Robust output feedback H-infinity control and filtering for uncertain linear systems
Chang, Xiao-Heng
2014-01-01
"Robust Output Feedback H-infinity Control and Filtering for Uncertain Linear Systems" discusses new and meaningful findings on robust output feedback H-infinity control and filtering for uncertain linear systems, presenting a number of useful and less conservative design results based on the linear matrix inequality (LMI) technique. Though primarily intended for graduate students in control and filtering, the book can also serve as a valuable reference work for researchers wishing to explore the area of robust H-infinity control and filtering of uncertain systems. Dr. Xiao-Heng Chang is a Professor at the College of Engineering, Bohai University, China.
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Down sampled signal processing for a B Factory bunch-by-bunch feedback system
International Nuclear Information System (INIS)
Hindi, H.; Hosseini, W.; Briggs, D.; Fox, J.; Hutton, A.
1992-03-01
A bunch-by-bunch feedback scheme is studied for damping coupled bunch synchrotron oscillations in the proposed PEP II B Factory. The quasi-linear feedback systems design incorporates a phase detector to provide a quantized measure of bunch phase, digital signal processing to compute an error correction signal and a kicker system to correct the energy of the bunches. A farm of digital processors, operating in parallel, is proposed to compute correction signals for the 1658 bunches of the B Factory. This paper studies the use of down sampled processing to reduce the computational complexity of the feedback system. We present simulation results showing the effect of down sampling on beam dynamics. Results show that down sampled processing can reduce the scale of the processing task by a factor of 10
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Zhang, Kemei; Zhao, Cong-Ran; Xie, Xue-Jun
2015-12-01
This paper considers the problem of output feedback stabilisation for stochastic high-order feedforward nonlinear systems with time-varying delay. By using the homogeneous domination theory and solving several troublesome obstacles in the design and analysis, an output feedback controller is constructed to drive the closed-loop system globally asymptotically stable in probability.
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Design of a Haptic Feedback System for Flight Envelope Protection
Van Baelen, D.; Ellerbroek, J.; van Paassen, M.M.; Mulder, M.
2018-01-01
Current Airbus aircraft use a fly-by-wire control device: a passive spring-damper system which generates, without any force feedback, an electrical signal to the flight control computer. Additionally, a hard flight envelope protection system is used which can limit the inputs of the pilot when
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Educators' Perceptions of Automated Feedback Systems
Debuse, Justin C. W.; Lawley, Meredith; Shibl, Rania
2008-01-01
Assessment of student learning is a core function of educators. Ideally students should be provided with timely, constructive feedback to facilitate learning. However, provision of high quality feedback becomes more complex as class sizes increase, modes of study expand and academic workloads increase. ICT solutions are being developed to…
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Mean Velocity Prediction Information Feedback Strategy in Two-Route Systems under ATIS
Directory of Open Access Journals (Sweden)
Jianqiang Wang
2015-02-01
Full Text Available Feedback contents of previous information feedback strategies in advanced traveler information systems are almost real-time traffic information. Compared with real-time information, prediction traffic information obtained by a reliable and effective prediction algorithm has many undisputable advantages. In prediction information environment, a traveler is prone to making a more rational route-choice. For these considerations, a mean velocity prediction information feedback strategy (MVPFS is presented. The approach adopts the autoregressive-integrated moving average model (ARIMA to forecast short-term traffic flow. Furthermore, prediction results of mean velocity are taken as feedback contents and displayed on a variable message sign to guide travelers' route-choice. Meanwhile, discrete choice model (Logit model is selected to imitate more appropriately travelers' route-choice behavior. In order to investigate the performance of MVPFS, a cellular automaton model with ARIMA is adopted to simulate a two-route scenario. The simulation shows that such innovative prediction feedback strategy is feasible and efficient. Even more importantly, this study demonstrates the excellence of prediction feedback ideology.
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Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network
Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao
2009-10-01
A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.
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Neural Mechanisms and Information Processing in Recognition Systems
Directory of Open Access Journals (Sweden)
Mamiko Ozaki
2014-10-01
Full Text Available Nestmate recognition is a hallmark of social insects. It is based on the match/mismatch of an identity signal carried by members of the society with that of the perceiving individual. While the behavioral response, amicable or aggressive, is very clear, the neural systems underlying recognition are not fully understood. Here we contrast two alternative hypotheses for the neural mechanisms that are responsible for the perception and information processing in recognition. We focus on recognition via chemical signals, as the common modality in social insects. The first, classical, hypothesis states that upon perception of recognition cues by the sensory system the information is passed as is to the antennal lobes and to higher brain centers where the information is deciphered and compared to a neural template. Match or mismatch information is then transferred to some behavior-generating centers where the appropriate response is elicited. An alternative hypothesis, that of “pre-filter mechanism”, posits that the decision as to whether to pass on the information to the central nervous system takes place in the peripheral sensory system. We suggest that, through sensory adaptation, only alien signals are passed on to the brain, specifically to an “aggressive-behavior-switching center”, where the response is generated if the signal is above a certain threshold.
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Social closeness and feedback modulate susceptibility to the framing effect
Sip, Kamila E.; Smith, David V.; Porcelli, Anthony J.; Kar, Kohitij; Delgado, Mauricio R.
2014-01-01
Although, we often seek social feedback from others to help us make decisions, little is known about how social feedback affects decisions under risk, particularly from a close peer. We conducted two experiments using an established framing task to probe how decision making is modulated by social feedback valence (positive, negative) and the level of closeness with feedback provider (friend, confederate). Participants faced mathematically equivalent decisions framed as either an opportunity to keep (gain frame) or lose (loss frame) part of an initial endowment. Periodically, participants were provided with positive (e.g., “Nice!”) or negative (e.g., “Lame!”) feedback about their choices. Such feedback was provided by either a confederate (Experiment 1), or a gender-matched close friend (Experiment 2). As expected, the framing effect was observed in both experiments. Critically, an individual’s susceptibility to the framing effect was modulated by the valence of the social feedback, but only when the feedback provider was a close friend. This effect was reflected in the activation patterns of ventromedial prefrontal cortex and posterior cingulate cortex, regions involved in complex decision making. Taken together, these results highlight social closeness as an important factor in understanding the impact of social feedback on neural mechanisms of decision making. PMID:25074501
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Reliability analysis of a consecutive r-out-of-n: F system based on neural networks
International Nuclear Information System (INIS)
Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada
2009-01-01
In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.
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Virkar, Yogesh S.; Shew, Woodrow L.; Restrepo, Juan G.; Ott, Edward
2016-10-01
Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model composed of two interacting networks: a model neural network interconnected by synapses that undergo spike-timing-dependent plasticity; and a model glial network interconnected by gap junctions that diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.
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Neural Basis of Intrinsic Motivation: Evidence from Event-Related Potentials.
Jin, Jia; Yu, Liping; Ma, Qingguo
2015-01-01
Human intrinsic motivation is of great importance in human behavior. However, although researchers have focused on this topic for decades, its neural basis was still unclear. The current study employed event-related potentials to investigate the neural disparity between an interesting stop-watch (SW) task and a boring watch-stop task (WS) to understand the neural mechanisms of intrinsic motivation. Our data showed that, in the cue priming stage, the cue of the SW task elicited smaller N2 amplitude than that of the WS task. Furthermore, in the outcome feedback stage, the outcome of the SW task induced smaller FRN amplitude and larger P300 amplitude than that of the WS task. These results suggested that human intrinsic motivation did exist and that it can be detected at the neural level. Furthermore, intrinsic motivation could be quantitatively indexed by the amplitude of ERP components, such as N2, FRN, and P300, in the cue priming stage or feedback stage. Quantitative measurements would also be convenient for intrinsic motivation to be added as a candidate social factor in the construction of a machine learning model.
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Orhan, A Emin; Ma, Wei Ji
2017-07-26
Animals perform near-optimal probabilistic inference in a wide range of psychophysical tasks. Probabilistic inference requires trial-to-trial representation of the uncertainties associated with task variables and subsequent use of this representation. Previous work has implemented such computations using neural networks with hand-crafted and task-dependent operations. We show that generic neural networks trained with a simple error-based learning rule perform near-optimal probabilistic inference in nine common psychophysical tasks. In a probabilistic categorization task, error-based learning in a generic network simultaneously explains a monkey's learning curve and the evolution of qualitative aspects of its choice behavior. In all tasks, the number of neurons required for a given level of performance grows sublinearly with the input population size, a substantial improvement on previous implementations of probabilistic inference. The trained networks develop a novel sparsity-based probabilistic population code. Our results suggest that probabilistic inference emerges naturally in generic neural networks trained with error-based learning rules.Behavioural tasks often require probability distributions to be inferred about task specific variables. Here, the authors demonstrate that generic neural networks can be trained using a simple error-based learning rule to perform such probabilistic computations efficiently without any need for task specific operations.
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Comparison of Multiple-Microphone and Single-Loudspeaker Adaptive Feedback/Echo Cancellation Systems
DEFF Research Database (Denmark)
Guo, Meng; Elmedyb, Thomas Bo; Jensen, Søren Holdt
2011-01-01
Recently, we introduced a frequency domain measure - the power transfer function - to predict the convergence rate, system stability bound and the steady-state behavior across time and frequency of a least mean square based feedback/echo cancellation algorithm in a general multiple-microphone and......Recently, we introduced a frequency domain measure - the power transfer function - to predict the convergence rate, system stability bound and the steady-state behavior across time and frequency of a least mean square based feedback/echo cancellation algorithm in a general multiple...
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Dynamical systems, attractors, and neural circuits.
Miller, Paul
2016-01-01
Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.
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Sliding Intermittent Control for BAM Neural Networks with Delays
Directory of Open Access Journals (Sweden)
Jianqiang Hu
2013-01-01
Full Text Available This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.
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Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder.
Wittenborn, A K; Rahmandad, H; Rick, J; Hosseinichimeh, N
2016-02-01
Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics. We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder. The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression. Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention.
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Effect of visual feedback on brain activation during motor tasks: an FMRI study.
Noble, Jeremy W; Eng, Janice J; Boyd, Lara A
2013-07-01
This study examined the effect of visual feedback and force level on the neural mechanisms responsible for the performance of a motor task. We used a voxel-wise fMRI approach to determine the effect of visual feedback (with and without) during a grip force task at 35% and 70% of maximum voluntary contraction. Two areas (contralateral rostral premotor cortex and putamen) displayed an interaction between force and feedback conditions. When the main effect of feedback condition was analyzed, higher activation when visual feedback was available was found in 22 of the 24 active brain areas, while the two other regions (contralateral lingual gyrus and ipsilateral precuneus) showed greater levels of activity when no visual feedback was available. The results suggest that there is a potentially confounding influence of visual feedback on brain activation during a motor task, and for some regions, this is dependent on the level of force applied.
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Automatic lung segmentation using control feedback system: morphology and texture paradigm.
Noor, Norliza M; Than, Joel C M; Rijal, Omar M; Kassim, Rosminah M; Yunus, Ashari; Zeki, Amir A; Anzidei, Michele; Saba, Luca; Suri, Jasjit S
2015-03-01
Interstitial Lung Disease (ILD) encompasses a wide array of diseases that share some common radiologic characteristics. When diagnosing such diseases, radiologists can be affected by heavy workload and fatigue thus decreasing diagnostic accuracy. Automatic segmentation is the first step in implementing a Computer Aided Diagnosis (CAD) that will help radiologists to improve diagnostic accuracy thereby reducing manual interpretation. Automatic segmentation proposed uses an initial thresholding and morphology based segmentation coupled with feedback that detects large deviations with a corrective segmentation. This feedback is analogous to a control system which allows detection of abnormal or severe lung disease and provides a feedback to an online segmentation improving the overall performance of the system. This feedback system encompasses a texture paradigm. In this study we studied 48 males and 48 female patients consisting of 15 normal and 81 abnormal patients. A senior radiologist chose the five levels needed for ILD diagnosis. The results of segmentation were displayed by showing the comparison of the automated and ground truth boundaries (courtesy of ImgTracer™ 1.0, AtheroPoint™ LLC, Roseville, CA, USA). The left lung's performance of segmentation was 96.52% for Jaccard Index and 98.21% for Dice Similarity, 0.61 mm for Polyline Distance Metric (PDM), -1.15% for Relative Area Error and 4.09% Area Overlap Error. The right lung's performance of segmentation was 97.24% for Jaccard Index, 98.58% for Dice Similarity, 0.61 mm for PDM, -0.03% for Relative Area Error and 3.53% for Area Overlap Error. The segmentation overall has an overall similarity of 98.4%. The segmentation proposed is an accurate and fully automated system.
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Raul, P R; Dwivedula, R V; Pagilla, P R
2016-07-01
The problem of controlling the load speed of a mechanical transmission system consisting of a belt-pulley and gear-pair is considered. The system is modeled as two inertia (motor and load) connected by a compliant transmission. If the transmission is assumed to be rigid, then using either the motor or load speed feedback provides the same result. However, with transmission compliance, due to belts or long shafts, the stability characteristics and performance of the closed-loop system are quite different when either motor or load speed feedback is employed. We investigate motor and load speed feedback schemes by utilizing the singular perturbation method. We propose and discuss a control scheme that utilizes both motor and load speed feedback, and design an adaptive feedforward action to reject load torque disturbances. The control algorithms are implemented on an experimental platform that is typically used in roll-to-roll manufacturing and results are shown and discussed. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
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Stereo camera based virtual cane system with identifiable distance tactile feedback for the blind.
Kim, Donghun; Kim, Kwangtaek; Lee, Sangyoun
2014-06-13
In this paper, we propose a new haptic-assisted virtual cane system operated by a simple finger pointing gesture. The system is developed by two stages: development of visual information delivery assistant (VIDA) with a stereo camera and adding a tactile feedback interface with dual actuators for guidance and distance feedbacks. In the first stage, user's pointing finger is automatically detected using color and disparity data from stereo images and then a 3D pointing direction of the finger is estimated with its geometric and textural features. Finally, any object within the estimated pointing trajectory in 3D space is detected and the distance is then estimated in real time. For the second stage, identifiable tactile signals are designed through a series of identification experiments, and an identifiable tactile feedback interface is developed and integrated into the VIDA system. Our approach differs in that navigation guidance is provided by a simple finger pointing gesture and tactile distance feedbacks are perfectly identifiable to the blind.
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Effects of stochastic time-delayed feedback on a dynamical system modeling a chemical oscillator
González Ochoa, Héctor O.; Perales, Gualberto Solís; Epstein, Irving R.; Femat, Ricardo
2018-05-01
We examine how stochastic time-delayed negative feedback affects the dynamical behavior of a model oscillatory reaction. We apply constant and stochastic time-delayed negative feedbacks to a point Field-Körös-Noyes photosensitive oscillator and compare their effects. Negative feedback is applied in the form of simulated inhibitory electromagnetic radiation with an intensity proportional to the concentration of oxidized light-sensitive catalyst in the oscillator. We first characterize the system under nondelayed inhibitory feedback; then we explore and compare the effects of constant (deterministic) versus stochastic time-delayed feedback. We find that the oscillatory amplitude, frequency, and waveform are essentially preserved when low-dispersion stochastic delayed feedback is used, whereas small but measurable changes appear when a large dispersion is applied.
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Control of 12-Cylinder Camless Engine with Neural Networks
Directory of Open Access Journals (Sweden)
Ashhab Moh’d Sami
2017-01-01
Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.
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Smart Braid Feedback for the Closed-Loop Control of Soft Robotic Systems.
Felt, Wyatt; Chin, Khai Yi; Remy, C David
2017-09-01
This article experimentally investigates the potential of using flexible, inductance-based contraction sensors in the closed-loop motion control of soft robots. Accurate motion control remains a highly challenging task for soft robotic systems. Precise models of the actuation dynamics and environmental interactions are often unavailable. This renders open-loop control impossible, while closed-loop control suffers from a lack of suitable feedback. Conventional motion sensors, such as linear or rotary encoders, are difficult to adapt to robots that lack discrete mechanical joints. The rigid nature of these sensors runs contrary to the aspirational benefits of soft systems. As truly soft sensor solutions are still in their infancy, motion control of soft robots has so far relied on laboratory-based sensing systems such as motion capture, electromagnetic (EM) tracking, or Fiber Bragg Gratings. In this article, we used embedded flexible sensors known as Smart Braids to sense the contraction of McKibben muscles through changes in inductance. We evaluated closed-loop control on two systems: a revolute joint and a planar, one degree of freedom continuum manipulator. In the revolute joint, our proposed controller compensated for elasticity in the actuator connections. The Smart Braid feedback allowed motion control with a steady-state root-mean-square (RMS) error of [1.5]°. In the continuum manipulator, Smart Braid feedback enabled tracking of the desired tip angle with a steady-state RMS error of [1.25]°. This work demonstrates that Smart Braid sensors can provide accurate position feedback in closed-loop motion control suitable for field applications of soft robotic systems.
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An active feedback system to control synchrotron oscillations in the SLC Damping Rings
International Nuclear Information System (INIS)
Corredoura, P.L.; Pellegrin, J.L.; Schwarz, H.D.; Sheppard, J.C.
1989-03-01
Initially the SLC Damping Rings accomplished Robinson instability damping by operating the RF accelerating cavities slightly detuned. In order to be able to run the cavities tuned and achieve damping for Robinson instability and synchrotron oscillations at injection an active feedback system has been developed. This paper describes the theoretical basis for the feedback system and the development of the hardware. Extensive measurements of the loop response including stored beam were performed. Overall performance of the system is also reported. 3 refs., 6 figs
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System Identification, Prediction, Simulation and Control with Neural Networks
DEFF Research Database (Denmark)
Sørensen, O.
1997-01-01
a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...
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Prolactin and Male Fertility: The Long and Short Feedback Regulation
Directory of Open Access Journals (Sweden)
M. K. Gill-Sharma
2009-01-01
Full Text Available In the last 20 years, a pituitary-hypothalamus tissue culture system with intact neural and portal connections has been developed in our lab and used to understand the feedback mechanisms that regulate the secretions of adenohypophyseal hormones and fertility of male rats. In the last decade, several in vivo rat models have also been developed in our lab with a view to substantiate the in vitro findings, in order to delineate the role of pituitary hormones in the regulation of fertility of male rats. These studies have relied on both surgical and pharmacological interventions to modulate the secretions of gonadotropins and testosterone. The interrelationship between the circadian release of reproductive hormones has also been ascertained in normal men. Our studies suggest that testosterone regulates the secretion of prolactin through a long feedback mechanism, which appears to have been conserved from rats to humans. These studies have filled in a major lacuna pertaining to the role of prolactin in male reproductive physiology by demonstrating the interdependence between testosterone and prolactin. Systemic levels of prolactin play a deterministic role in the mechanism of chromatin condensation during spermiogenesis.
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General, database-driven fast-feedback system for the Stanford Linear Collider
International Nuclear Information System (INIS)
Rouse, F.; Allison, S.; Castillo, S.; Gromme, T.; Hall, B.; Hendrickson, L.; Himel, T.; Krauter, K.; Sass, B.; Shoaee, H.
1991-05-01
A new feedback system has been developed for stabilizing the SLC beams at many locations. The feedback loops are designed to sample and correct at the 60 Hz repetition rate of the accelerator. Each loop can be distributed across several of the standard 80386 microprocessors which control the SLC hardware. A new communications system, KISNet, has been implemented to pass signals between the microprocessors at this rate. The software is written in a general fashion using the state space formalism of digital control theory. This allows a new loop to be implemented by just setting up the online database and perhaps installing a communications link. 3 refs., 4 figs
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Directory of Open Access Journals (Sweden)
Melle J.W. Van Der Molen
2014-01-01
Full Text Available Cognitive models posit that the fear of negative evaluation (FNE is a hallmark feature of social anxiety. As such, individuals with high FNE may show biased information processing when faced with social evaluation. The aim of the current study was to examine the neural underpinnings of anticipating and processing of social-evaluative feedback, and its correlates with FNE. We used a social judgment paradigm in which female participants (N=31 were asked to indicate whether they believed to be socially accepted or rejected by their peers. Anticipatory attention was indexed by the stimulus preceding negativity (SPN, while the feedback-related negativity and P3 were used to index the processing of social-evaluative feedback. Results provided evidence of an optimism bias in social peer evaluation, as participants more often predicted to be socially accepted than rejected. Participants with high levels of FNE needed more time to provide their judgments about the social-evaluative outcome. While anticipating social-evaluative feedback, SPN amplitudes were larger for anticipated social acceptance than for social rejection feedback. Interestingly, the SPN during anticipated social acceptance was larger in participants with high levels of FNE. None of the feedback-related brain potentials correlated with the FNE. Together, the results provide evidence of biased information processing in individuals with high levels of FNE when anticipating (rather than processing social-evaluative feedback. The delayed response times in high FNE individuals were interpreted to reflect augmented vigilance imposed by the upcoming social evaluative threat. Allegedly, the SPN constitutes a neural marker of this vigilance in females with higher FNE levels, particularly when anticipating social acceptance feedback.
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Van der Molen, Melle J. W.; Poppelaars, Eefje S.; Van Hartingsveldt, Caroline T. A.; Harrewijn, Anita; Gunther Moor, Bregtje; Westenberg, P. Michiel
2014-01-01
Cognitive models posit that the fear of negative evaluation (FNE) is a hallmark feature of social anxiety. As such, individuals with high FNE may show biased information processing when faced with social evaluation. The aim of the current study was to examine the neural underpinnings of anticipating and processing social-evaluative feedback, and its correlates with FNE. We used a social judgment paradigm in which female participants (N = 31) were asked to indicate whether they believed to be socially accepted or rejected by their peers. Anticipatory attention was indexed by the stimulus preceding negativity (SPN), while the feedback-related negativity and P3 were used to index the processing of social-evaluative feedback. Results provided evidence of an optimism bias in social peer evaluation, as participants more often predicted to be socially accepted than rejected. Participants with high levels of FNE needed more time to provide their judgments about the social-evaluative outcome. While anticipating social-evaluative feedback, SPN amplitudes were larger for anticipated social acceptance than for social rejection feedback. Interestingly, the SPN during anticipated social acceptance was larger in participants with high levels of FNE. None of the feedback-related brain potentials correlated with the FNE. Together, the results provided evidence of biased information processing in individuals with high levels of FNE when anticipating (rather than processing) social-evaluative feedback. The delayed response times in high FNE individuals were interpreted to reflect augmented vigilance imposed by the upcoming social-evaluative threat. Possibly, the SPN constitutes a neural marker of this vigilance in females with higher FNE levels, particularly when anticipating social acceptance feedback. PMID:24478667
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Lau, Jennifer Y. F.; Guyer, Amanda E.; Tone, Erin B.; Jenness, Jessica; Parrish, Jessica M.; Pine, Daniel S.; Nelson, Eric E.
2012-01-01
Peer rejection powerfully predicts adolescent anxiety. While cognitive differences influence anxious responses to social feedback, little is known about neural contributions. Twelve anxious and twelve age-, gender- and IQ-matched, psychiatrically healthy adolescents received "not interested" and "interested" feedback from unknown peers during a…
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A high precision dual feedback discrete control system designed for satellite trajectory simulator
Liu, Ximin; Liu, Liren; Sun, Jianfeng; Xu, Nan
2005-08-01
Cooperating with the free-space laser communication terminals, the satellite trajectory simulator is used to test the acquisition, pointing, tracking and communicating performances of the terminals. So the satellite trajectory simulator plays an important role in terminal ground test and verification. Using the double-prism, Sun etc in our group designed a satellite trajectory simulator. In this paper, a high precision dual feedback discrete control system designed for the simulator is given and a digital fabrication of the simulator is made correspondingly. In the dual feedback discrete control system, Proportional- Integral controller is used in velocity feedback loop and Proportional- Integral- Derivative controller is used in position feedback loop. In the controller design, simplex method is introduced and an improvement to the method is made. According to the transfer function of the control system in Z domain, the digital fabrication of the simulator is given when it is exposed to mechanism error and moment disturbance. Typically, when the mechanism error is 100urad, the residual standard error of pitching angle, azimuth angle, x-coordinate position and y-coordinate position are 0.49urad, 6.12urad, 4.56urad, 4.09urad respectively. When the moment disturbance is 0.1rad, the residual standard error of pitching angle, azimuth angle, x-coordinate position and y-coordinate position are 0.26urad, 0.22urad, 0.16urad, 0.15urad respectively. The digital fabrication results demonstrate that the dual feedback discrete control system designed for the simulator can achieve the anticipated high precision performance.
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Container-code recognition system based on computer vision and deep neural networks
Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao
2018-04-01
Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.
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How Are Feedbacks Represented in Land Models?
Directory of Open Access Journals (Sweden)
Yang Chen
2016-09-01
Full Text Available Land systems are characterised by many feedbacks that can result in complex system behaviour. We defined feedbacks as the two-way influences between the land use system and a related system (e.g., climate, soils and markets, both of which are encompassed by the land system. Land models that include feedbacks thus probably more accurately mimic how land systems respond to, e.g., policy or climate change. However, representing feedbacks in land models is a challenge. We reviewed articles incorporating feedbacks into land models and analysed each with predefined indicators. We found that (1 most modelled feedbacks couple land use systems with transport, soil and market systems, while only a few include feedbacks between land use and social systems or climate systems; (2 equation-based land use models that follow a top-down approach prevail; and (3 feedbacks’ effects on system behaviour remain relatively unexplored. We recommend that land system modellers (1 consider feedbacks between land use systems and social systems; (2 adopt (bottom-up approaches suited to incorporating spatial heterogeneity and better representing land use decision-making; and (3 pay more attention to nonlinear system behaviour and its implications for land system management and policy.
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Lectures in feedback design for multivariable systems
Isidori, Alberto
2017-01-01
This book focuses on methods that relate, in one form or another, to the “small-gain theorem”. It is aimed at readers who are interested in learning methods for the design of feedback laws for linear and nonlinear multivariable systems in the presence of model uncertainties. With worked examples throughout, it includes both introductory material and more advanced topics. Divided into two parts, the first covers relevant aspects of linear-systems theory, the second, nonlinear theory. In order to deepen readers’ understanding, simpler single-input–single-output systems generally precede treatment of more complex multi-input–multi-output (MIMO) systems and linear systems precede nonlinear systems. This approach is used throughout, including in the final chapters, which explain the latest advanced ideas governing the stabilization, regulation, and tracking of nonlinear MIMO systems. Two major design problems are considered, both in the presence of model uncertainties: asymptotic stabilization with a “...
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Architecture of the APS real-time orbit feedback system
International Nuclear Information System (INIS)
Carwardine, J. A.; Lenkszus, F. R.
1997-01-01
The APS Real-Time Orbit Feedback System is designed to stabilize the orbit of the stored positron beam against low-frequency sources such as mechanical vibration and power supply ripple. A distributed array of digital signal processors is used to measure the orbit and compute corrections at a 1kHz rate. The system also provides extensive beam diagnostic tools. This paper describes the architectural aspects of the system and describes how the orbit correction algorithms are implemented
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Architecture of the APS real-time orbit feedback system.
Energy Technology Data Exchange (ETDEWEB)
Carwardine, J. A.; Lenkszus, F. R.
1997-11-21
The APS Real-Time Orbit Feedback System is designed to stabilize the orbit of the stored positron beam against low-frequency sources such as mechanical vibration and power supply ripple. A distributed array of digital signal processors is used to measure the orbit and compute corrections at a 1kHz rate. The system also provides extensive beam diagnostic tools. This paper describes the architectural aspects of the system and describes how the orbit correction algorithms are implemented.