Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

Science.gov (United States)

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

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.

PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

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A. Cancelier

Full Text Available Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

Mechanism for propagation of rate signals through a 10-layer feedforward neuronal network

International Nuclear Information System (INIS)

Jie, Li; Wan-Qing, Yu; Ding, Xu; Feng, Liu; Wei, Wang

2009-01-01

Using numerical simulations, we explore the mechanism for propagation of rate signals through a 10-layer feedforward network composed of Hodgkin–Huxley (HH) neurons with sparse connectivity. When white noise is afferent to the input layer, neuronal firing becomes progressively more synchronous in successive layers and synchrony is well developed in deeper layers owing to the feedforward connections between neighboring layers. The synchrony ensures the successful propagation of rate signals through the network when the synaptic conductance is weak. As the synaptic time constant τ syn varies, coherence resonance is observed in the network activity due to the intrinsic property of HH neurons. This makes the output firing rate single-peaked as a function of τ syn , suggesting that the signal propagation can be modulated by the synaptic time constant. These results are consistent with experimental results and advance our understanding of how information is processed in feedforward networks. (cross-disciplinary physics and related areas of science and technology)

Quantum generalisation of feedforward neural networks

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Wan, Kwok Ho; Dahlsten, Oscar; Kristjánsson, Hlér; Gardner, Robert; Kim, M. S.

2017-09-01

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e., unitary (the classical networks we generalise are called feedforward, and have step-function activation functions). The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.

A novel approach to error function minimization for feedforward neural networks

International Nuclear Information System (INIS)

Sinkus, R.

1995-01-01

Feedforward neural networks with error backpropagation are widely applied to pattern recognition. One general problem encountered with this type of neural networks is the uncertainty, whether the minimization procedure has converged to a global minimum of the cost function. To overcome this problem a novel approach to minimize the error function is presented. It allows to monitor the approach to the global minimum and as an outcome several ambiguities related to the choice of free parameters of the minimization procedure are removed. (orig.)

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

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.

Feedforward neural network model estimating pollutant removal process within mesophilic upflow anaerobic sludge blanket bioreactor treating industrial starch processing wastewater.

Science.gov (United States)

Antwi, Philip; Li, Jianzheng; Meng, Jia; Deng, Kaiwen; Koblah Quashie, Frank; Li, Jiuling; Opoku Boadi, Portia

2018-06-01

In this a, three-layered feedforward-backpropagation artificial neural network (BPANN) model was developed and employed to evaluate COD removal an upflow anaerobic sludge blanket (UASB) reactor treating industrial starch processing wastewater. At the end of UASB operation, microbial community characterization revealed satisfactory composition of microbes whereas morphology depicted rod-shaped archaea. pH, COD, NH 4 + , VFA, OLR and biogas yield were selected by principal component analysis and used as input variables. Whilst tangent sigmoid function (tansig) and linear function (purelin) were assigned as activation functions at the hidden-layer and output-layer, respectively, optimum BPANN architecture was achieved with Levenberg-Marquardt algorithm (trainlm) after eleven training algorithms had been tested. Based on performance indicators such the mean squared errors, fractional variance, index of agreement and coefficient of determination (R 2 ), the BPANN model demonstrated significant performance with R 2 reaching 87%. The study revealed that, control and optimization of an anaerobic digestion process with BPANN model was feasible. Copyright © 2018 Elsevier Ltd. All rights reserved.

Modeling of quasistatic magnetic hysteresis with feed-forward neural networks

International Nuclear Information System (INIS)

Makaveev, Dimitre; Dupre, Luc; De Wulf, Marc; Melkebeek, Jan

2001-01-01

A modeling technique for rate-independent (quasistatic) scalar magnetic hysteresis is presented, using neural networks. Based on the theory of dynamic systems and the wiping-out and congruency properties of the classical scalar Preisach hysteresis model, the choice of a feed-forward neural network model is motivated. The neural network input parameters at each time step are the corresponding magnetic field strength and memory state, thereby assuring accurate prediction of the change of magnetic induction. For rate-independent hysteresis, the current memory state can be determined by the last extreme magnetic field strength and induction values, kept in memory. The choice of a network training set is motivated and the performance of the network is illustrated for a test set not used during training. Very accurate prediction of both major and minor hysteresis loops is observed, proving that the neural network technique is suitable for hysteresis modeling. [copyright] 2001 American Institute of Physics

Neural network feedforward control of a closed-circuit wind tunnel

Science.gov (United States)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

Breast cancer detection via Hu moment invariant and feedforward neural network

Science.gov (United States)

Zhang, Xiaowei; Yang, Jiquan; Nguyen, Elijah

2018-04-01

One of eight women can get breast cancer during all her life. This study used Hu moment invariant and feedforward neural network to diagnose breast cancer. With the help of K-fold cross validation, we can test the out-of-sample accuracy of our method. Finally, we found that our methods can improve the accuracy of detecting breast cancer and reduce the difficulty of judging.

Direct and inverse neural networks modelling applied to study the influence of the gas diffusion layer properties on PBI-based PEM fuel cells

Energy Technology Data Exchange (ETDEWEB)

Lobato, Justo; Canizares, Pablo; Rodrigo, Manuel A.; Linares, Jose J. [Chemical Engineering Department, University of Castilla-La Mancha, Campus Universitario s/n, 13004 Ciudad Real (Spain); Piuleac, Ciprian-George; Curteanu, Silvia [Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, ' ' Gh. Asachi' ' Technical University Iasi Bd. D. Mangeron, No. 71A, 700050 IASI (Romania)

2010-08-15

This article shows the application of a very useful mathematical tool, artificial neural networks, to predict the fuel cells results (the value of the tortuosity and the cell voltage, at a given current density, and therefore, the power) on the basis of several properties that define a Gas Diffusion Layer: Teflon content, air permeability, porosity, mean pore size, hydrophobia level. Four neural networks types (multilayer perceptron, generalized feedforward network, modular neural network, and Jordan-Elman neural network) have been applied, with a good fitting between the predicted and the experimental values in the polarization curves. A simple feedforward neural network with one hidden layer proved to be an accurate model with good generalization capability (error about 1% in the validation phase). A procedure based on inverse neural network modelling was able to determine, with small errors, the initial conditions leading to imposed values for characteristics of the fuel cell. In addition, the use of this tool has been proved to be very attractive in order to predict the cell performance, and more interestingly, the influence of the properties of the gas diffusion layer on the cell performance, allowing possible enhancements of this material by changing some of its properties. (author)

Training feed-forward neural networks with gain constraints

Science.gov (United States)

Hartman

2000-04-01

Inaccurate input-output gains (partial derivatives of outputs with respect to inputs) are common in neural network models when input variables are correlated or when data are incomplete or inaccurate. Accurate gains are essential for optimization, control, and other purposes. We develop and explore a method for training feedforward neural networks subject to inequality or equality-bound constraints on the gains of the learned mapping. Gain constraints are implemented as penalty terms added to the objective function, and training is done using gradient descent. Adaptive and robust procedures are devised for balancing the relative strengths of the various terms in the objective function, which is essential when the constraints are inconsistent with the data. The approach has the virtue that the model domain of validity can be extended via extrapolation training, which can dramatically improve generalization. The algorithm is demonstrated here on artificial and real-world problems with very good results and has been advantageously applied to dozens of models currently in commercial use.

Selection of hidden layer nodes in neural networks by statistical tests

International Nuclear Information System (INIS)

Ciftcioglu, Ozer

1992-05-01

A statistical methodology for selection of the number of hidden layer nodes in feedforward neural networks is described. The method considers the network as an empirical model for the experimental data set subject to pattern classification so that the selection process becomes a model estimation through parameter identification. The solution is performed for an overdetermined estimation problem for identification using nonlinear least squares minimization technique. The number of the hidden layer nodes is determined as result of hypothesis testing. Accordingly the redundant network structure with respect to the number of parameters is avoided and the classification error being kept to a minimum. (author). 11 refs.; 4 figs.; 1 tab

Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network

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Trong-Ngoc Le

2016-01-01

Full Text Available Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN, which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the “ground truth.” Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.

Time series prediction by feedforward neural networks - is it difficult?

International Nuclear Information System (INIS)

Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang

2003-01-01

The difficulties that a neural network faces when trying to learn from a quasi-periodic time series are studied analytically using a teacher-student scenario where the random input is divided into two macroscopic regions with different variances, 1 and 1/γ 2 (γ >> 1). The generalization error is found to decrease as ε g ∝ exp(-α/γ 2 ), where α is the number of examples per input dimension. In contradiction to this very slow vanishing generalization error, the next output prediction is found to be almost free of mistakes. This picture is consistent with learning quasi-periodic time series produced by feedforward neural networks, which is dominated by enhanced components of the Fourier spectrum of the input. Simulation results are in good agreement with the analytical results

Neural dynamics of feedforward and feedback processing in figure-ground segregation.

Science.gov (United States)

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.

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.

Neural dynamics of feedforward and feedback processing in figure-ground segregation

Science.gov (United States)

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

Prediction of metal corrosion using feed-forward neural networks

International Nuclear Information System (INIS)

Mahjani, M.G.; Jalili, S.; Jafarian, M.; Jaberi, A.

2004-01-01

The reliable prediction of corrosion behavior for the effective control of corrosion is a fundamental requirement. Since real world corrosion never seems to involve quite the same conditions that have previously been tested, using corrosion literature does not provide the necessary answers. In order to provide a methodology for predicting corrosion in real and complex situations, artificial neural networks can be utilized. Feed-forward artificial neural network (FFANN) is an information-processing paradigm inspired by the way the densely interconnected, parallel structure of the human brain process information.The aim of the present work is to predict corrosion behavior in critical conditions, such as industrial applications, based on some laboratory experimental data. Electrochemical behavior of stainless steel in different conditions were studied, using polarization technique and Tafel curves. Back-propagation neural networks models were developed to predict the corrosion behavior. The trained networks result in predicted value in good comparison to the experimental data. They have generally been claimed to be successful in modeling the corrosion behavior. The results are presented in two tables. Table 1 gives corrosion behavior of stainless-steel as a function of pH and CuSO 4 concentration and table 2 gives corrosion behavior of stainless - steel as a function of electrode surface area and CuSO 4 concentration. (authors)

Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise

OpenAIRE

Mehrshad Salmasi; Homayoun Mahdavi-Nasab

2012-01-01

Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in n...

Modular representation of layered neural networks.

Science.gov (United States)

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

Science.gov (United States)

Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

2016-06-01

There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

Neural Network Based Model of an Industrial Oil-Fired Boiler System ...

African Journals Online (AJOL)

A two-layer feed-forward neural network with Hyperbolic tangent sigmoid ... The neural network model when subjected to test, using the validation input data; ... Proportional Integral Derivative (PID) Controller is used to control the neural ...

A SIMULATION OF THE PENICILLIN G PRODUCTION BIOPROCESS APPLYING NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

A.J.G. da Cruz

1997-12-01

Full Text Available The production of penicillin G by Penicillium chrysogenum IFO 8644 was simulated employing a feedforward neural network with three layers. The neural network training procedure used an algorithm combining two procedures: random search and backpropagation. The results of this approach were very promising, and it was observed that the neural network was able to accurately describe the nonlinear behavior of the process. Besides, the results showed that this technique can be successfully applied to control process algorithms due to its long processing time and its flexibility in the incorporation of new data

Feed-forward segmentation of figure-ground and assignment of border-ownership.

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Hans Supèr

Full Text Available Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment.

Feed-forward segmentation of figure-ground and assignment of border-ownership.

Science.gov (United States)

Supèr, Hans; Romeo, August; Keil, Matthias

2010-05-19

Figure-ground is the segmentation of visual information into objects and their surrounding backgrounds. Two main processes herein are boundary assignment and surface segregation, which rely on the integration of global scene information. Recurrent processing either by intrinsic horizontal connections that connect surrounding neurons or by feedback projections from higher visual areas provide such information, and are considered to be the neural substrate for figure-ground segmentation. On the contrary, a role of feedforward projections in figure-ground segmentation is unknown. To have a better understanding of a role of feedforward connections in figure-ground organization, we constructed a feedforward spiking model using a biologically plausible neuron model. By means of surround inhibition our simple 3-layered model performs figure-ground segmentation and one-sided border-ownership coding. We propose that the visual system uses feed forward suppression for figure-ground segmentation and border-ownership assignment.

Prior-knowledge-based feedforward network simulation of true boiling point curve of crude oil.

Science.gov (United States)

Chen, C W; Chen, D Z

2001-11-01

Theoretical results and practical experience indicate that feedforward networks can approximate a wide class of functional relationships very well. This property is exploited in modeling chemical processes. Given finite and noisy training data, it is important to encode the prior knowledge in neural networks to improve the fit precision and the prediction ability of the model. In this paper, as to the three-layer feedforward networks and the monotonic constraint, the unconstrained method, Joerding's penalty function method, the interpolation method, and the constrained optimization method are analyzed first. Then two novel methods, the exponential weight method and the adaptive method, are proposed. These methods are applied in simulating the true boiling point curve of a crude oil with the condition of increasing monotonicity. The simulation experimental results show that the network models trained by the novel methods are good at approximating the actual process. Finally, all these methods are discussed and compared with each other.

A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

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M. Njah

2017-06-01

Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

Science.gov (United States)

Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

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Haizhou Wu

2016-01-01

Full Text Available Symbiotic organisms search (SOS is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs. In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.

neural network based model o work based model of an industrial oil

African Journals Online (AJOL)

eobe

technique. g, Neural Network Model, Regression, Mean Square Error, PID controller. ... during the training processes. An additio ... used to carry out simulation studies of the mode .... A two-layer feed-forward neural network with Matlab.

Boundedness and convergence of online gradient method with penalty for feedforward neural networks.

Science.gov (United States)

Zhang, Huisheng; Wu, Wei; Liu, Fei; Yao, Mingchen

2009-06-01

In this brief, we consider an online gradient method with penalty for training feedforward neural networks. Specifically, the penalty is a term proportional to the norm of the weights. Its roles in the method are to control the magnitude of the weights and to improve the generalization performance of the network. By proving that the weights are automatically bounded in the network training with penalty, we simplify the conditions that are required for convergence of online gradient method in literature. A numerical example is given to support the theoretical analysis.

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits

Science.gov (United States)

2018-01-01

Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures—recurrent connections, shared feed-forward projections, and shared gain fluctuations—on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing. PMID:29408930

Interpretation of correlated neural variability from models of feed-forward and recurrent circuits.

Directory of Open Access Journals (Sweden)

Volker Pernice

2018-02-01

Full Text Available Neural populations respond to the repeated presentations of a sensory stimulus with correlated variability. These correlations have been studied in detail, with respect to their mechanistic origin, as well as their influence on stimulus discrimination and on the performance of population codes. A number of theoretical studies have endeavored to link network architecture to the nature of the correlations in neural activity. Here, we contribute to this effort: in models of circuits of stochastic neurons, we elucidate the implications of various network architectures-recurrent connections, shared feed-forward projections, and shared gain fluctuations-on the stimulus dependence in correlations. Specifically, we derive mathematical relations that specify the dependence of population-averaged covariances on firing rates, for different network architectures. In turn, these relations can be used to analyze data on population activity. We examine recordings from neural populations in mouse auditory cortex. We find that a recurrent network model with random effective connections captures the observed statistics. Furthermore, using our circuit model, we investigate the relation between network parameters, correlations, and how well different stimuli can be discriminated from one another based on the population activity. As such, our approach allows us to relate properties of the neural circuit to information processing.

Potential usefulness of an artificial neural network for assessing ventricular size

International Nuclear Information System (INIS)

Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

1995-01-01

An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

Character Recognition Using Genetically Trained Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

Feature to prototype transition in neural networks

Science.gov (United States)

Krotov, Dmitry; Hopfield, John

Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

Universal approximation in p-mean by neural networks

NARCIS (Netherlands)

Burton, R.M; Dehling, H.G

A feedforward neural net with d input neurons and with a single hidden layer of n neurons is given by [GRAPHICS] where a(j), theta(j), w(ji) is an element of R. In this paper we study the approximation of arbitrary functions f: R-d --> R by a neural net in an L-p(mu) norm for some finite measure mu

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

Science.gov (United States)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Controlling the chaotic discrete-Hénon system using a feedforward neural network with an adaptive learning rate

OpenAIRE

GÖKCE, Kürşad; UYAROĞLU, Yılmaz

2013-01-01

This paper proposes a feedforward neural network-based control scheme to control the chaotic trajectories of a discrete-Hénon map in order to stay within an acceptable distance from the stable fixed point. An adaptive learning back propagation algorithm with online training is employed to improve the effectiveness of the proposed method. The simulation study carried in the discrete-Hénon system verifies the validity of the proposed control system.

Force control of a magnetorheological damper using an elementary hysteresis model-based feedforward neural network

International Nuclear Information System (INIS)

Ekkachai, Kittipong; Nilkhamhang, Itthisek; Tungpimolrut, Kanokvate

2013-01-01

An inverse controller is proposed for a magnetorheological (MR) damper that consists of a hysteresis model and a voltage controller. The force characteristics of the MR damper caused by excitation signals are represented by a feedforward neural network (FNN) with an elementary hysteresis model (EHM). The voltage controller is constructed using another FNN to calculate a suitable input signal that will allow the MR damper to produce the desired damping force. The performance of the proposed EHM-based FNN controller is experimentally compared to existing control methodologies, such as clipped-optimal control, signum function control, conventional FNN, and recurrent neural network with displacement or velocity inputs. The results show that the proposed controller, which does not require force feedback to implement, provides excellent accuracy, fast response time, and lower energy consumption. (paper)

Feedback enhances feedforward figure-ground segmentation by changing firing mode.

Science.gov (United States)

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.

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.

Neural networks within multi-core optic fibers.

Science.gov (United States)

Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

2016-07-07

Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

Science.gov (United States)

DeMarse, Thomas B; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J; Wheeler, Bruce C

2016-01-01

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized

Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays.

Science.gov (United States)

Mejias, Jorge F; Payeur, Alexandre; Selin, Erik; Maler, Leonard; Longtin, André

2014-01-01

The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry-also known as "open-loop feedback"-, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves) via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.

Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays

Directory of Open Access Journals (Sweden)

Jorge F Mejias

2014-02-01

Full Text Available The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry — also known as ’open-loop feedback’ —, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different regimes of gain control: subtractive, divisive, and non-monotonic. Subtractive gain control was obtained when noise is very low in the network. Also, it was possible to change from divisive to non-monotonic gain control by simply modulating the strength of the feedforward inhibition, which may be achieved via long-term synaptic plasticity. The particular case of divisive gain control has been previously observed in vivo in weakly electric fish. These gain control regimes were robust to the presence of temporal delays in the inhibitory feedforward pathway, which were found to linearize the input-to-output mappings (or f-I curves via a novel variability-increasing mechanism. Our findings highlight the feedforward-induced gain control analyzed here as a highly versatile mechanism of information gating in the brain.

Introduction to Artificial Neural Networks

DEFF Research Database (Denmark)

Larsen, Jan

1999-01-01

The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

Science.gov (United States)

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

2017-01-01

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

Learning and Generalisation in Neural Networks with Local Preprocessing

OpenAIRE

Kutsia, Merab

2007-01-01

We study learning and generalisation ability of a specific two-layer feed-forward neural network and compare its properties to that of a simple perceptron. The input patterns are mapped nonlinearly onto a hidden layer, much larger than the input layer, and this mapping is either fixed or may result from an unsupervised learning process. Such preprocessing of initially uncorrelated random patterns results in the correlated patterns in the hidden layer. The hidden-to-output mapping of the net...

Temporal neural networks and transient analysis of complex engineering systems

Science.gov (United States)

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.

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

Science.gov (United States)

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.

Artificial Neural Networks to Detect Risk of Type 2 Diabetes | Baha ...

African Journals Online (AJOL)

A multilayer feedforward architecture with backpropagation algorithm was designed using Neural Network Toolbox of Matlab. The network was trained using batch mode backpropagation with gradient descent and momentum. Best performed network identified during the training was 2 hidden layers of 6 and 3 neurons, ...

Subtractive, divisive and non-monotonic gain control in feedforward nets linearized by noise and delays

OpenAIRE

Mejias, Jorge F.; Payeur, Alexandre; Selin, Erik; Maler, Leonard; Longtin, André

2014-01-01

The control of input-to-output mappings, or gain control, is one of the main strategies used by neural networks for the processing and gating of information. Using a spiking neural network model, we studied the gain control induced by a form of inhibitory feedforward circuitry—also known as “open-loop feedback”—, which has been experimentally observed in a cerebellum-like structure in weakly electric fish. We found, both analytically and numerically, that this network displays three different...

Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Petr Maca

2016-01-01

Full Text Available The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI and the standardized precipitation evaporation index (SPEI and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

Bringing Interpretability and Visualization with Artificial Neural Networks

Science.gov (United States)

Gritsenko, Andrey

2017-01-01

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…

Forecasting performances of three automated modelling techniques during the economic crisis 2007-2009

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

2014-01-01

. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four......In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact...... that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem...

Feedforward inhibition and synaptic scaling--two sides of the same coin?

Science.gov (United States)

Keck, Christian; Savin, Cristina; Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

Feedforward inhibition and synaptic scaling--two sides of the same coin?

Directory of Open Access Journals (Sweden)

Christian Keck

Full Text Available Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing.

Ensemble learning in fixed expansion layer networks for mitigating catastrophic forgetting.

Science.gov (United States)

Coop, Robert; Mishtal, Aaron; Arel, Itamar

2013-10-01

Catastrophic forgetting is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward neural networks, arises when nonstationary inputs lead to loss of previously learned mappings. The majority of the schemes proposed in the literature for mitigating catastrophic forgetting were not data driven and did not scale well. We introduce the fixed expansion layer (FEL) feedforward neural network, which embeds a sparsely encoding hidden layer to help mitigate forgetting of prior learned representations. In addition, we investigate a novel framework for training ensembles of FEL networks, based on exploiting an information-theoretic measure of diversity between FEL learners, to further control undesired plasticity. The proposed methodology is demonstrated on a basic classification task, clearly emphasizing its advantages over existing techniques. The architecture proposed can be enhanced to address a range of computational intelligence tasks, such as regression problems and system control.

Noise-enhanced categorization in a recurrently reconnected neural network

International Nuclear Information System (INIS)

Monterola, Christopher; Zapotocky, Martin

2005-01-01

We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails

Noise-enhanced categorization in a recurrently reconnected neural network

Science.gov (United States)

Monterola, Christopher; Zapotocky, Martin

2005-03-01

We investigate the interplay of recurrence and noise in neural networks trained to categorize spatial patterns of neural activity. We develop the following procedure to demonstrate how, in the presence of noise, the introduction of recurrence permits to significantly extend and homogenize the operating range of a feed-forward neural network. We first train a two-level perceptron in the absence of noise. Following training, we identify the input and output units of the feed-forward network, and thus convert it into a two-layer recurrent network. We show that the performance of the reconnected network has features reminiscent of nondynamic stochastic resonance: the addition of noise enables the network to correctly categorize stimuli of subthreshold strength, with optimal noise magnitude significantly exceeding the stimulus strength. We characterize the dynamics leading to this effect and contrast it to the behavior of a more simple associative memory network in which noise-mediated categorization fails.

Fastest learning in small-world neural networks

International Nuclear Information System (INIS)

Simard, D.; Nadeau, L.; Kroeger, H.

2005-01-01

We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

An H(∞) control approach to robust learning of feedforward neural networks.

Science.gov (United States)

Jing, Xingjian

2011-09-01

A novel H(∞) robust control approach is proposed in this study to deal with the learning problems of feedforward neural networks (FNNs). The analysis and design of a desired weight update law for the FNN is transformed into a robust controller design problem for a discrete dynamic system in terms of the estimation error. The drawbacks of some existing learning algorithms can therefore be revealed, especially for the case that the output data is fast changing with respect to the input or the output data is corrupted by noise. Based on this approach, the optimal learning parameters can be found by utilizing the linear matrix inequality (LMI) optimization techniques to achieve a predefined H(∞) "noise" attenuation level. Several existing BP-type algorithms are shown to be special cases of the new H(∞)-learning algorithm. Theoretical analysis and several examples are provided to show the advantages of the new method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Natural Language Processing with Small Feed-Forward Networks

OpenAIRE

Botha, Jan A.; Pitler, Emily; Ma, Ji; Bakalov, Anton; Salcianu, Alex; Weiss, David; McDonald, Ryan; Petrov, Slav

2017-01-01

We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models. Motivated by resource-constrained environments like mobile phones, we showcase simple techniques for obtaining such small neural network models, and investigate different tradeoffs when deciding how to allocate a small memory...

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Science.gov (United States)

Lücke, Jörg

2012-01-01

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing. PMID:22457610

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

International Nuclear Information System (INIS)

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

2008-01-01

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

Biomechanical constraints on the feedforward regulation of endpoint stiffness.

Science.gov (United States)

Hu, Xiao; Murray, Wendy M; Perreault, Eric J

2012-10-01

Although many daily tasks tend to destabilize arm posture, it is still possible to have stable interactions with the environment by regulating the multijoint mechanics of the arm in a task-appropriate manner. For postural tasks, this regulation involves the appropriate control of endpoint stiffness, which represents the stiffness of the arm at the hand. Although experimental studies have been used to evaluate endpoint stiffness control, including the orientation of maximal stiffness, the underlying neural strategies remain unknown. Specifically, the relative importance of feedforward and feedback mechanisms has yet to be determined due to the difficulty separately identifying the contributions of these mechanisms in human experiments. This study used a previously validated three-dimensional musculoskeletal model of the arm to quantify the degree to which the orientation of maximal endpoint stiffness could be changed using only steady-state muscle activations, used to represent feedforward motor commands. Our hypothesis was that the feedforward control of endpoint stiffness orientation would be significantly constrained by the biomechanical properties of the musculoskeletal system. Our results supported this hypothesis, demonstrating substantial biomechanical constraints on the ability to regulate endpoint stiffness throughout the workspace. The ability to regulate stiffness orientation was further constrained by additional task requirements, such as the need to support the arm against gravity or exert forces on the environment. Together, these results bound the degree to which slowly varying feedforward motor commands can be used to regulate the orientation of maximum arm stiffness and provide a context for better understanding conditions in which feedback control may be needed.

A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

Directory of Open Access Journals (Sweden)

Zhekang Dong

2014-01-01

Full Text Available In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

A novel memristive multilayer feedforward small-world neural network with its applications in PID control.

Science.gov (United States)

Dong, Zhekang; Duan, Shukai; Hu, Xiaofang; Wang, Lidan; Li, Hai

2014-01-01

In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme.

Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise.

Science.gov (United States)

Vuković, Najdan; Miljković, Zoran

2015-03-01

Feedforward neural networks (FFNN) are among the most used neural networks for modeling of various nonlinear problems in engineering. In sequential and especially real time processing all neural networks models fail when faced with outliers. Outliers are found across a wide range of engineering problems. Recent research results in the field have shown that to avoid overfitting or divergence of the model, new approach is needed especially if FFNN is to run sequentially or in real time. To accommodate limitations of FFNN when training data contains a certain number of outliers, this paper presents new learning algorithm based on improvement of conventional extended Kalman filter (EKF). Extended Kalman filter robust to outliers (EKF-OR) is probabilistic generative model in which measurement noise covariance is not constant; the sequence of noise measurement covariance is modeled as stochastic process over the set of symmetric positive-definite matrices in which prior is modeled as inverse Wishart distribution. In each iteration EKF-OR simultaneously estimates noise estimates and current best estimate of FFNN parameters. Bayesian framework enables one to mathematically derive expressions, while analytical intractability of the Bayes' update step is solved by using structured variational approximation. All mathematical expressions in the paper are derived using the first principles. Extensive experimental study shows that FFNN trained with developed learning algorithm, achieves low prediction error and good generalization quality regardless of outliers' presence in training data. Copyright © 2014 Elsevier Ltd. All rights reserved.

Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

Directory of Open Access Journals (Sweden)

Jing Yang

2016-07-01

Full Text Available The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

Synchronization and Inter-Layer Interactions of Noise-Driven Neural Networks.

Science.gov (United States)

Yuniati, Anis; Mai, Te-Lun; Chen, Chi-Ming

2017-01-01

In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a non-synchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, grid, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

Effects of topologies on signal propagation in feedforward networks

Science.gov (United States)

Zhao, Jia; Qin, Ying-Mei; Che, Yan-Qiu

2018-01-01

We systematically investigate the effects of topologies on signal propagation in feedforward networks (FFNs) based on the FitzHugh-Nagumo neuron model. FFNs with different topological structures are constructed with same number of both in-degrees and out-degrees in each layer and given the same input signal. The propagation of firing patterns and firing rates are found to be affected by the distribution of neuron connections in the FFNs. Synchronous firing patterns emerge in the later layers of FFNs with identical, uniform, and exponential degree distributions, but the number of synchronous spike trains in the output layers of the three topologies obviously differs from one another. The firing rates in the output layers of the three FFNs can be ordered from high to low according to their topological structures as exponential, uniform, and identical distributions, respectively. Interestingly, the sequence of spiking regularity in the output layers of the three FFNs is consistent with the firing rates, but their firing synchronization is in the opposite order. In summary, the node degree is an important factor that can dramatically influence the neuronal network activity.

Feed-Forward Neural Networks and Minimal Search Space Learning

Czech Academy of Sciences Publication Activity Database

Neruda, Roman

2005-01-01

Roč. 4, č. 12 (2005), s. 1867-1872 ISSN 1109-2750 R&D Projects: GA ČR GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : search space * feed-forward networks * genetic algorithm s Subject RIV: BA - General Mathematics

A new backpropagation learning algorithm for layered neural networks with nondifferentiable units.

Science.gov (United States)

Oohori, Takahumi; Naganuma, Hidenori; Watanabe, Kazuhisa

2007-05-01

We propose a digital version of the backpropagation algorithm (DBP) for three-layered neural networks with nondifferentiable binary units. This approach feeds teacher signals to both the middle and output layers, whereas with a simple perceptron, they are given only to the output layer. The additional teacher signals enable the DBP to update the coupling weights not only between the middle and output layers but also between the input and middle layers. A neural network based on DBP learning is fast and easy to implement in hardware. Simulation results for several linearly nonseparable problems such as XOR demonstrate that the DBP performs favorably when compared to the conventional approaches. Furthermore, in large-scale networks, simulation results indicate that the DBP provides high performance.

DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging

International Nuclear Information System (INIS)

Barhen, J.; Protopopescu, V.; Reister, D.

1999-01-01

A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

The intention of this report 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: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

Sequential neural models with stochastic layers

DEFF Research Database (Denmark)

Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

2016-01-01

How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

Energy Technology Data Exchange (ETDEWEB)

Ammi, Yamina; Khaouane, Latifa; Hanini, Salah [University of Medea, Medea (Algeria)

2015-11-15

This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

International Nuclear Information System (INIS)

Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

2015-01-01

This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

Coordinated three-dimensional motion of the head and torso by dynamic neural networks.

Science.gov (United States)

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.

Moving image compression and generalization capability of constructive neural networks

Science.gov (United States)

Ma, Liying; Khorasani, Khashayar

2001-03-01

To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks NNs have been developed. Particularly, several constructive feed-forward neural networks FNNs have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference 2000, we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network OHL-FNN for compressing digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/ or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images, video sequences. The first, or other specified frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a football game.

Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia, E-mail: claubia@nuclear.ufmg.br [Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG (Brazil). Programa de Pós-Graduação em Ciências e Técnicas Nucleares

2017-07-01

An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k{sub ef{sub f}}. The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi{sub L}ayer{sub P}erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

Artificial neural networks for spatial distribution of fuel assemblies in reload of PWR reactors

International Nuclear Information System (INIS)

Oliveira, Edyene; Castro, Victor F.; Velásquez, Carlos E.; Pereira, Claubia

2017-01-01

An artificial neural network methodology is being developed in order to find an optimum spatial distribution of the fuel assemblies in a nuclear reactor core during reload. The main bounding parameter of the modelling was the neutron multiplication factor, k ef f . The characteristics of the network are defined by the nuclear parameters: cycle, burnup, enrichment, fuel type, and average power peak of each element. These parameters were obtained by the ORNL nuclear code package SCALE6.0. As for the artificial neural network, the ANN Feedforward Multi L ayer P erceptron with various layers and neurons were constructed. Three algorithms were used and tested: LM (Levenberg-Marquardt), SCG (Scaled Conjugate Gradient) and BayR (Bayesian Regularization). Artificial neural network have implemented using MATLAB 2015a version. As preliminary results, the spatial distribution of the fuel assemblies in the core using a neural network was slightly better than the standard core. (author)

A Feed-Forward Control Realizing Fast Response for Three-Branch Interleaved DC-DC Converter in DC Microgrid

Directory of Open Access Journals (Sweden)

Haojie Wang

2016-07-01

Full Text Available It is a common practice for storage batteries to be connected to DC microgrid buses through DC-DC converters for voltage support on islanded operation mode. A feed-forward control based dual-loop constant voltage PI control for three-branch interleaved DC-DC converters (TIDC is proposed for storage batteries in DC microgrids. The working principle of TIDC is analyzed, and the factors influencing the response rate based on the dual-loop constant voltage control for TIDC are discussed, and then the method of feed-forward control for TIDC is studied to improve the response rate for load changing. A prototype of the TIDC is developed and an experimental platform is built. The experiment results show that DC bus voltage sags or swells caused by load changing can be reduced and the time for voltage recovery can be decreased significantly with the proposed feed-forward control.

A Feed-Forward Control Realizing Fast Response for Three-Branch Interleaved DC-DC Converter in DC Microgrid

DEFF Research Database (Denmark)

Wang, Haojie; Han, Minxiao; Yan, Wenli

2016-01-01

It is a common practice for storage batteries to be connected to DC microgrid buses through DC-DC converters for voltage support on islanded operation mode. A feed-forward control based dual-loop constant voltage PI control for three-branch interleaved DC-DC converters (TIDC) is proposed...... for storage batteries in DC microgrids. The working principle of TIDC is analyzed, and the factors influencing the response rate based on the dual-loop constant voltage control for TIDC are discussed, and then the method of feed-forward control for TIDC is studied to improve the response rate for load...... changing. A prototype of the TIDC is developed and an experimental platform is built. The experiment results show that DC bus voltage sags or swells caused by load changing can be reduced and the time for voltage recovery can be decreased significantly with the proposed feed-forward control....

Short-term electricity prices forecasting in a competitive market: A neural network approach

International Nuclear Information System (INIS)

Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

2007-01-01

This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

Prediction of geomagnetic storm using neural networks: Comparison of the efficiency of the Satellite and ground-based input parameters

International Nuclear Information System (INIS)

Stepanova, Marina; Antonova, Elizavieta; Munos-Uribe, F A; Gordo, S L Gomez; Torres-Sanchez, M V

2008-01-01

Different kinds of neural networks have established themselves as an effective tool in the prediction of different geomagnetic indices, including the Dst being the most important constituent for determination of the impact of Space Weather on the human life. Feed-forward networks with one hidden layer are used to forecast the Dst variation, using separately the solar wind paramenters, polar cap index, and auroral electrojet index as input parameters. It was found that in all three cases the storm-time intervals were predicted much more precisely as quite time intervals. The majority of cross-correlation coefficients between predicted and observed Dst of strong geomagnetic storms are situated between 0.8 and 0.9. Changes in the neural network architecture, including the number of nodes in the input and hidden layers and the transfer functions between them lead to an improvement of a network performance up to 10%.

A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems.

Science.gov (United States)

Kuntanapreeda, S; Fullmer, R R

1996-01-01

A training method for a class of neural network controllers is presented which guarantees closed-loop system stability. The controllers are assumed to be nonlinear, feedforward, sampled-data, full-state regulators implemented as single hidden-layer neural networks. The controlled systems must be locally hermitian and observable. Stability of the closed-loop system is demonstrated by determining a Lyapunov function, which can be used to identify a finite stability region about the regulator point.

Application of a feedforward neural network in the search for kuroko deposits in the hokuroku district, Japan

Science.gov (United States)

Singer, D.A.; Kouda, R.

1996-01-01

A feedforward neural network with one hidden layer and five neurons was trained to recognize the distance to kuroko mineral deposits. Average amounts per hole of pyrite, sericite, and gypsum plus anhydrite as measured by X-rays in 69 drillholes were used in train the net. Drillholes near and between the Fukazawa, Furutobe, and Shakanai mines were used. The training data were selected carefully to represent well-explored areas where some confidence of the distance to ore was assured. A logarithmic transform was applied to remove the skewness of distance and each variable was scaled and centered by subtracting the median and dividing by the interquartile range. The learning algorithm of annealing plus conjugate gradients was used to minimise the mean squared error of the sealed distance to ore. The trained network then was applied to all of the 152 drillholes that had measured gypsum, sericite, and pyrite. A contour plot of the neural net predicted distance to ore shows fairly wide areas of 1 km or less to ore; each of the known deposit groups is within the 1 km contour. The high and htw distances on the margins of the contoured distance plot are in part the result of boundary effects of the contouring algorithm. For example, the short distances to ore predicted west of the Shakanai (Hanaoka) deposits are in basement. However, the short distances to ore predicted northeast of Furotobe, just off the figure, coincide with the location of the Nurukawa kuroko deposit and the Omaki deposit, south of the Shakanai-Hanaoka deposits, seems to be on an extension of short distance to ore contour, but is beyond the 3 km limit from drillholes. Also of interest are some areas only a few kilometers from the Fukazawa and Shakanai groups of deposits that are estimated to be many kilometers from ore, apparently reflecting the network's recognition of the extreme local variability of the geology near some deposits. 1996 International Association for Mathematical Geology.

Feedforward neural control of toe walking in humans.

Science.gov (United States)

Lorentzen, Jakob; Willerslev-Olsen, Maria; Hüche Larsen, Helle; Svane, Christian; Forman, Christian; Frisk, Rasmus; Farmer, Simon Francis; Kersting, Uwe; Nielsen, Jens Bo

2018-03-23

Activation of ankle muscles at ground contact during toe walking is unaltered when sensory feedback is blocked or the ground is suddenly dropped. Responses in the soleus muscle to transcranial magnetic stimulation, but not peripheral nerve stimulation, are facilitated at ground contact during toe walking. We argue that toe walking is supported by feedforward control at ground contact. Toe walking requires careful control of the ankle muscles in order to absorb the impact of ground contact and maintain a stable position of the joint. The present study aimed to clarify the peripheral and central neural mechanisms involved. Fifteen healthy adults walked on a treadmill (3.0 km h -1 ). Tibialis anterior (TA) and soleus (Sol) EMG, knee and ankle joint angles, and gastrocnemius-soleus muscle fascicle lengths were recorded. Peripheral and central contributions to the EMG activity were assessed by afferent blockade, H-reflex testing, transcranial magnetic brain stimulation (TMS) and sudden unloading of the planter flexor muscle-tendon complex. Sol EMG activity started prior to ground contact and remained high throughout stance. TA EMG activity, which is normally seen around ground contact during heel strike walking, was absent. Although stretch of the Achilles tendon-muscle complex was observed after ground contact, this was not associated with lengthening of the ankle plantar flexor muscle fascicles. Sol EMG around ground contact was not affected by ischaemic blockade of large-diameter sensory afferents, or the sudden removal of ground support shortly after toe contact. Soleus motor-evoked potentials elicited by TMS were facilitated immediately after ground contact, whereas Sol H-reflexes were not. These findings indicate that at the crucial time of ankle stabilization following ground contact, toe walking is governed by centrally mediated motor drive rather than sensory driven reflex mechanisms. These findings have implications for our understanding of the control of

Estimation of operational parameters for a direct injection turbocharged spark ignition engine by using regression analysis and artificial neural network

Directory of Open Access Journals (Sweden)

Tosun Erdi

2017-01-01

Full Text Available This study was aimed at estimating the variation of several engine control parameters within the rotational speed-load map, using regression analysis and artificial neural network techniques. Duration of injection, specific fuel consumption, exhaust gas at turbine inlet, and within the catalytic converter brick were chosen as the output parameters for the models, while engine speed and brake mean effective pressure were selected as independent variables for prediction. Measurements were performed on a turbocharged direct injection spark ignition engine fueled with gasoline. A three-layer feed-forward structure and back-propagation algorithm was used for training the artificial neural network. It was concluded that this technique is capable of predicting engine parameters with better accuracy than linear and non-linear regression techniques.

Artificial neural networks for modeling time series of beach litter in the southern North Sea.

Science.gov (United States)

Schulz, Marcus; Matthies, Michael

2014-07-01

In European marine waters, existing monitoring programs of beach litter need to be improved concerning litter items used as indicators of pollution levels, efficiency, and effectiveness. In order to ease and focus future monitoring of beach litter on few important litter items, feed-forward neural networks consisting of three layers were developed to relate single litter items to general categories of marine litter. The neural networks developed were applied to seven beaches in the southern North Sea and modeled time series of five general categories of marine litter, such as litter from fishing, shipping, and tourism. Results of regression analyses show that general categories were predicted significantly moderately to well. Measured and modeled data were in the same order of magnitude, and minima and maxima overlapped well. Neural networks were found to be eligible tools to deliver reliable predictions of marine litter with low computational effort and little input of information. Copyright © 2014 Elsevier Ltd. All rights reserved.

Feedforward Control of a 3-D Physiological Articulatory Model for Vowel Production

Institute of Scientific and Technical Information of China (English)

FANG Qiang; Akikazu Nishikido; Jianwu Dang

2009-01-01

A three-dimensional (3-D) physiological articulatory model was developed to account for the bio-mechanical properties of the speech organs in speech production. Control of the model to investigate the mechanism of speech production requires an efficient control module to estimate muscle activation patterns, which is used to manipulate the 3-D physiological arUculatory model, according to the desired articulatory posture. For this purpose, a feedforward control strategy was developed by mapping the articulatory target to the corresponding muscle activation pattern via the intrinsic representation of vowel articulation. In this process, the articulatory postures are first mapped to the corresponding intrinsic representations; then, the articulatory postures are clustered in the intrinsic representations space and a nonlinear function is ap-proximated for each cluster to map the intrinsic representation of vowel articulation to the muscle activation pattern by using general regression neural networks (GRNN). The results show that the feedforward control module is able to manipulate the 3-D physiological articulatory model for vowel production with high accu-racy both acoustically and articulatodly.

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...

Control of a local neural network by feedforward and feedback inhibition

NARCIS (Netherlands)

Remme, M.W.H.; Wadman, W.J.

2004-01-01

The signal transfer of a neuronal network is shaped by the local interactions between the excitatory principal cells and the inhibitory interneurons. We investigated with a simple lumped model how feedforward and feedback inhibition in.uence the steady-state network signal transfer. We analyze how

Neural estimation of kinetic rate constants from dynamic PET-scans

DEFF Research Database (Denmark)

Fog, Torben L.; Nielsen, Lars Hupfeldt; Hansen, Lars Kai

1994-01-01

A feedforward neural net is trained to invert a simple three compartment model describing the tracer kinetics involved in the metabolism of [18F]fluorodeoxyglucose in the human brain. The network can estimate rate constants from positron emission tomography sequences and is about 50 times faster ...

An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

International Nuclear Information System (INIS)

Avci, E.

2007-01-01

In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics.

Science.gov (United States)

Catic, Aida; Gurbeta, Lejla; Kurtovic-Kozaric, Amina; Mehmedbasic, Senad; Badnjevic, Almir

2018-02-13

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology "Mehmedbasic" for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman's) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for

Direct adaptive control using feedforward neural networks

OpenAIRE

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...

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

Science.gov (United States)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

A novel method to produce nonlinear empirical physical formulas for experimental nonlinear electro-optical responses of doped nematic liquid crystals: Feedforward neural network approach

Energy Technology Data Exchange (ETDEWEB)

Yildiz, Nihat, E-mail: nyildiz@cumhuriyet.edu.t [Cumhuriyet University, Faculty of Science and Literature, Department of Physics, 58140 Sivas (Turkey); San, Sait Eren; Okutan, Mustafa [Department of Physics, Gebze Institute of Technology, P.O. Box 141, Gebze 41400, Kocaeli (Turkey); Kaya, Hueseyin [Cumhuriyet University, Faculty of Science and Literature, Department of Physics, 58140 Sivas (Turkey)

2010-04-15

Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs.

A novel method to produce nonlinear empirical physical formulas for experimental nonlinear electro-optical responses of doped nematic liquid crystals: Feedforward neural network approach

International Nuclear Information System (INIS)

Yildiz, Nihat; San, Sait Eren; Okutan, Mustafa; Kaya, Hueseyin

2010-01-01

Among other significant obstacles, inherent nonlinearity in experimental physical response data poses severe difficulty in empirical physical formula (EPF) construction. In this paper, we applied a novel method (namely layered feedforward neural network (LFNN) approach) to produce explicit nonlinear EPFs for experimental nonlinear electro-optical responses of doped nematic liquid crystals (NLCs). Our motivation was that, as we showed in a previous theoretical work, an appropriate LFNN, due to its exceptional nonlinear function approximation capabilities, is highly relevant to EPF construction. Therefore, in this paper, we obtained excellently produced LFNN approximation functions as our desired EPFs for above-mentioned highly nonlinear response data of NLCs. In other words, by using suitable LFNNs, we successfully fitted the experimentally measured response and predicted the new (yet-to-be measured) response data. The experimental data (response versus input) were diffraction and dielectric properties versus bias voltage; and they were all taken from our previous experimental work. We conclude that in general, LFNN can be applied to construct various types of EPFs for the corresponding various nonlinear physical perturbation (thermal, electronic, molecular, electric, optical, etc.) data of doped NLCs.

Feedback Enhances Feedforward Figure-Ground Segmentation by Changing Firing Mode

Science.gov (United States)

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

A neural network device for on-line particle identification in cosmic ray experiments

International Nuclear Information System (INIS)

Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

2004-01-01

On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

Artificial neural network based approach to transmission lines protection

International Nuclear Information System (INIS)

Joorabian, M.

1999-05-01

The aim of this paper is to present and accurate fault detection technique for high speed distance protection using artificial neural networks. The feed-forward multi-layer neural network with the use of supervised learning and the common training rule of error back-propagation is chosen for this study. Information available locally at the relay point is passed to a neural network in order for an assessment of the fault location to be made. However in practice there is a large amount of information available, and a feature extraction process is required to reduce the dimensionality of the pattern vectors, whilst retaining important information that distinguishes the fault point. The choice of features is critical to the performance of the neural networks learning and operation. A significant feature in this paper is that an artificial neural network has been designed and tested to enhance the precision of the adaptive capabilities for distance protection

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

Combined feedforward and feedback control of a redundant, nonlinear, dynamic musculoskeletal system.

Science.gov (United States)

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.

Asymmetric Temporal Integration of Layer 4 and Layer 2/3 Inputs in Visual Cortex

OpenAIRE

Hang, Giao B.; Dan, Yang

2010-01-01

Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices...

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.

Comparative experiments regarding approaches to feedforward hysteresis compensation for piezoceramic actuators

International Nuclear Information System (INIS)

Gu, Guo-Ying; Zhu, Li-Min

2014-01-01

Piezoceramic actuators (PCAs) are desired devices in many micro/nano-positioning applications. The performance of PCA-based applications is severely limited by the presence of hysteresis nonlinearity. To remedy the hysteresis nonlinearity in such systems, feedforward hysteresis compensation is the most common technique. In the literature, many different feedforward hysteresis compensation approaches have been developed, but there are no comparative studies of these approaches. Focusing on the modified Prandtl-Ishlinskii model (MPIM) for asymmetric hysteresis description of piezoceramic actuators, three feedforward hysteresis compensation approaches—inverse hysteresis compensation (IHC), without inverse hysteresis compensation (WIHC), and direct inverse hysteresis compensation (DIHC)—are developed and compared in this paper. Extensive comparative experiments were conducted on a PCA-actuated stage to verify the effectiveness of the three different feedforward control approaches to hysteresis compensation. The experimental results show that the performances among the three approaches are rather similar, and the main differences among them are due to the specific implementation of each approach. (paper)

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

Science.gov (United States)

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.

A high-speed analog neural processor

NARCIS (Netherlands)

Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

Separation prediction in two dimensional boundary layer flows using artificial neural networks

International Nuclear Information System (INIS)

Sabetghadam, F.; Ghomi, H.A.

2003-01-01

In this article, the ability of artificial neural networks in prediction of separation in steady two dimensional boundary layer flows is studied. Data for network training is extracted from numerical solution of an ODE obtained from Von Karman integral equation with approximate one parameter Pohlhousen velocity profile. As an appropriate neural network, a two layer radial basis generalized regression artificial neural network is used. The results shows good agreements between the overall behavior of the flow fields predicted by the artificial neural network and the actual flow fields for some cases. The method easily can be extended to unsteady separation and turbulent as well as compressible boundary layer flows. (author)

New approach to ECG's features recognition involving neural network

International Nuclear Information System (INIS)

Babloyantz, A.; Ivanov, V.V.; Zrelov, P.V.

2001-01-01

A new approach for the detection of slight changes in the form of the ECG signal is proposed. It is based on the approximation of raw ECG data inside each RR-interval by the expansion in polynomials of special type and on the classification of samples represented by sets of expansion coefficients using a layered feed-forward neural network. The transformation applied provides significantly simpler data structure, stability to noise and to other accidental factors. A by-product of the method is the compression of ECG data with factor 5

A Neural Network Approach for GMA Butt Joint Welding

DEFF Research Database (Denmark)

Christensen, Kim Hardam; Sørensen, Torben

2003-01-01

This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...... penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least...

A Neural Network Approach for GMA Butt Joint Welding

DEFF Research Database (Denmark)

Christensen, Kim Hardam; Sørensen, Torben

2003-01-01

penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...

Automated optimal coordination of multiple-DOF neuromuscular actions in feedforward neuroprostheses.

Science.gov (United States)

Lujan, J Luis; Crago, Patrick E

2009-01-01

This paper describes a new method for designing feedforward controllers for multiple-muscle, multiple-DOF, motor system neural prostheses. The design process is based on experimental measurement of the forward input/output properties of the neuromechanical system and numerical optimization of stimulation patterns to meet muscle coactivation criteria, thus resolving the muscle redundancy (i.e., overcontrol) and the coupled DOF problems inherent in neuromechanical systems. We designed feedforward controllers to control the isometric forces at the tip of the thumb in two directions during stimulation of three thumb muscles as a model system. We tested the method experimentally in ten able-bodied individuals and one patient with spinal cord injury. Good control of isometric force in both DOFs was observed, with rms errors less than 10% of the force range in seven experiments and statistically significant correlations between the actual and target forces in all ten experiments. Systematic bias and slope errors were observed in a few experiments, likely due to the neuromuscular fatigue. Overall, the tests demonstrated the ability of a general design approach to satisfy both control and coactivation criteria in multiple-muscle, multiple-axis neuromechanical systems, which is applicable to a wide range of neuromechanical systems and stimulation electrodes.

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...

3-D inversion of borehole-to-surface electrical data using a back-propagation neural network

Science.gov (United States)

Ho, Trong Long

2009-08-01

The "fluid-flow tomography", an advanced technique for geoelectrical survey based on the conventional mise-à-la-masse measurement, has been developed by Exploration Geophysics Laboratory at the Kyushu University. This technique is proposed to monitor fluid-flow behavior during water injection and production in a geothermal field. However data processing of this technique is very costly. In this light, this paper will discuss the solution to cost reduction by applying a neural network in the data processing. A case study in the Takigami geothermal field in Japan will be used to illustrate this. The achieved neural network in this case study is three-layered and feed-forward. The most successful learning algorithm in this network is the Resilient Propagation (RPROP). Consequently, the study advances the pragmatism of the "fluid-flow tomography" technique which can be widely used for geothermal fields. Accuracy of the solution is then verified by using root mean square (RMS) misfit error as an indicator.

Identification of Complex Dynamical Systems with Neural Networks (2/2)

CERN Multimedia

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...

Identification of Complex Dynamical Systems with Neural Networks (1/2)

CERN Multimedia

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...

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

Science.gov (United States)

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation.

Science.gov (United States)

Klink, P Christiaan; Dagnino, Bruno; Gariel-Mathis, Marie-Alice; Roelfsema, Pieter R

2017-07-05

The visual cortex is hierarchically organized, with low-level areas coding for simple features and higher areas for complex ones. Feedforward and feedback connections propagate information between areas in opposite directions, but their functional roles are only partially understood. We used electrical microstimulation to perturb the propagation of neuronal activity between areas V1 and V4 in monkeys performing a texture-segregation task. In both areas, microstimulation locally caused a brief phase of excitation, followed by inhibition. Both these effects propagated faithfully in the feedforward direction from V1 to V4. Stimulation of V4, however, caused little V1 excitation, but it did yield a delayed suppression during the late phase of visually driven activity. This suppression was pronounced for the V1 figure representation and weaker for background representations. Our results reveal functional differences between feedforward and feedback processing in texture segregation and suggest a specific modulating role for feedback connections in perceptual organization. Copyright © 2017 Elsevier Inc. All rights reserved.

Feedforward Nonlinear Control Using Neural Gas Network

OpenAIRE

Machón-González, Iván; López-García, Hilario

2017-01-01

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 ...

Neural Dynamics of Feedforward and Feedback Processing in Figure-Ground Segregation

OpenAIRE

Oliver W. Layton; Ennio eMingolla; Arash eYazdanbakhsh

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 feedba...

Pattern Recognition and Classification of Fatal Traffic Accidents in Israel A Neural Network Approach

DEFF Research Database (Denmark)

Prato, Carlo Giacomo; Gitelman, Victoria; Bekhor, Shlomo

2011-01-01

on 1,793 fatal traffic accidents occurred during the period between 2003 and 2006 and applies Kohonen and feed-forward back-propagation neural networks with the objective of extracting from the data typical patterns and relevant factors. Kohonen neural networks reveal five compelling accident patterns....... Feed-forward back-propagation neural networks indicate that sociodemographic characteristics of drivers and victims, accident location, and period of the day are extremely relevant factors. Accident patterns suggest that countermeasures are necessary for identified problems concerning mainly vulnerable...

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

Directory of Open Access Journals (Sweden)

Jie-Sheng Wang

2015-01-01

Full Text Available For predicting the key technology indicators (concentrate grade and tailings recovery rate of flotation process, a feed-forward neural network (FNN based soft-sensor model optimized by the hybrid algorithm combining particle swarm optimization (PSO algorithm and gravitational search algorithm (GSA is proposed. Although GSA has better optimization capability, it has slow convergence velocity and is easy to fall into local optimum. So in this paper, the velocity vector and position vector of GSA are adjusted by PSO algorithm in order to improve its convergence speed and prediction accuracy. Finally, the proposed hybrid algorithm is adopted to optimize the parameters of FNN soft-sensor model. Simulation results show that the model has better generalization and prediction accuracy for the concentrate grade and tailings recovery rate to meet the online soft-sensor requirements of the real-time control in the flotation process.

Design of Artificial Neural Network-Based pH Estimator

Directory of Open Access Journals (Sweden)

Shebel A. Alsabbah

2010-10-01

Full Text Available Taking into consideration the cost, size and drawbacks might be found with real hardware instrument for measuring pH values such that the complications of the wiring, installing, calibrating and troubleshooting the system, would make a person look for a cheaper, accurate, and alternative choice to perform the measuring operation, Where’s hereby, a feedforward artificial neural network-based pH estimator has to be proposed. The proposed estimator has been designed with multi- layer perceptrons. One input which is a measured base stream and two outputs represent pH values at strong base and strong/weak acids for a titration process. The created data base has been obtained with consideration of temperature variation. The final numerical results ensure the effectiveness and robustness of the design neural network-based pH estimator.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

Science.gov (United States)

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

Modeling and inverse feedforward control for conducting polymer actuators with hysteresis

International Nuclear Information System (INIS)

Wang, Xiangjiang; Alici, Gursel; Tan, Xiaobo

2014-01-01

Conducting polymer actuators are biocompatible with a small footprint, and operate in air or liquid media under low actuation voltages. This makes them excellent actuators for macro- and micro-manipulation devices, however, their positioning ability or accuracy is adversely affected by their hysteresis non-linearity under open-loop control strategies. In this paper, we establish a hysteresis model for conducting polymer actuators, based on a rate-independent hysteresis model known as the Duhem model. The hysteresis model is experimentally identified and integrated with the linear dynamics of the actuator. This combined model is inverted to control the displacement of the tri-layer actuators considered in this study, without using any external feedback. The inversion requires an inverse hysteresis model which was experimentally identified using an inverse neural network model. Experimental results show that the position tracking errors are reduced by more than 50% when the hysteresis inverse model is incorporated into an inversion-based feedforward controller, indicating the potential of the proposed method in enabling wider use of such smart actuators. (paper)

Minimal-Inversion Feedforward-And-Feedback Control System

Science.gov (United States)

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.

Pupil size directly modulates the feedforward response in human primary visual cortex independently of attention.

Science.gov (United States)

Bombeke, Klaas; Duthoo, Wout; Mueller, Sven C; Hopf, Jens-Max; Boehler, C Nico

2016-02-15

Controversy revolves around the question of whether psychological factors like attention and emotion can influence the initial feedforward response in primary visual cortex (V1). Although traditionally, the electrophysiological correlate of this response in humans (the C1 component) has been found to be unaltered by psychological influences, a number of recent studies have described attentional and emotional modulations. Yet, research into psychological effects on the feedforward V1 response has neglected possible direct contributions of concomitant pupil-size modulations, which are known to also occur under various conditions of attentional load and emotional state. Here we tested the hypothesis that such pupil-size differences themselves directly affect the feedforward V1 response. We report data from two complementary experiments, in which we used procedures that modulate pupil size without differences in attentional load or emotion while simultaneously recording pupil-size and EEG data. Our results confirm that pupil size indeed directly influences the feedforward V1 response, showing an inverse relationship between pupil size and early V1 activity. While it is unclear in how far this effect represents a functionally-relevant adaptation, it identifies pupil-size differences as an important modulating factor of the feedforward response of V1 and could hence represent a confounding variable in research investigating the neural influence of psychological factors on early visual processing. Copyright © 2015 Elsevier Inc. All rights reserved.

An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

Science.gov (United States)

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...

Comparison between hybrid feedforward-feedback, feedforward, and feedback structures for active noise control of fMRI noise.

Science.gov (United States)

Reddy, Rajiv M; Panahi, Issa M S

2008-01-01

The performance of FIR feedforward, IIR feedforward, FIR feedback, hybrid FIR feedforward--FIR feedback, and hybrid IIR feedforward - FIR feedback structures for active noise control (ANC) are compared for an fMRI noise application. The filtered-input normalized least squares (FxNLMS) algorithm is used to update the coefficients of the adaptive filters in all these structures. Realistic primary and secondary paths of an fMRI bore are used by estimating them on a half cylindrical acrylic bore of 0.76 m (D)x1.52 m (L). Detailed results of the performance of the ANC system are presented in the paper for each of these structures. We find that the IIR feedforward structure produces most of the performance improvement in the hybrid IIR feedforward - FIR feedback structure and adding the feedback structure becomes almost redundant in the case of fMRI noise.

A One-Layer Recurrent Neural Network for Pseudoconvex Optimization Problems With Equality and Inequality Constraints.

Science.gov (United States)

Qin, Sitian; Yang, Xiudong; Xue, Xiaoping; Song, Jiahui

2017-10-01

Pseudoconvex optimization problem, as an important nonconvex optimization problem, plays an important role in scientific and engineering applications. In this paper, a recurrent one-layer neural network is proposed for solving the pseudoconvex optimization problem with equality and inequality constraints. It is proved that from any initial state, the state of the proposed neural network reaches the feasible region in finite time and stays there thereafter. It is also proved that the state of the proposed neural network is convergent to an optimal solution of the related problem. Compared with the related existing recurrent neural networks for the pseudoconvex optimization problems, the proposed neural network in this paper does not need the penalty parameters and has a better convergence. Meanwhile, the proposed neural network is used to solve three nonsmooth optimization problems, and we make some detailed comparisons with the known related conclusions. In the end, some numerical examples are provided to illustrate the effectiveness of the performance of the proposed neural network.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

Science.gov (United States)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

Science.gov (United States)

Zheng, W.; Hu, F. R.; Zhang, M.; Chen, Z. Y.; Zhao, X. Q.; Wang, X. L.; Shi, P.; Zhang, X. L.; Zhang, X. Q.; Zhou, Y. N.; Wei, Y. N.; Pan, Y.; J-TEXT team

2018-05-01

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time ({{T}warn} ). In particular, the {{T}warn} is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Selection of W-pair-production in DELPHI with feed-forward neural networks

International Nuclear Information System (INIS)

Becks, K.-H.; Buschmann, P.; Drees, J.; Mueller, U.; Wahlen, H.

2001-01-01

Since 1998 feed-forward networks have been applied for the separation of hadronic WW-decays from background processes measured by the DELPHI collaboration at different center-of-mass energies of the Large Electron Positron collider at CERN. Prior to the publication of the 189 GeV results intensive studies of systematic effects and uncertainties were performed. The methods and results will be discussed and compared to standard selection procedures

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Directory of Open Access Journals (Sweden)

Xiao-Li Li

2014-01-01

Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

Stability analysis of a three-phase grid-connected DC power supply with small DC-link capacitor and voltage feed-forward compensation

DEFF Research Database (Denmark)

Török, Lajos; Mathe, L.

2017-01-01

The purpose of this work was to investigate effect of the DC-link voltage feed-forward compensation on the stability of the three-phase-grid connected DC power supply, used for electrolysis application, equipped with small DC link capacitor. In case of weak grid condition, the system...

Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.

Science.gov (United States)

Jang, Hojin; Plis, Sergey M; Calhoun, Vince D; Lee, Jong-Hwan

2017-01-15

Feedforward deep neural networks (DNNs), artificial neural networks with multiple hidden layers, have recently demonstrated a record-breaking performance in multiple areas of applications in computer vision and speech processing. Following the success, DNNs have been applied to neuroimaging modalities including functional/structural magnetic resonance imaging (MRI) and positron-emission tomography data. However, no study has explicitly applied DNNs to 3D whole-brain fMRI volumes and thereby extracted hidden volumetric representations of fMRI that are discriminative for a task performed as the fMRI volume was acquired. Our study applied fully connected feedforward DNN to fMRI volumes collected in four sensorimotor tasks (i.e., left-hand clenching, right-hand clenching, auditory attention, and visual stimulus) undertaken by 12 healthy participants. Using a leave-one-subject-out cross-validation scheme, a restricted Boltzmann machine-based deep belief network was pretrained and used to initialize weights of the DNN. The pretrained DNN was fine-tuned while systematically controlling weight-sparsity levels across hidden layers. Optimal weight-sparsity levels were determined from a minimum validation error rate of fMRI volume classification. Minimum error rates (mean±standard deviation; %) of 6.9 (±3.8) were obtained from the three-layer DNN with the sparsest condition of weights across the three hidden layers. These error rates were even lower than the error rates from the single-layer network (9.4±4.6) and the two-layer network (7.4±4.1). The estimated DNN weights showed spatial patterns that are remarkably task-specific, particularly in the higher layers. The output values of the third hidden layer represented distinct patterns/codes of the 3D whole-brain fMRI volume and encoded the information of the tasks as evaluated from representational similarity analysis. Our reported findings show the ability of the DNN to classify a single fMRI volume based on the

Classification of Urinary Calculi using Feed-Forward Neural Networks

African Journals Online (AJOL)

NJD

Genetic algorithms were used for optimization of neural networks and for selection of the ... Urinary calculi, infrared spectroscopy, classification, neural networks, variable ..... note that the best accuracy is obtained for whewellite, weddellite.

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

Germ layers, the neural crest and emergent organization in development and evolution.

Science.gov (United States)

Hall, Brian K

2018-04-10

Discovered in chick embryos by Wilhelm His in 1868 and named the neural crest by Arthur Milnes Marshall in 1879, the neural crest cells that arise from the neural folds have since been shown to differentiate into almost two dozen vertebrate cell types and to have played major roles in the evolution of such vertebrate features as bone, jaws, teeth, visceral (pharyngeal) arches, and sense organs. I discuss the discovery that ectodermal neural crest gave rise to mesenchyme and the controversy generated by that finding; the germ layer theory maintained that only mesoderm could give rise to mesenchyme. A second topic of discussion is germ layers (including the neural crest) as emergent levels of organization in animal development and evolution that facilitated major developmental and evolutionary change. The third topic is gene networks, gene co-option, and the evolution of gene-signaling pathways as key to developmental and evolutionary transitions associated with the origin and evolution of the neural crest and neural crest cells. © 2018 Wiley Periodicals, Inc.

Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network

Directory of Open Access Journals (Sweden)

Mehmet Şimşir

2016-01-01

Full Text Available Low power hub motors are widely used in electromechanical systems such as electrical bicycles and solar vehicles due to their robustness and compact structure. Such systems driven by hub motors (in wheel motors encounter previously defined and undefined faults under operation. It may inevitably lead to the interruption of the electromechanical system operation; hence, economic losses take place at certain times. Therefore, in order to maintain system operation sustainability, the motor should be precisely monitored and the faults are diagnosed considering various significant motor parameters. In this study, the artificial feedforward backpropagation neural network approach is proposed to real-time monitor and diagnose the faults of the hub motor by measuring seven main system parameters. So as to construct a necessary model, we trained the model, using a data set consisting of 4160 samples where each has 7 parameters, by the MATLAB environment until the best model is obtained. The results are encouraging and meaningful for the specific motor and the developed model may be applicable to other types of hub motors. The prosperous model of the whole system was embedded into Arduino Due microcontroller card and the mobile real-time monitoring and fault diagnosis system prototype for hub motor was designed and manufactured.

Supervised artificial neural network-based method for conversion of solar radiation data (case study: Algeria)

Science.gov (United States)

Laidi, Maamar; Hanini, Salah; Rezrazi, Ahmed; Yaiche, Mohamed Redha; El Hadj, Abdallah Abdallah; Chellali, Farouk

2017-04-01

In this study, a backpropagation artificial neural network (BP-ANN) model is used as an alternative approach to predict solar radiation on tilted surfaces (SRT) using a number of variables involved in physical process. These variables are namely the latitude of the site, mean temperature and relative humidity, Linke turbidity factor and Angstrom coefficient, extraterrestrial solar radiation, solar radiation data measured on horizontal surfaces (SRH), and solar zenith angle. Experimental solar radiation data from 13 stations spread all over Algeria around the year (2004) were used for training/validation and testing the artificial neural networks (ANNs), and one station was used to make the interpolation of the designed ANN. The ANN model was trained, validated, and tested using 60, 20, and 20 % of all data, respectively. The configuration 8-35-1 (8 inputs, 35 hidden, and 1 output neurons) presented an excellent agreement between the prediction and the experimental data during the test stage with determination coefficient of 0.99 and root meat squared error of 5.75 Wh/m2, considering a three-layer feedforward backpropagation neural network with Levenberg-Marquardt training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. This novel model could be used by researchers or scientists to design high-efficiency solar devices that are usually tilted at an optimum angle to increase the solar incident on the surface.

Assessing artificial neural network performance in estimating the layer properties of pavements

Directory of Open Access Journals (Sweden)

Gloria Inés Beltran

2014-05-01

Full Text Available A major concern in assessing the structural condition of existing flexible pavements is the estimation of the mechanical properties of constituent layers, which is useful for the design and decision-making process in road management systems. This parameter identification problem is truly complex due to the large number of variables involved in pavement behavior. To this end, non-conventional adaptive or approximate solutions via Artificial Neural Networks – ANNs – are considered to properly map pavement response field measurements. Previous investigations have demonstrated the exceptional ability of ANNs in layer moduli estimation from non-destructive deflection tests, but most of the reported cases were developed using synthetic deflection data or hypothetical pavement systems. This paper presents further attempts to back-calculate layer moduli via ANN modeling, using a database gathered from field tests performed on three- and four-layer pavement systems. Traditional layer structuring and pavements with a stabilized subbase were considered. A three-stage methodology is developed in this study to design and validate an “optimum” ANN-based model, i.e., the best architecture possible along with adequate learning rules. An assessment of the resulting ANN model demonstrates its forecasting capabilities and efficiency in solving a complex parameter identification problem concerning pavements.

Kernel Function Tuning for Single-Layer Neural Networks

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/

Antibacterial, anti-inflammatory and neuroprotective layer-by-layer coatings for neural implants

Science.gov (United States)

Zhang, Zhiling; Nong, Jia; Zhong, Yinghui

2015-08-01

Objective. Infection, inflammation, and neuronal loss are common issues that seriously affect the functionality and longevity of chronically implanted neural prostheses. Minocycline hydrochloride (MH) is a broad-spectrum antibiotic and effective anti-inflammatory drug that also exhibits potent neuroprotective activities. In this study, we investigated the development of biocompatible thin film coatings capable of sustained release of MH for improving the long term performance of implanted neural electrodes. Approach. We developed a novel magnesium binding-mediated drug delivery mechanism for controlled and sustained release of MH from an ultrathin hydrophilic layer-by-layer (LbL) coating and characterized the parameters that control MH loading and release. The anti-biofilm, anti-inflammatory and neuroprotective potencies of the LbL coating and released MH were also examined. Main results. Sustained release of physiologically relevant amount of MH for 46 days was achieved from the Mg2+-based LbL coating at a thickness of 1.25 μm. In addition, MH release from the LbL coating is pH-sensitive. The coating and released MH demonstrated strong anti-biofilm, anti-inflammatory, and neuroprotective potencies. Significance. This study reports, for the first time, the development of a bioactive coating that can target infection, inflammation, and neuroprotection simultaneously, which may facilitate the translation of neural interfaces to clinical applications.

Results of adaptive feedforward on GTA

International Nuclear Information System (INIS)

Ziomek, C.D.; Denney, P.M.; Regan, A.H.; Lynch, M.T.; Jachim, S.P.; Eaton, L.E.; Natter, E.F.

1993-01-01

This paper presents the results of the adaptive feedforward system in use on the Ground Test Accelerator (GTA). The adaptive feedforward system was shown to correct repetitive, high-frequency errors in the amplitude and phase of the RF field of the pulsed accelerator. The adaptive feedforward system was designed as an augmentation to the RF field feedback control system and was able to extend the closed-loop bandwidth and disturbance rejection by a factor of ten. Within a second implementation, the adaptive feedforward hardware was implemented in place of the feedback control system and was shown to negate both beam transients and phase droop in the klystron amplifier

Neural control of magnetic suspension systems

Science.gov (United States)

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.

A biologically inspired neural net for trajectory formation and obstacle avoidance.

Science.gov (United States)

Glasius, R; Komoda, A; Gielen, S C

1996-06-01

In this paper we present a biologically inspired two-layered neural network for trajectory formation and obstacle avoidance. The two topographically ordered neural maps consist of analog neurons having continuous dynamics. The first layer, the sensory map, receives sensory information and builds up an activity pattern which contains the optimal solution (i.e. shortest path without collisions) for any given set of current position, target positions and obstacle positions. Targets and obstacles are allowed to move, in which case the activity pattern in the sensory map will change accordingly. The time evolution of the neural activity in the second layer, the motor map, results in a moving cluster of activity, which can be interpreted as a population vector. Through the feedforward connections between the two layers, input of the sensory map directs the movement of the cluster along the optimal path from the current position of the cluster to the target position. The smooth trajectory is the result of the intrinsic dynamics of the network only. No supervisor is required. The output of the motor map can be used for direct control of an autonomous system in a cluttered environment or for control of the actuators of a biological limb or robot manipulator. The system is able to reach a target even in the presence of an external perturbation. Computer simulations of a point robot and a multi-joint manipulator illustrate the theory.

Feedforward and feedback projections of caudal belt and parabelt areas of auditory cortex: refining the hierarchical model

Directory of Open Access Journals (Sweden)

Troy A Hackett

2014-04-01

Full Text Available Our working model of the primate auditory cortex recognizes three major regions (core, belt, parabelt, subdivided into thirteen areas. The connections between areas are topographically ordered in a manner consistent with information flow along two major anatomical axes: core-belt-parabelt and caudal-rostral. Remarkably, most of the connections supporting this model were revealed using retrograde tracing techniques. Little is known about laminar circuitry, as anterograde tracing of axon terminations has rarely been used. The purpose of the present study was to examine the laminar projections of three areas of auditory cortex, pursuant to analysis of all areas. The selected areas were: middle lateral belt (ML; caudomedial belt (CM; and caudal parabelt (CPB. Injections of anterograde tracers yielded data consistent with major features of our model, and also new findings that compel modifications. Results supporting the model were: 1 feedforward projection from ML and CM terminated in CPB; 2 feedforward projections from ML and CPB terminated in rostral areas of the belt and parabelt; and 3 feedback projections typified inputs to the core region from belt and parabelt. At odds with the model was the convergence of feedforward inputs into rostral medial belt from ML and CPB. This was unexpected since CPB is at a higher stage of the processing hierarchy, with mainly feedback projections to all other belt areas. Lastly, extending the model, feedforward projections from CM, ML, and CPB overlapped in the temporal parietal occipital area (TPO in the superior temporal sulcus, indicating significant auditory influence on sensory processing in this region. The combined results refine our working model and highlight the need to complete studies of the laminar inputs to all areas of auditory cortex. Their documentation is essential for developing informed hypotheses about the neurophysiological influences of inputs to each layer and area.

Reliability analysis of C-130 turboprop engine components using artificial neural network

Science.gov (United States)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

A one-layer recurrent neural network for constrained nonsmooth optimization.

Science.gov (United States)

Liu, Qingshan; Wang, Jun

2011-10-01

This paper presents a novel one-layer recurrent neural network modeled by means of a differential inclusion for solving nonsmooth optimization problems, in which the number of neurons in the proposed neural network is the same as the number of decision variables of optimization problems. Compared with existing neural networks for nonsmooth optimization problems, the global convexity condition on the objective functions and constraints is relaxed, which allows the objective functions and constraints to be nonconvex. It is proven that the state variables of the proposed neural network are convergent to optimal solutions if a single design parameter in the model is larger than a derived lower bound. Numerical examples with simulation results substantiate the effectiveness and illustrate the characteristics of the proposed neural network.

Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

In this work we consider forecasting macroeconomic variables during an economic crisis. The focus is on a speci…c class of models, the so-called single hidden-layer feedforward autoregressive neural network models. What makes these models interesting in the present context is that they form a cla...... during the economic crisis 2007–2009. Forecast accuracy is measured by the root mean square forecast error. Hypothesis testing is also used to compare the performance of the different techniques with each other....

Feedback and feedforward locomotor adaptations to ankle-foot load in people with incomplete spinal cord injury.

Science.gov (United States)

Gordon, Keith E; Wu, Ming; Kahn, Jennifer H; Schmit, Brian D

2010-09-01

Humans with spinal cord injury (SCI) modulate locomotor output in response to limb load. Understanding the neural control mechanisms responsible for locomotor adaptation could provide a framework for selecting effective interventions. We quantified feedback and feedforward locomotor adaptations to limb load modulations in people with incomplete SCI. While subjects airstepped (stepping performed with kinematic assistance and 100% bodyweight support), a powered-orthosis created a dorisflexor torque during the "stance phase" of select steps producing highly controlled ankle-load perturbations. When given repetitive, stance phase ankle-load, the increase in hip extension work, 0.27 J/kg above baseline (no ankle-load airstepping), was greater than the response to ankle-load applied during a single step, 0.14 J/kg (P = 0.029). This finding suggests that, at the hip, subjects produced both feedforward and feedback locomotor modulations. We estimate that, at the hip, the locomotor response to repetitive ankle-load was modulated almost equally by ongoing feedback and feedforward adaptations. The majority of subjects also showed after-effects in hip kinetic patterns that lasted 3 min in response to repetitive loading, providing additional evidence of feedforward locomotor adaptations. The magnitude of the after-effect was proportional to the response to repetitive ankle-foot load (R(2) = 0.92). In contrast, increases in soleus EMG amplitude were not different during repetitive and single-step ankle-load exposure, suggesting that ankle locomotor modulations were predominately feedback-based. Although subjects made both feedback and feedforward locomotor adaptations to changes in ankle-load, between-subject variations suggest that walking function may be related to the ability to make feedforward adaptations.

Feedforward-feedback control of dissolved oxygen concentration in a predenitrification system.

Science.gov (United States)

Yong, Ma; Yongzhen, Peng; Shuying, Wang

2005-07-01

As the largest single energy-consuming component in most biological wastewater treatment systems, aeration control is of great interest from the point of view of saving energy and improving wastewater treatment plant efficiency. In this paper, three different strategies, including conventional constant dissolved oxygen (DO) set-point control, cascade DO set-point control, and feedforward-feedback DO set-point control were evaluated using the denitrification layout of the IWA simulation benchmark. Simulation studies showed that the feedforward-feedback DO set-point control strategy was better than the other control strategies at meeting the effluent standards and reducing operational costs. The control strategy works primarily by feedforward control based on an ammonium sensor located at the head of the aerobic process. It has an important advantage over effluent measurements in that there is no (or only a very short) time delay for information; feedforward control was combined with slow feedback control to compensate for model approximations. The feedforward-feedback DO control was implemented in a lab-scale wastewater treatment plant for a period of 60 days. Compared to operation with constant DO concentration, the required airflow could be reduced by up to 8-15% by employing the feedforward-feedback DO-control strategy, and the effluent ammonia concentration could be reduced by up to 15-25%. This control strategy can be expected to be accepted by the operating personnel in wastewater treatment plants.

Growth kinetics of borided layers: Artificial neural network and least square approaches

Science.gov (United States)

Campos, I.; Islas, M.; Ramírez, G.; VillaVelázquez, C.; Mota, C.

2007-05-01

The present study evaluates the growth kinetics of the boride layer Fe 2B in AISI 1045 steel, by means of neural networks and the least square techniques. The Fe 2B phase was formed at the material surface using the paste boriding process. The surface boron potential was modified considering different boron paste thicknesses, with exposure times of 2, 4 and 6 h, and treatment temperatures of 1193, 1223 and 1273 K. The neural network and the least square models were set by the layer thickness of Fe 2B phase, and assuming that the growth of the boride layer follows a parabolic law. The reliability of the techniques used is compared with a set of experiments at a temperature of 1223 K with 5 h of treatment time and boron potentials of 2, 3, 4 and 5 mm. The results of the Fe 2B layer thicknesses show a mean error of 5.31% for the neural network and 3.42% for the least square method.

Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

Science.gov (United States)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

Gas metal arc welding of butt joint with varying gap width based on neural networks

DEFF Research Database (Denmark)

Christensen, Kim Hardam; Sørensen, Torben

2005-01-01

penetration, when the gap width is varying during the welding process. The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters, has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least......This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full...

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

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.

Feedforward-feedback hybrid control for magnetic shape memory alloy actuators based on the Krasnosel'skii-Pokrovskii model.

Science.gov (United States)

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.

Asymmetric temporal integration of layer 4 and layer 2/3 inputs in visual cortex.

Science.gov (United States)

Hang, Giao B; Dan, Yang

2011-01-01

Neocortical neurons in vivo receive concurrent synaptic inputs from multiple sources, including feedforward, horizontal, and feedback pathways. Layer 2/3 of the visual cortex receives feedforward input from layer 4 and horizontal input from layer 2/3. Firing of the pyramidal neurons, which carries the output to higher cortical areas, depends critically on the interaction of these pathways. Here we examined synaptic integration of inputs from layer 4 and layer 2/3 in rat visual cortical slices. We found that the integration is sublinear and temporally asymmetric, with larger responses if layer 2/3 input preceded layer 4 input. The sublinearity depended on inhibition, and the asymmetry was largely attributable to the difference between the two inhibitory inputs. Interestingly, the asymmetric integration was specific to pyramidal neurons, and it strongly affected their spiking output. Thus via cortical inhibition, the temporal order of activation of layer 2/3 and layer 4 pathways can exert powerful control of cortical output during visual processing.

Gradual DropIn of Layers to Train Very Deep Neural Networks

OpenAIRE

Smith, Leslie N.; Hand, Emily M.; Doster, Timothy

2015-01-01

We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the ne...

Effects of controlled element dynamics on human feedforward behavior in ramp-tracking tasks.

Science.gov (United States)

Laurense, Vincent A; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus René M; Mulder, Max

2015-02-01

In real-life manual control tasks, human controllers are often required to follow a visible and predictable reference signal, enabling them to use feedforward control actions in conjunction with feedback actions that compensate for errors. Little is known about human control behavior in these situations. This paper investigates how humans adapt their feedforward control dynamics to the controlled element dynamics in a combined ramp-tracking and disturbance-rejection task. A human-in-the-loop experiment is performed with a pursuit display and vehicle-like controlled elements, ranging from a single integrator through second-order systems with a break frequency at either 3, 2, or 1 rad/s, to a double integrator. Because the potential benefits of feedforward control increase with steeper ramp segments in the target signal, three steepness levels are tested to investigate their possible effect on feedforward control with the various controlled elements. Analyses with four novel models of the operator, fitted to time-domain data, reveal feedforward control for all tested controlled elements and both (nonzero) tested levels of ramp steepness. For the range of controlled element dynamics investigated, it is found that humans adapt to these dynamics in their feedforward response, with a close to perfect inversion of the controlled element dynamics. No significant effects of ramp steepness on the feedforward model parameters are found.

Neural network stochastic simulation applied for quantifying uncertainties

Directory of Open Access Journals (Sweden)

N Foudil-Bey

2016-09-01

Full Text Available Generally the geostatistical simulation methods are used to generate several realizations of physical properties in the sub-surface, these methods are based on the variogram analysis and limited to measures correlation between variables at two locations only. In this paper, we propose a simulation of properties based on supervised Neural network training at the existing drilling data set. The major advantage is that this method does not require a preliminary geostatistical study and takes into account several points. As a result, the geological information and the diverse geophysical data can be combined easily. To do this, we used a neural network with multi-layer perceptron architecture like feed-forward, then we used the back-propagation algorithm with conjugate gradient technique to minimize the error of the network output. The learning process can create links between different variables, this relationship can be used for interpolation of the properties on the one hand, or to generate several possible distribution of physical properties on the other hand, changing at each time and a random value of the input neurons, which was kept constant until the period of learning. This method was tested on real data to simulate multiple realizations of the density and the magnetic susceptibility in three-dimensions at the mining camp of Val d'Or, Québec (Canada.

Feedforward temperature control using a heat flux microsensor

OpenAIRE

Lartz, Douglas John

1993-01-01

The concept of using heat flux measurements to provide the input for a feedforward temperature control loop is investigated. The feedforward loop is added to proportional and integral feedback control to increase the speed of the response to a disturbance. Comparison is made between the feedback and the feedback plus feedforward control laws. The control law with the feedforward control loop is also compared to the conventional approach of adding derivative control to speed up ...

A one-layer recurrent neural network for constrained nonconvex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2015-01-01

In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of the proposed neural network is convergent to the feasible region in finite time and stays there thereafter, provided that the penalty parameter is sufficiently large. The lower bounds of the penalty parameter and convergence time are also estimated. In addition, any neural state of the proposed neural network is convergent to its equilibrium point set which satisfies the Karush-Kuhn-Tucker conditions of the optimization problem. Moreover, the equilibrium point set is equivalent to the optimal solution to the nonconvex optimization problem if the objective function and constraints satisfy given conditions. Four numerical examples are provided to illustrate the performances of the proposed neural network.

A One-Layer Recurrent Neural Network for Constrained Complex-Variable Convex Optimization.

Science.gov (United States)

Qin, Sitian; Feng, Jiqiang; Song, Jiahui; Wen, Xingnan; Xu, Chen

2018-03-01

In this paper, based on calculus and penalty method, a one-layer recurrent neural network is proposed for solving constrained complex-variable convex optimization. It is proved that for any initial point from a given domain, the state of the proposed neural network reaches the feasible region in finite time and converges to an optimal solution of the constrained complex-variable convex optimization finally. In contrast to existing neural networks for complex-variable convex optimization, the proposed neural network has a lower model complexity and better convergence. Some numerical examples and application are presented to substantiate the effectiveness of the proposed neural network.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Directory of Open Access Journals (Sweden)

Shalin Savalia

2018-05-01

Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

Science.gov (United States)

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

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

Information flow in layered networks of non-monotonic units

International Nuclear Information System (INIS)

Neves, Fabio Schittler; Schubert, Benno Martim; Erichsen, Rubem Jr

2015-01-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information. (paper)

Information flow in layered networks of non-monotonic units

Science.gov (United States)

Schittler Neves, Fabio; Martim Schubert, Benno; Erichsen, Rubem, Jr.

2015-07-01

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

Contextual Feedback to Superficial Layers of V1

NARCIS (Netherlands)

Muckli, Lars; De Martino, Federico; Vizioli, Luca; Petro, Lucy S; Smith, Fraser W; Ugurbil, Kamil; Goebel, R.; Yacoub, Essa

2015-01-01

Neuronal cortical circuitry comprises feedforward, lateral, and feedback projections, each of which terminates in distinct cortical layers [1-3]. In sensory systems, feedforward processing transmits signals from the external world into the cortex, whereas feedback pathways signal the brain's

Enhanced HMAX model with feedforward feature learning for multiclass categorization

Directory of Open Access Journals (Sweden)

Yinlin eLi

2015-10-01

Full Text Available In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 milliseconds of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: 1 To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; 2 To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; 3 Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

Science.gov (United States)

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Modeling the Effects of Cu Content and Deformation Variables on the High-Temperature Flow Behavior of Dilute Al-Fe-Si Alloys Using an Artificial Neural Network.

Science.gov (United States)

Shakiba, Mohammad; Parson, Nick; Chen, X-Grant

2016-06-30

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002-0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C-550 °C) and strain rates (0.01-10 s -1 ). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.

Feedforward Control of Magnetically Levitated Planar Actuators

OpenAIRE

Bloemers, T.; Proimadis, I.; Kasemsinsup, Y.; Tóth, R.

2018-01-01

The present report summarizes the work conducted during the internship on Feedforward Control of the Magnetic Levitation Setup. Different feedforward strategies, specifically tailored for this setup, are developed and reviewed. These feedforward methods explicitly take the intrinsic position-dependent behavior of the magnetic levitation setup into account. Additionally, closed-loop stability of the given setup is assessed. All investigations are carried out under the rigid-body assumption of ...

Real-Time Analysis of Online Product Reviews by Means of Multi-Layer Feed-Forward Neural Networks

Directory of Open Access Journals (Sweden)

Reinhold Decker

2014-11-01

Full Text Available In the recent past, the quantitative analysis of online product reviews (OPRs has become a popular manifestation of marketing intelligence activities focusing on products that are frequently subject to electronic word-of-mouth (eWOM. Typical elements of OPRs are overall star ratings, product at- tribute scores, recommendations, pros and cons, and free texts. The first three elements are of pa r- ticular interest because they provide an aggregate view of reviewers’ opinions about the products of interest. However, the significance of individual product attributes in the overall evaluation pro c- ess  can  vary  in  the  course  of  time.  Accordingly,  ad  hoc  analyses  of  OPRs  that  have  been downloaded at a certain point in time are of limited value for dynamic eWOM monitoring because of their snapshot character. On the other hand, opinion platforms can increase the meaningfulness of the OPRs posted there and, therewith, the usefulness of the platform as a whole, by directing eWOM activities to those product attributes that really matter at present. This paper therefore in- troduces a neural network-based approach that allows the dynamic tracking of the influence the posted scores of product attributes have on the overall star ratings of the concerning products. By using an elasticity measure, this approach supports the identification of those attributes that tend to lose or gain significance in the product evaluation process over time. The usability of this ap- proach is demonstrated using real OPR data on digital cameras and hotels.

CUDA-accelerated genetic feedforward-ANN training for data mining

International Nuclear Information System (INIS)

Patulea, Catalin; Peace, Robert; Green, James

2010-01-01

We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

CUDA-accelerated genetic feedforward-ANN training for data mining

Energy Technology Data Exchange (ETDEWEB)

Patulea, Catalin; Peace, Robert; Green, James, E-mail: cpatulea@sce.carleton.ca, E-mail: rpeace@sce.carleton.ca, E-mail: jrgreen@sce.carleton.ca [School of Systems and Computer Engineering, Carleton University, Ottawa, K1S 5B6 (Canada)

2010-11-01

We present an implementation of genetic algorithm (GA) training of feedforward artificial neural networks (ANNs) targeting commodity graphics cards (GPUs). By carefully mapping the problem onto the unique GPU architecture, we achieve order-of-magnitude speedup over a conventional CPU implementation. Furthermore, we show that the speedup is consistent across a wide range of data set sizes, making this implementation ideal for large data sets. This performance boost enables the genetic algorithm to search a larger subset of the solution space, which results in more accurate pattern classification. Finally, we demonstrate this method in the context of the 2009 UC San Diego Data Mining Contest, achieving a world-class lift on a data set of 94682 e-commerce transactions.

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Directory of Open Access Journals (Sweden)

Shuihua Wang

2015-08-01

Full Text Available Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE, principal component analysis (PCA, feedforward neural network (FNN trained by fitness-scaled chaotic artificial bee colony (FSCABC and biogeography-based optimization (BBO, respectively. The K-fold stratified cross validation (SCV was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed “WE + PCA + FSCABC-FNN” and “WE + PCA + BBO-FNN” methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: “(CH + MP + US + PCA + GA-FNN ” of 84.8%, “(CH + MP + US + PCA + PSO-FNN” of 87.9%, “(CH + MP + US + PCA + ABC-FNN” of 85.4%, “(CH + MP + US + PCA + kSVM” of 88.2%, and “(CH + MP + US + PCA + FSCABC-FNN” of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification.

Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

Directory of Open Access Journals (Sweden)

Mahmoud Akbarian

2015-07-01

Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP neural network with scaled conjugate gradient (trainscg back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth among pregnant women with lupus by using identified effective variables.

Artificial neural network-aided image analysis system for cell counting.

Science.gov (United States)

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

A two-layer recurrent neural network for nonsmooth convex optimization problems.

Science.gov (United States)

Qin, Sitian; Xue, Xiaoping

2015-06-01

In this paper, a two-layer recurrent neural network is proposed to solve the nonsmooth convex optimization problem subject to convex inequality and linear equality constraints. Compared with existing neural network models, the proposed neural network has a low model complexity and avoids penalty parameters. It is proved that from any initial point, the state of the proposed neural network reaches the equality feasible region in finite time and stays there thereafter. Moreover, the state is unique if the initial point lies in the equality feasible region. The equilibrium point set of the proposed neural network is proved to be equivalent to the Karush-Kuhn-Tucker optimality set of the original optimization problem. It is further proved that the equilibrium point of the proposed neural network is stable in the sense of Lyapunov. Moreover, from any initial point, the state is proved to be convergent to an equilibrium point of the proposed neural network. Finally, as applications, the proposed neural network is used to solve nonlinear convex programming with linear constraints and L1 -norm minimization problems.

Binding and segmentation via a neural mass model trained with Hebbian and anti-Hebbian mechanisms.

Science.gov (United States)

Cona, Filippo; Zavaglia, Melissa; Ursino, Mauro

2012-04-01

Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

Parameter estimation using compensatory neural networks

Indian Academy of Sciences (India)

of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron ..... Engelbrecht A P, Cloete I, Geldenhuys J, Zurada J M 1995 Automatic scaling using gamma learning for feedforward neural networks. From natural to artificial computing.

Understanding the role of speech production in reading: Evidence for a print-to-speech neural network using graphical analysis.

Science.gov (United States)

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).

Prediction of friction factor of pure water flowing inside vertical smooth and microfin tubes by using artificial neural networks

Science.gov (United States)

Çebi, A.; Akdoğan, E.; Celen, A.; Dalkilic, A. S.

2017-02-01

An artificial neural network (ANN) model of friction factor in smooth and microfin tubes under heating, cooling and isothermal conditions was developed in this study. Data used in ANN was taken from a vertically positioned heat exchanger experimental setup. Multi-layered feed-forward neural network with backpropagation algorithm, radial basis function networks and hybrid PSO-neural network algorithm were applied to the database. Inputs were the ratio of cross sectional flow area to hydraulic diameter, experimental condition number depending on isothermal, heating, or cooling conditions and mass flow rate while the friction factor was the output of the constructed system. It was observed that such neural network based system could effectively predict the friction factor values of the flows regardless of their tube types. A dependency analysis to determine the strongest parameter that affected the network and database was also performed and tube geometry was found to be the strongest parameter of all as a result of analysis.

Artificial neural network for modeling the extraction of aromatic hydrocarbons from lube oil cuts

Energy Technology Data Exchange (ETDEWEB)

Mehrkesh, A.H.; Hajimirzaee, S. [Islamic Azad University, Majlesi Branch, Isfahan (Iran, Islamic Republic of); Hatamipour, M.S.; Tavakoli, T. [Department of Chemical Engineering, University of Isfahan, Isfahan (Iran, Islamic Republic of)

2011-03-15

An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. (Copyright copyright 2011 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

[Membrane-bound cytokine and feedforward regulation].

Science.gov (United States)

Wu, Ke-Fu; Zheng, Guo-Guang; Ma, Xiao-Tong; Song, Yu-Hua

2013-10-01

Feedback and feedforward widely exist in life system, both of them are the basic processes of control system. While the concept of feedback has been widely used in life science, feedforward regulation was systematically studied in neurophysiology, awaiting further evidence and mechanism in molecular biology and cell biology. The authors put forward a hypothesis about the feedforward regulation of membrane bound macrophage colony stimulation factor (mM-CSF) on the basis of their previous work. This hypothesis might provide a new direction for the study on the biological effects of mM-CSF on leukemia and solid tumors, and contribute to the study on other membrane bound cytokines.

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Science.gov (United States)

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

A one-layer recurrent neural network for constrained nonsmooth invex optimization.

Science.gov (United States)

Li, Guocheng; Yan, Zheng; Wang, Jun

2014-02-01

Invexity is an important notion in nonconvex optimization. In this paper, a one-layer recurrent neural network is proposed for solving constrained nonsmooth invex optimization problems, designed based on an exact penalty function method. It is proved herein that any state of the proposed neural network is globally convergent to the optimal solution set of constrained invex optimization problems, with a sufficiently large penalty parameter. In addition, any neural state is globally convergent to the unique optimal solution, provided that the objective function and constraint functions are pseudoconvex. Moreover, any neural state is globally convergent to the feasible region in finite time and stays there thereafter. The lower bounds of the penalty parameter and convergence time are also estimated. Two numerical examples are provided to illustrate the performances of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

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

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

Science.gov (United States)

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

Feedforward mapping for engine control

OpenAIRE

Aran, Volkan; Ünel, Mustafa; Unel, Mustafa

2016-01-01

Feedforward control is widely used in electronic control units of internal combustion engines besides feedback controls. However, almost all feedforward control values are used in table form, also called maps, having engine speed and engine torque in their axes. Table approach limits all inte ractions in two input dimensions. This paper focuses on application of Gaussian process modelling of errors of inverse parametric model of the valve position. Validation results based on ...

Visualization of neural networks using saliency maps

DEFF Research Database (Denmark)

Mørch, Niels J.S.; Kjems, Ulrik; Hansen, Lars Kai

1995-01-01

The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feedforward neural networks, with emphasis on the ill-posed case, where the dimensionality of the input-field by far exceeds the number of examples. Several levels of approximations...

Neurale Netværk anvendt indenfor Proceskontrol. Neural Network for Process Control

DEFF Research Database (Denmark)

Madsen, Per Printz

-Layer Perceptron net. Der er opstillet koncepter/metoder til såvel feedforward regulering som feedback regulering. Multi-Layer Perceptronen er i stand til at regulere et ulineært, multivariabelt og dynamisk system, således at der opnås følgende: 1. Systemet lineariseres således, at der opnås ensartet steprespons i...

Forcast of TEXT plasma disruptions using soft X-rays as input signal in a neural network

International Nuclear Information System (INIS)

Vannucci, A.; Oliveira, K.A.; Tajima, T.

1998-02-01

A feed-forward neural network with two hidden layers is used in this work to forecast major and minor disruptive instabilities in TEXT discharges. Using soft X-ray signals as input data, the neural net is trained with one disruptive plasma pulse, and a different disruptive discharge is used for validation. After being properly trained the networks, with the same set of weights. is then used to forecast disruptions in two others different plasma pulses. It is observed that the neural net is able to predict the incoming of a disruption more than 3 ms in advance. This time interval is almost three times longer than the one already obtained previously when magnetic signal from a Mirnov coil was used to feed the neural networks with. To our own eye we fail to see any indication of an upcoming disruption from the experimental data this far back from the time of disruption. Finally, from what we observe in the predictive behavior of our network, speculations are made whether the disruption triggering mechanism would be associated to an increase of the m = 2 magnetic island, that disturbs the central part of the plasma column afterwards or, in face of the results from this work, the initial perturbation would have occurred first in the central part of the plasma column, within the q = 1 magnetic surface, and then the m = 2 MHD mode would be destabilized afterwards

Modeling Broadband Microwave Structures by Artificial Neural Networks

Directory of Open Access Journals (Sweden)

V. Otevrel

2004-06-01

Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

Neural Network Predictions of the 4-Quadrant Wageningen Propeller Series

National Research Council Canada - National Science Library

Roddy, Robert F; Hess, David E; Faller, Will

2006-01-01

.... This report describes the development of feedforward neural network (FFNN) predictions of four-quadrant thrust and torque behavior for the Wageningen B-Screw Series of propellers and for two Wageningen ducted propeller series...

Learning behavior and temporary minima of two-layer neural networks

NARCIS (Netherlands)

Annema, Anne J.; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

This paper presents a mathematical analysis of the occurrence of temporary minima during training of a single-output, two-layer neural network, with learning according to the back-propagation algorithm. A new vector decomposition method is introduced, which simplifies the mathematical analysis of

Probing feedforward and feedback contributions to awareness with visual masking and transcranial magnetic stimulation.

Science.gov (United States)

Tapia, Evelina; Beck, Diane M

2014-01-01

A number of influential theories posit that visual awareness relies not only on the initial, stimulus-driven (i.e., feedforward) sweep of activation but also on recurrent feedback activity within and between brain regions. These theories of awareness draw heavily on data from masking paradigms in which visibility of one stimulus is reduced due to the presence of another stimulus. More recently transcranial magnetic stimulation (TMS) has been used to study the temporal dynamics of visual awareness. TMS over occipital cortex affects performance on visual tasks at distinct time points and in a manner that is comparable to visual masking. We draw parallels between these two methods and examine evidence for the neural mechanisms by which visual masking and TMS suppress stimulus visibility. Specifically, both methods have been proposed to affect feedforward as well as feedback signals when applied at distinct time windows relative to stimulus onset and as a result modify visual awareness. Most recent empirical evidence, moreover, suggests that while visual masking and TMS impact stimulus visibility comparably, the processes these methods affect may not be as similar as previously thought. In addition to reviewing both masking and TMS studies that examine feedforward and feedback processes in vision, we raise questions to guide future studies and further probe the necessary conditions for visual awareness.

Jerk derivative feedforward control for motion systems

NARCIS (Netherlands)

Boerlage, M.L.G.; Tousain, R.L.; Steinbuch, M.

2004-01-01

This work discusses reference trajectory relevant model based feedforward design. For motion systems which contain at least one rigid body mode and which are subject to reference trajectories with mostly low frequency energy, the proposed feedforward controller improves tracking performance

Feedforward and feedback motor control abnormalities implicate cerebellar dysfunctions in autism spectrum disorder.

Science.gov (United States)

Mosconi, Matthew W; Mohanty, Suman; Greene, Rachel K; Cook, Edwin H; Vaillancourt, David E; Sweeney, John A

2015-02-04

Sensorimotor abnormalities are common in autism spectrum disorder (ASD) and among the earliest manifestations of the disorder. They have been studied far less than the social-communication and cognitive deficits that define ASD, but a mechanistic understanding of sensorimotor abnormalities in ASD may provide key insights into the neural underpinnings of the disorder. In this human study, we examined rapid, precision grip force contractions to determine whether feedforward mechanisms supporting initial motor output before sensory feedback can be processed are disrupted in ASD. Sustained force contractions also were examined to determine whether reactive adjustments to ongoing motor behavior based on visual feedback are altered. Sustained force was studied across multiple force levels and visual gains to assess motor and visuomotor mechanisms, respectively. Primary force contractions of individuals with ASD showed greater peak rate of force increases and large transient overshoots. Individuals with ASD also showed increased sustained force variability that scaled with force level and was more severe when visual gain was highly amplified or highly degraded. When sustaining a constant force level, their reactive adjustments were more periodic than controls, and they showed increased reliance on slower feedback mechanisms. Feedforward and feedback mechanism alterations each were associated with more severe social-communication impairments in ASD. These findings implicate anterior cerebellar circuits involved in feedforward motor control and posterior cerebellar circuits involved in transforming visual feedback into precise motor adjustments in ASD. Copyright © 2015 the authors 0270-6474/15/352015-11$15.00/0.

The Multi-Layered Perceptrons Neural Networks for the Prediction of Daily Solar Radiation

OpenAIRE

Radouane Iqdour; Abdelouhab Zeroual

2007-01-01

The Multi-Layered Perceptron (MLP) Neural networks have been very successful in a number of signal processing applications. In this work we have studied the possibilities and the met difficulties in the application of the MLP neural networks for the prediction of daily solar radiation data. We have used the Polack-Ribière algorithm for training the neural networks. A comparison, in term of the statistical indicators, with a linear model most used in literature, is also perfo...

Parameter estimation in space systems using recurrent neural networks

Science.gov (United States)

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.

Artificial neural networks for dynamic monitoring of simulated-operating parameters of high temperature gas cooled engineering test reactor (HTTR)

International Nuclear Information System (INIS)

Seker, Serhat; Tuerkcan, Erdinc; Ayaz, Emine; Barutcu, Burak

2003-01-01

This paper addresses to the problem of utilisation of the artificial neural networks (ANNs) for detecting anomalies as well as physical parameters of a nuclear power plant during power operation in real time. Three different types of neural network algorithms were used namely, feed-forward neural network (back-propagation, BP) and two types of recurrent neural networks (RNN). The data used in this paper were gathered from the simulation of the power operation of the Japan's High Temperature Engineering Testing Reactor (HTTR). For the wide range of power operation, 56 signals were generated by the reactor dynamic simulation code for several hours of normal power operation at different power ramps between 30 and 100% nominal power. Paper will compare the outcomes of different neural networks and presents the neural network system and the determination of physical parameters from the simulated operating data

Fuzzy PID Feedback Control of Piezoelectric Actuator with Feedforward Compensation

Directory of Open Access Journals (Sweden)

Ziqiang Chi

2014-01-01

Full Text Available Piezoelectric actuator is widely used in the field of micro/nanopositioning. However, piezoelectric hysteresis introduces nonlinearity to the system, which is the major obstacle to achieve a precise positioning. In this paper, the Preisach model is employed to describe the hysteresis characteristic of piezoelectric actuator and an inverse Preisach model is developed to construct a feedforward controller. Considering that the analytical expression of inverse Preisach model is difficult to derive and not suitable for practical application, a digital inverse model is established based on the input and output data of a piezoelectric actuator. Moreover, to mitigate the compensation error of the feedforward control, a feedback control scheme is implemented using different types of control algorithms in terms of PID control, fuzzy control, and fuzzy PID control. Extensive simulation studies are carried out using the three kinds of control systems. Comparative investigation reveals that the fuzzy PID control system with feedforward compensation is capable of providing quicker response and better control accuracy than the other two ones. It provides a promising way of precision control for piezoelectric actuator.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Optimized Neural Network for Fault Diagnosis and Classification

International Nuclear Information System (INIS)

Elaraby, S.M.

2005-01-01

This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

Mass reconstruction with a neural network

International Nuclear Information System (INIS)

Loennblad, L.; Peterson, C.; Roegnvaldsson, T.

1992-01-01

A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W→qanti q, where W-bosons are produced in panti p reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using 'intelligent' variables in instances when the amount of training instances is limited. (orig.)

Optimum operating conditions for a water purification process integrated to a heat transformer with energy recycling using neural network inverse

Energy Technology Data Exchange (ETDEWEB)

Hernandez, J.A.; Siqueiros, J.; Juarez-Romero, D. [Centro de Investigacion en Ingenieria y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 62209 (Mexico); Bassam, A. [Posgrado en Ingenieria y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos (UAEM), Av. Universidad No. 1001, Col. Chamilpa, Cuernavaca, Morelos C.P. 62209 (Mexico)

2009-04-15

Artificial neural network inverse (ANNi) is applied to calculate the optimal operating conditions on the coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling. An artificial neural network (ANN) model is developed to predict the COP which was increased with energy recycling. This ANN model takes into account the input and output temperatures for each one of the four components (absorber, generator, evaporator, and condenser), as well as two pressures and LiBr + H{sub 2}O concentrations. For the network, a feedforward with one hidden layer, a Levenberg-Marquardt learning algorithm, a hyperbolic tangent sigmoid transfer function and a linear transfer function were used. The best fitting training data set was obtained with three neurons in the hidden layer. On the validation data set, simulations and experimental data test were in good agreement (R > 0.99). This ANN model can be used to predict the COP when the input variables (operating conditions) are well known. However, to control the COP in the system, we developed a strategy to estimate the optimal input variables when a COP is required from ANNi. An optimization method (the Nelder-Mead simplex method) is used to fit the unknown input variable resulted from the ANNi. This methodology can be applied to control on-line the performance of the system. (author)

Typology of nonlinear activity waves in a layered neural continuum.

Science.gov (United States)

Koch, Paul; Leisman, Gerry

2006-04-01

Neural tissue, a medium containing electro-chemical energy, can amplify small increments in cellular activity. The growing disturbance, measured as the fraction of active cells, manifests as propagating waves. In a layered geometry with a time delay in synaptic signals between the layers, the delay is instrumental in determining the amplified wavelengths. The growth of the waves is limited by the finite number of neural cells in a given region of the continuum. As wave growth saturates, the resulting activity patterns in space and time show a variety of forms, ranging from regular monochromatic waves to highly irregular mixtures of different spatial frequencies. The type of wave configuration is determined by a number of parameters, including alertness and synaptic conditioning as well as delay. For all cases studied, using numerical solution of the nonlinear Wilson-Cowan (1973) equations, there is an interval in delay in which the wave mixing occurs. As delay increases through this interval, during a series of consecutive waves propagating through a continuum region, the activity within that region changes from a single-frequency to a multiple-frequency pattern and back again. The diverse spatio-temporal patterns give a more concrete form to several metaphors advanced over the years to attempt an explanation of cognitive phenomena: Activity waves embody the "holographic memory" (Pribram, 1991); wave mixing provides a plausible cause of the competition called "neural Darwinism" (Edelman, 1988); finally the consecutive generation of growing neural waves can explain the discontinuousness of "psychological time" (Stroud, 1955).

Implementation of multi-layer feed forward neural network on PIC16F877 microcontroller

International Nuclear Information System (INIS)

Nur Aira Abd Rahman

2005-01-01

Artificial Neural Network (ANN) is an electronic model based on the neural structure of the brain. Similar to human brain, ANN consists of interconnected simple processing units or neurons that process input to generate output signals. ANN operation is divided into 2 categories; training mode and service mode. This project aims to implement ANN on PIC micro-controller that enable on-chip or stand alone training and service mode. The input can varies from sensors or switches, while the output can be used to control valves, motors, light source and a lot more. As partial development of the project, this paper reports the current status and results of the implemented ANN. The hardware fraction of this project incorporates Microchip PIC16F877A microcontrollers along with uM-FPU math co-processor. uM-FPU is a 32-bit floating point co-processor utilized to execute complex calculation requires by the sigmoid activation function for neuron. ANN algorithm is converted to software program written in assembly language. The implemented ANN structure is three layer with one hidden layer, and five neurons with two hidden neurons. To prove the operability and functionality, the network is trained to solve three common logic gate operations; AND, OR, and XOR. This paper concludes that the ANN had been successfully implemented on PIC16F877a and uM-FPU math co-processor hardware that works accordingly on both training and service mode. (Author)

Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network

Directory of Open Access Journals (Sweden)

Debesh Jha

2017-01-01

Full Text Available Background. Error-free diagnosis of Alzheimer’s disease (AD from healthy control (HC patients at an early stage of the disease is a major concern, because information about the condition’s severity and developmental risks present allows AD sufferer to take precautionary measures before irreversible brain damage occurs. Recently, there has been great interest in computer-aided diagnosis in magnetic resonance image (MRI classification. However, distinguishing between Alzheimer’s brain data and healthy brain data in older adults (age > 60 is challenging because of their highly similar brain patterns and image intensities. Recently, cutting-edge feature extraction technologies have found extensive application in numerous fields, including medical image analysis. Here, we propose a dual-tree complex wavelet transform (DTCWT for extracting features from an image. The dimensionality of feature vector is reduced by using principal component analysis (PCA. The reduced feature vector is sent to feed-forward neural network (FNN to distinguish AD and HC from the input MR images. These proposed and implemented pipelines, which demonstrate improvements in classification output when compared to that of recent studies, resulted in high and reproducible accuracy rates of 90.06 ± 0.01% with a sensitivity of 92.00 ± 0.04%, a specificity of 87.78 ± 0.04%, and a precision of 89.6 ± 0.03% with 10-fold cross-validation.

Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

International Nuclear Information System (INIS)

Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

2010-01-01

This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

Science.gov (United States)

Bianchini, Monica; Scarselli, Franco

2014-08-01

Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

Science.gov (United States)

Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

2016-01-01

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper.

Learning representations for the early detection of sepsis with deep neural networks.

Science.gov (United States)

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

A low-complexity feed-forward I/Q imbalance compensation algorithm

NARCIS (Netherlands)

Moseley, N.A.; Slump, Cornelis H.

2006-01-01

This paper presents a low-complexity adaptive feed- forward I/Q imbalance compensation algorithm. The feed-forward so- lution has guaranteed stability. Due to its blind nature the algorithm is easily incorporated into an existing receiver design. The algorithm uses three estimators to obtain the

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements.

Science.gov (United States)

Botzer, Lior; Karniel, Amir

2013-07-01

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Robust recurrent neural network modeling for software fault detection and correction prediction

International Nuclear Information System (INIS)

Hu, Q.P.; Xie, M.; Ng, S.H.; Levitin, G.

2007-01-01

Software fault detection and correction processes are related although different, and they should be studied together. A practical approach is to apply software reliability growth models to model fault detection, and fault correction process is assumed to be a delayed process. On the other hand, the artificial neural networks model, as a data-driven approach, tries to model these two processes together with no assumptions. Specifically, feedforward backpropagation networks have shown their advantages over analytical models in fault number predictions. In this paper, the following approach is explored. First, recurrent neural networks are applied to model these two processes together. Within this framework, a systematic networks configuration approach is developed with genetic algorithm according to the prediction performance. In order to provide robust predictions, an extra factor characterizing the dispersion of prediction repetitions is incorporated into the performance function. Comparisons with feedforward neural networks and analytical models are developed with respect to a real data set

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)

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

Science.gov (United States)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

Science.gov (United States)

Khanali, Majid; Mobli, Hossein; Hosseinzadeh-Bandbafha, Homa

2017-12-01

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for

Using neural networks with jet shapes to identify b jets in e+e- interactions

International Nuclear Information System (INIS)

Bellantoni, L.; Conway, J.S.; Jacobsen, J.E.; Pan, Y.B.; Wu Saulan

1991-01-01

A feed-forward neural network trained using backpropagation was used to discriminate between b and light quark jets in e + e - → Z 0 → qanti q events. The information presented to the network consisted of 25 jet shape variables. The network successfully identified b jets in two- and three-jet events modeled using a detector simulation. The jet identification efficiency for two-jet events was 61% and the probability to call a light quark jet a b jet equal to 20%. (orig.)

Neural Network Predictions of the 4-Quadrant Wageningen Propeller Series (CD-ROM)

National Research Council Canada - National Science Library

Roddy, Robert F; Hess, David E; Faller, Will

2006-01-01

.... This report describes the development of feedforward neural network (FFNN) predictions of four-quadrant thrust and torque behavior for the Wageningen B-Screw Series of propellers and for two Wageningen ducted propeller series...

An artificial neural network to predict resting energy expenditure in obesity.

Science.gov (United States)

Disse, Emmanuel; Ledoux, Séverine; Bétry, Cécile; Caussy, Cyrielle; Maitrepierre, Christine; Coupaye, Muriel; Laville, Martine; Simon, Chantal

2017-09-01

The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m -2 ) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m -2 . External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive

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)

Identification of the feedforward component in manual control with predictable target signals.

Science.gov (United States)

Drop, Frank M; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus M; Mulder, Max

2013-12-01

In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

Tracking and vertex finding with drift chambers and neural networks

International Nuclear Information System (INIS)

Lindsey, C.

1991-09-01

Finding tracks, track vertices and event vertices with neural networks from drift chamber signals is discussed. Simulated feed-forward neural networks have been trained with back-propagation to give track parameters using Monte Carlo simulated tracks in one case and actual experimental data in another. Effects on network performance of limited weight resolution, noise and drift chamber resolution are given. Possible implementations in hardware are discussed. 7 refs., 10 figs

Neural Model for Left-Handed CPW Bandpass Filter Loaded Split Ring Resonator

Science.gov (United States)

Liu, Haiwen; Wang, Shuxin; Tan, Mingtao; Zhang, Qijun

2010-02-01

Compact left-handed coplanar waveguide (CPW) bandpass filter loaded split ring resonator (SRR) is presented in this paper. The proposed filter exhibits a quasi-elliptic function response and its circuit size occupies only 12 × 11.8 mm2 (≈0.21 λg × 0.20 λg). Also, a simple circuit model is given and the parametric study of this filter is discussed. Then, with the aid of NeuroModeler software, a five-layer feed-forward perceptron neural networks model is built up to optimize the proposed filter design fast and accurately. Finally, this newly left-handed CPW bandpass filter was fabricated and measured. A good agreement between simulations and measurement verifies the proposed left-handed filter and the validity of design methodology.

Structural learning in feedforward and feedback control.

Science.gov (United States)

Yousif, Nada; Diedrichsen, Jörn

2012-11-01

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.

Feed-Forward Control in Resonant DC Link Inverter

OpenAIRE

Apinan Aurasopon; Worawat Sa-ngiavibool

2008-01-01

This paper proposes a feed-forward control in resonant dc link inverter. The feed-forward control configuration is based on synchronous sigma-delta modulation. The simulation results showing the proposed technique can reject non-ideal dc bus improving the total harmonic distortion.

Combined feedforward and feedback control of end milling system

OpenAIRE

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...

Low-order feedforward controllers: Optimal performance and practical considerations

OpenAIRE

Hast, Martin; Hägglund, Tore

2014-01-01

Feedforward control from measurable disturbances can significantly improve the performance in control loops. However, tuning rules for such controllers are scarce. In this paper design rules for how to choose optimal low-order feedforward controller parameter are presented. The parameters are chosen so that the integrated squared error, when the system is subject to a step disturbance, is minimized. The approach utilizes a controller structure that decouples the feedforward and the feedback c...

Artificial neural networks for plasma spectroscopy analysis

International Nuclear Information System (INIS)

Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

1992-01-01

Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

Hardware implementation of stochastic spiking neural networks.

Science.gov (United States)

Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Forecast of TEXT plasma disruptions using soft X rays as input signal in a neural network

International Nuclear Information System (INIS)

Vannucci, A.; Oliveira, K.A.; Tajima, T.

1999-01-01

A feedforward neural network with two hidden layers is used to forecast major and minor disruptive instabilities in TEXT tokamak discharges. Using the experimental data of soft X ray signals as input data, the neural network is trained with one disruptive plasma discharge, and a different disruptive discharge is used for validation. After being properly trained, the networks, with the same set of weights, are used to forecast disruptions in two other plasma discharges. It is observed that the neural network is able to predict the occurrence of a disruption more than 3 ms in advance. This time interval is almost 3 times longer than the one already obtained previously when a magnetic signal from a Mirnov coil was used to feed the neural networks. Visually no indication of an upcoming disruption is seen from the experimental data this far back from the time of disruption. Finally, by observing the predictive behaviour of the network for the disruptive discharges analysed and comparing the soft X ray data with the corresponding magnetic experimental signal, it is conjectured about where inside the plasma column the disruption first started. (author)

Theoretical properties of the global optimizer of two layer neural network

OpenAIRE

Boob, Digvijay; Lan, Guanghui

2017-01-01

In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves "almost" all functions which are not piecewise linear), we have that first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. ...

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

Science.gov (United States)

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

The mechanism of synchronization in feed-forward neuronal networks

International Nuclear Information System (INIS)

Goedeke, S; Diesmann, M

2008-01-01

Synchronization in feed-forward subnetworks of the brain has been proposed to explain the precisely timed spike patterns observed in experiments. While the attractor dynamics of these networks is now well understood, the underlying single neuron mechanisms remain unexplained. Previous attempts have captured the effects of the highly fluctuating membrane potential by relating spike intensity f(U) to the instantaneous voltage U generated by the input. This article shows that f is high during the rise and low during the decay of U(t), demonstrating that the U-dot-dependence of f, not refractoriness, is essential for synchronization. Moreover, the bifurcation scenario is quantitatively described by a simple f(U,U-dot) relationship. These findings suggest f(U,U-dot) as the relevant model class for the investigation of neural synchronization phenomena in a noisy environment

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)

Propagation of spiking regularity and double coherence resonance in feedforward networks.

Science.gov (United States)

Men, Cong; Wang, Jiang; Qin, Ying-Mei; Deng, Bin; Tsang, Kai-Ming; Chan, Wai-Lok

2012-03-01

We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

Forecasting the mortality rates of Indonesian population by using neural network

Science.gov (United States)

Safitri, Lutfiani; Mardiyati, Sri; Rahim, Hendrisman

2018-03-01

A model that can represent a problem is required in conducting a forecasting. One of the models that has been acknowledged by the actuary community in forecasting mortality rate is the Lee-Certer model. Lee Carter model supported by Neural Network will be used to calculate mortality forecasting in Indonesia. The type of Neural Network used is feedforward neural network aligned with backpropagation algorithm in python programming language. And the final result of this study is mortality rate in forecasting Indonesia for the next few years

Separating true V0's from combinatoric background with a neural network

International Nuclear Information System (INIS)

Justice, M.

1997-01-01

A feedforward multilayered neural network has been trained to ''recognize'' true V0's in the presence of a large combinatoric background using simulated data for 2 GeV/nucleon Ni + Cu interactions. The resulting neural network filter has been applied to actual data from the EOS TPC experiment. An enhancement of signal to background over more traditional selection mechanisms has been observed. (orig.)

PEAK TRACKING WITH A NEURAL NETWORK FOR SPECTRAL RECOGNITION

NARCIS (Netherlands)

COENEGRACHT, PMJ; METTING, HJ; VANLOO, EM; SNOEIJER, GJ; DOORNBOS, DA

1993-01-01

A peak tracking method based on a simulated feed-forward neural network with back-propagation is presented. The network uses the normalized UV spectra and peak areas measured in one chromatogram for peak recognition. It suffices to train the network with only one set of spectra recorded in one

FEM-based neural-network approach to nonlinear modeling with application to longitudinal vehicle dynamics control.

Science.gov (United States)

Kalkkuhl, J; Hunt, K J; Fritz, H

1999-01-01

An finite-element methods (FEM)-based neural-network approach to Nonlinear AutoRegressive with eXogenous input (NARX) modeling is presented. The method uses multilinear interpolation functions on C0 rectangular elements. The local and global structure of the resulting model is analyzed. It is shown that the model can be interpreted both as a local model network and a single layer feedforward neural network. The main aim is to use the model for nonlinear control design. The proposed FEM NARX description is easily accessible to feedback linearizing control techniques. Its use with a two-degrees of freedom nonlinear internal model controller is discussed. The approach is applied to modeling of the nonlinear longitudinal dynamics of an experimental lorry, using measured data. The modeling results are compared with local model network and multilayer perceptron approaches. A nonlinear speed controller was designed based on the identified FEM model. The controller was implemented in a test vehicle, and several experimental results are presented.

Beneficial role of noise in artificial neural networks

International Nuclear Information System (INIS)

Monterola, Christopher; Saloma, Caesar; Zapotocky, Martin

2008-01-01

We demonstrate enhancement of neural networks efficacy to recognize frequency encoded signals and/or to categorize spatial patterns of neural activity as a result of noise addition. For temporal information recovery, noise directly added to the receiving neurons allow instantaneous improvement of signal-to-noise ratio [Monterola and Saloma, Phys. Rev. Lett. 2002]. For spatial patterns however, recurrence is necessary to extend and homogenize the operating range of a feed-forward neural network [Monterola and Zapotocky, Phys. Rev. E 2005]. Finally, using the size of the basin of attraction of the networks learned patterns (dynamical fixed points), a procedure for estimating the optimal noise is demonstrated

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

Science.gov (United States)

Idris, N. H.; Salim, N. A.; Othman, M. M.; Yasin, Z. M.

2018-03-01

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Control of 12-Cylinder Camless Engine with Neural Networks

OpenAIRE

Ashhab Moh’d Sami

2017-01-01

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 appl...

Cell-type specific short-term plasticity at auditory nerve synapses controls feed-forward inhibition in the dorsal cochlear nucleus

Directory of Open Access Journals (Sweden)

Miloslav eSedlacek

2014-07-01

Full Text Available Feedforward inhibition represents a powerful mechanism by which control of the timing and fidelity of action potentials in local synaptic circuits of various brain regions is achieved. In the cochlear nucleus, the auditory nerve provides excitation to both principal neurons and inhibitory interneurons. Here, we investigated the synaptic circuit associated with fusiform cells (FCs, principal neurons of the dorsal cochlear nucleus (DCN that receive excitation from auditory nerve fibers and inhibition from tuberculoventral cells (TVCs on their basal dendrites in the deep layer of DCN. Despite the importance of these inputs in regulating fusiform cell firing behavior, the mechanisms determining the balance of excitation and feed-forward inhibition in this circuit are not well understood. Therefore, we examined the timing and plasticity of auditory nerve driven feed-forward inhibition (FFI onto FCs. We find that in some FCs, excitatory and inhibitory components of feed-forward inhibition had the same stimulation thresholds indicating they could be triggered by activation of the same fibers. In other FCs, excitation and inhibition exhibit different stimulus thresholds, suggesting FCs and TVCs might be activated by different sets of fibers. In addition we find that during repetitive activation, synapses formed by the auditory nerve onto TVCs and FCs exhibit distinct modes of short-term plasticity. Feed-forward inhibitory post-synaptic currents (IPSCs in FCs exhibit short-term depression because of prominent synaptic depression at the auditory nerve-TVC synapse. Depression of this feedforward inhibitory input causes a shift in the balance of fusiform cell synaptic input towards greater excitation and suggests that fusiform cell spike output will be enhanced by physiological patterns of auditory nerve activity.

Permutation parity machines for neural cryptography.

Science.gov (United States)

Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

2010-06-01

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Permutation parity machines for neural cryptography

International Nuclear Information System (INIS)

Reyes, Oscar Mauricio; Zimmermann, Karl-Heinz

2010-01-01

Recently, synchronization was proved for permutation parity machines, multilayer feed-forward neural networks proposed as a binary variant of the tree parity machines. This ability was already used in the case of tree parity machines to introduce a key-exchange protocol. In this paper, a protocol based on permutation parity machines is proposed and its performance against common attacks (simple, geometric, majority and genetic) is studied.

Stochastic resonance in feedforward acupuncture networks

Science.gov (United States)

Qin, Ying-Mei; Wang, Jiang; Men, Cong; Deng, Bin; Wei, Xi-Le; Yu, Hai-Tao; Chan, Wai-Lok

2014-10-01

Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh-Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

Differences in feedforward trunk muscle activity in subgroups of patients with mechanical low back pain.

Science.gov (United States)

Silfies, Sheri P; Mehta, Rupal; Smith, Sue S; Karduna, Andrew R

2009-07-01

To investigate alterations in trunk muscle timing patterns in subgroups of patients with mechanical low back pain (MLBP). Our hypothesis was that subjects with MLBP would demonstrate delayed muscle onset and have fewer muscles functioning in a feedforward manner than the control group. We further hypothesized that we would find differences between subgroups of our patients with MLBP, grouped according to diagnosis (segmental instability and noninstability). Case-control. Laboratory. Forty-three patients with chronic MLBP (25 instability, 18 noninstability) and 39 asymptomatic controls. Not applicable. Surface electromyography was used to measure onset time of 10 trunk muscles during a self-perturbation task. Trunk muscle onset latency relative to the anterior deltoid was calculated and the number of muscles functioning in feedforward determined. Activation timing patterns (Pfeedforward (P=.02; eta=.30; 1-beta=.83) were statistically different between patients with MLBP and controls. The control group activated the external oblique, lumbar multifidus, and erector spinae muscles in a feedforward manner. The heterogeneous MLBP group did not activate the trunk musculature in feedforward, but responded with significantly delayed activations. MLBP subgroups demonstrated significantly different timing patterns. The noninstability MLBP subgroup activated trunk extensors in a feedforward manner, similar to the control group, but significantly earlier than the instability subgroup. Lack of feedforward activation of selected trunk musculature in patients with MLBP may result in a period of inefficient muscular stabilization. Activation timing was more impaired in the instability than the noninstability MLBP subgroup. Training specifically for recruitment timing may be an important component of the rehabilitation program.

A new method to estimate parameters of linear compartmental models using artificial neural networks

International Nuclear Information System (INIS)

Gambhir, Sanjiv S.; Keppenne, Christian L.; Phelps, Michael E.; Banerjee, Pranab K.

1998-01-01

At present, the preferred tool for parameter estimation in compartmental analysis is an iterative procedure; weighted nonlinear regression. For a large number of applications, observed data can be fitted to sums of exponentials whose parameters are directly related to the rate constants/coefficients of the compartmental models. Since weighted nonlinear regression often has to be repeated for many different data sets, the process of fitting data from compartmental systems can be very time consuming. Furthermore the minimization routine often converges to a local (as opposed to global) minimum. In this paper, we examine the possibility of using artificial neural networks instead of weighted nonlinear regression in order to estimate model parameters. We train simple feed-forward neural networks to produce as outputs the parameter values of a given model when kinetic data are fed to the networks' input layer. The artificial neural networks produce unbiased estimates and are orders of magnitude faster than regression algorithms. At noise levels typical of many real applications, the neural networks are found to produce lower variance estimates than weighted nonlinear regression in the estimation of parameters from mono- and biexponential models. These results are primarily due to the inability of weighted nonlinear regression to converge. These results establish that artificial neural networks are powerful tools for estimating parameters for simple compartmental models. (author)

Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

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Jonathan Cannon

2015-11-01

Full Text Available Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.

Science.gov (United States)

Cannon, Jonathan; Kopell, Nancy; Gardner, Timothy; Markowitz, Jeffrey

2015-11-01

Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran

2017-08-17

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

Adaptive feedforward in the LANL rf control system

International Nuclear Information System (INIS)

Ziomek, C.D.

1992-01-01

This paper describes an adaptive feedforward system that corrects repetitive errors in the amplitude and phase of the RF field of a pulsed accelerator. High-frequency disturbances that are beyond the effective bandwidth of the RF field feedback control system can be eliminated with a feedforward system. Many RF field disturbances for a pulsed accelerator are repetitive, occurring at the same relative time in every pulse. This design employs digital signal processing hardware to adaptively determine and track the control signals required to eliminate the repetitive errors in the feedback control system. In order to provide the necessary high-frequency response, the adaptive feedforward hardware provides the calculated control signal prior to the repetitive disturbance that it corrects. This system has been demonstrated to reduce the transient disturbances caused by beam pulses. Furthermore, it has been shown to negate high-frequency phase and amplitude oscillations in a high-power klystron amplifier caused by PFN ripple on the high-voltage. The design and results of the adaptive feedforward system are presented

Failure detection studies by layered neural network

International Nuclear Information System (INIS)

Ciftcioglu, O.; Seker, S.; Turkcan, E.

1991-06-01

Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs

Adaptive Regularization of Neural Networks Using Conjugate Gradient

DEFF Research Database (Denmark)

Goutte, Cyril; Larsen, Jan

1998-01-01

Andersen et al. (1997) and Larsen et al. (1996, 1997) suggested a regularization scheme which iteratively adapts regularization parameters by minimizing validation error using simple gradient descent. In this contribution we present an improved algorithm based on the conjugate gradient technique........ Numerical experiments with feedforward neural networks successfully demonstrate improved generalization ability and lower computational cost...

Modelling the Flow Stress of Alloy 316L using a Multi-Layered Feed Forward Neural Network with Bayesian Regularization

Science.gov (United States)

Abiriand Bhekisipho Twala, Olufunminiyi

2017-08-01

In this paper, a multilayer feedforward neural network with Bayesian regularization constitutive model is developed for alloy 316L during high strain rate and high temperature plastic deformation. The input variables are strain rate, temperature and strain while the output value is the flow stress of the material. The results show that the use of Bayesian regularized technique reduces the potential of overfitting and overtraining. The prediction quality of the model is thereby improved. The model predictions are in good agreement with experimental measurements. The measurement data used for the network training and model comparison were taken from relevant literature. The developed model is robust as it can be generalized to deformation conditions slightly below or above the training dataset.

Recognition of decays of charged tracks with neural network techniques

International Nuclear Information System (INIS)

Stimpfl-Abele, G.

1991-01-01

We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

Directory of Open Access Journals (Sweden)

Mohammad S. Islam

2017-01-01

Full Text Available Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs for robust movement decoding of Parkinson’s disease (PD and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value at about 0.729±0.16 for decoding movement from the resting state and about 0.671±0.14 for decoding left and right visually cued movements.

Three-layer magnetoconvection

International Nuclear Information System (INIS)

Lin, M.-K.; Silvers, L.J.; Proctor, M.R.E.

2008-01-01

It is believed that some stars have two or more convection zones in close proximity near to the stellar photosphere. These zones are separated by convectively stable regions that are relatively narrow. Due to the close proximity of these regions it is important to construct mathematical models to understand the transport and mixing of passive and dynamic quantities. One key quantity of interest is a magnetic field, a dynamic vector quantity, that can drastically alter the convectively driven flows, and have an important role in coupling the different layers. In this Letter we present the first investigation into the effect of an imposed magnetic field in such a geometry. We focus our attention on the effect of field strength and show that, while there are some similarities with results for magnetic field evolution in a single layer, new and interesting phenomena are also present in a three layer system

High-performance control of a three-phase voltage-source converter including feedforward compensation of the estimated load current

International Nuclear Information System (INIS)

Leon, Andres E.; Solsona, Jorge A.; Busada, Claudio; Chiacchiarini, Hector; Valla, Maria Ines

2009-01-01

In this paper a new control strategy for voltage-source converters (VSC) is introduced. The proposed strategy consists of a nonlinear feedback controller based on feedback linearization plus a feedforward compensation of the estimated load current. In our proposal an energy function and the direct-axis current are considered as outputs, in order to avoid the internal dynamics. In this way, a full linearization is obtained via nonlinear transformation and feedback. An estimate of the load current is feedforwarded to improve the performance of the whole system and to diminish the capacitor size. This estimation allows to obtain a more rugged and cheaper implementation. The estimate is calculated by using a nonlinear reduced-order observer. The proposal is validated through different tests. These tests include performance in presence of switching frequency, measurement filters delays, parameters uncertainties and disturbances in the input voltage.

NL(q) Theory: A Neural Control Framework with Global Asymptotic Stability Criteria.

Science.gov (United States)

Vandewalle, Joos; De Moor, Bart L.R.; Suykens, Johan A.K.

1997-06-01

In this paper a framework for model-based neural control design is presented, consisting of nonlinear state space models and controllers, parametrized by multilayer feedforward neural networks. The models and closed-loop systems are transformed into so-called NL(q) system form. NL(q) systems represent a large class of nonlinear dynamical systems consisting of q layers with alternating linear and static nonlinear operators that satisfy a sector condition. For such NL(q)s sufficient conditions for global asymptotic stability, input/output stability (dissipativity with finite L(2)-gain) and robust stability and performance are presented. The stability criteria are expressed as linear matrix inequalities. In the analysis problem it is shown how stability of a given controller can be checked. In the synthesis problem two methods for neural control design are discussed. In the first method Narendra's dynamic backpropagation for tracking on a set of specific reference inputs is modified with an NL(q) stability constraint in order to ensure, e.g., closed-loop stability. In a second method control design is done without tracking on specific reference inputs, but based on the input/output stability criteria itself, within a standard plant framework as this is done, for example, in H( infinity ) control theory and &mgr; theory. Copyright 1997 Elsevier Science Ltd.

Neural network approximation of tip-abrasion effects in AFM imaging

International Nuclear Information System (INIS)

Bakucz, Peter; Dziomba, Thorsten; Koenders, Ludger; Krüger-Sehm, Rolf; Yacoot, Andrew

2008-01-01

The abrasion (wear) of tips used in scanning force microscopy (SFM) directly influences SFM image quality and is therefore of great relevance to quantitative SFM measurements. The increasing implementation of automated SFM measurement schemes has become a strong driving force for increasing efforts towards the prediction of tip wear, as it needs to be ensured that the probe is exchanged before a level of tip wear is reached that adversely affects the measurement quality. In this paper, we describe the identification of tip abrasion in a system of SFM measurements. We attempt to model the tip-abrasion process as a concatenation of a mapping from the measured AFM data to a regression vector and a nonlinear mapping from the regressor space to the output space. The mapping is formed as a basis function expansion. Feedforward neural networks are used to approximate this mapping. The one-hidden layer network gave a good quality of fit for the training and test sets for the tip-abrasion system. We illustrate our method with AFM measurements of both fine periodic structures and randomly oriented sharp features and compare our neural network results with those obtained using other methods

Neural network approximation of tip-abrasion effects in AFM imaging

Science.gov (United States)

Bakucz, Peter; Yacoot, Andrew; Dziomba, Thorsten; Koenders, Ludger; Krüger-Sehm, Rolf

2008-06-01

The abrasion (wear) of tips used in scanning force microscopy (SFM) directly influences SFM image quality and is therefore of great relevance to quantitative SFM measurements. The increasing implementation of automated SFM measurement schemes has become a strong driving force for increasing efforts towards the prediction of tip wear, as it needs to be ensured that the probe is exchanged before a level of tip wear is reached that adversely affects the measurement quality. In this paper, we describe the identification of tip abrasion in a system of SFM measurements. We attempt to model the tip-abrasion process as a concatenation of a mapping from the measured AFM data to a regression vector and a nonlinear mapping from the regressor space to the output space. The mapping is formed as a basis function expansion. Feedforward neural networks are used to approximate this mapping. The one-hidden layer network gave a good quality of fit for the training and test sets for the tip-abrasion system. We illustrate our method with AFM measurements of both fine periodic structures and randomly oriented sharp features and compare our neural network results with those obtained using other methods.

A One-Layer Recurrent Neural Network for Real-Time Portfolio Optimization With Probability Criterion.

Science.gov (United States)

Liu, Qingshan; Dang, Chuangyin; Huang, Tingwen

2013-02-01

This paper presents a decision-making model described by a recurrent neural network for dynamic portfolio optimization. The portfolio-optimization problem is first converted into a constrained fractional programming problem. Since the objective function in the programming problem is not convex, the traditional optimization techniques are no longer applicable for solving this problem. Fortunately, the objective function in the fractional programming is pseudoconvex on the feasible region. It leads to a one-layer recurrent neural network modeled by means of a discontinuous dynamic system. To ensure the optimal solutions for portfolio optimization, the convergence of the proposed neural network is analyzed and proved. In fact, the neural network guarantees to get the optimal solutions for portfolio-investment advice if some mild conditions are satisfied. A numerical example with simulation results substantiates the effectiveness and illustrates the characteristics of the proposed neural network.

ECO INVESTMENT PROJECT MANAGEMENT THROUGH TIME APPLYING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Tamara Gvozdenović

2007-06-01

Full Text Available he concept of project management expresses an indispensable approach to investment projects. Time is often the most important factor in these projects. The artificial neural network is the paradigm of data processing, which is inspired by the one used by the biological brain, and it is used in numerous, different fields, among which is the project management. This research is oriented to application of artificial neural networks in managing time of investment project. The artificial neural networks are used to define the optimistic, the most probable and the pessimistic time in PERT method. The program package Matlab: Neural Network Toolbox is used in data simulation. The feed-forward back propagation network is chosen.

Semiconductor Three-Dimensional Photonic Crystals with Novel Layer-by-Layer Structures

Directory of Open Access Journals (Sweden)

Satoshi Iwamoto

2016-05-01

Full Text Available Three-dimensional photonic crystals (3D PhCs are a fascinating platform for manipulating photons and controlling their interactions with matter. One widely investigated structure is the layer-by-layer woodpile structure, which possesses a complete photonic bandgap. On the other hand, other types of 3D PhC structures also offer various possibilities for controlling light by utilizing the three dimensional nature of structures. In this article, we discuss our recent research into novel types of layer-by-layer structures, including the experimental demonstration of a 3D PhC nanocavity formed in a <110>-layered diamond structure and the realization of artificial optical activity in rotationally stacked woodpile structures.

Neural controller for adaptive movements with unforeseen payloads.

Science.gov (United States)

Kuperstein, M; Wang, J

1990-01-01

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

A quantum-implementable neural network model

Science.gov (United States)

Chen, Jialin; Wang, Lingli; Charbon, Edoardo

2017-10-01

A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

Science.gov (United States)

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Design of a universal two-layered neural network derived from the PLI theory

Science.gov (United States)

Hu, Chia-Lun J.

2004-05-01

The if-and-only-if (IFF) condition that a set of M analog-to-digital vector-mapping relations can be learned by a one-layered-feed-forward neural network (OLNN) is that all the input analog vectors dichotomized by the i-th output bit must be positively, linearly independent, or PLI. If they are not PLI, then the OLNN just cannot learn no matter what learning rules is employed because the solution of the connection matrix does not exist mathematically. However, in this case, one can still design a parallel-cascaded, two-layered, perceptron (PCTLP) to acheive this general mapping goal. The design principle of this "universal" neural network is derived from the major mathematical properties of the PLI theory - changing the output bits of the dependent relations existing among the dichotomized input vectors to make the PLD relations PLI. Then with a vector concatenation technique, the required mapping can still be learned by this PCTLP system with very high efficiency. This paper will report in detail the mathematical derivation of the general design principle and the design procedures of the PCTLP neural network system. It then will be verified in general by a practical numerical example.

Grid-Voltage-Feedforward Active Damping for Grid-Connected Inverter with LCL Filter

DEFF Research Database (Denmark)

Lu, Minghui; Wang, Xiongfei; Blaabjerg, Frede

2016-01-01

For the grid-connected voltage source inverters, the feedforward scheme of grid voltage is commonly adopted to mitigate the current distortion caused by grid background voltages harmonics. This paper investigates the grid-voltage-feedforward active damping for grid connected inverter with LCL...... filter. It reveals that proportional feedforward control can not only fulfill the mitigation of grid disturbance, but also offer damping effects on the LCL filter resonance. Digital delays are intrinsic to digital controlled inverters; with these delays, the feedforward control can be equivalent...

Three Pillars for the Neural Control of Appetite.

Science.gov (United States)

Sternson, Scott M; Eiselt, Anne-Kathrin

2017-02-10

The neural control of appetite is important for understanding motivated behavior as well as the present rising prevalence of obesity. Over the past several years, new tools for cell type-specific neuron activity monitoring and perturbation have enabled increasingly detailed analyses of the mechanisms underlying appetite-control systems. Three major neural circuits strongly and acutely influence appetite but with notably different characteristics. Although these circuits interact, they have distinct properties and thus appear to contribute to separate but interlinked processes influencing appetite, thereby forming three pillars of appetite control. Here, we summarize some of the key characteristics of appetite circuits that are emerging from recent work and synthesize the findings into a provisional framework that can guide future studies.

Video Feedforward for Rapid Learning of a Picture-Based Communication System

Science.gov (United States)

Smith, Jemma; Hand, Linda; Dowrick, Peter W.

2014-01-01

This study examined the efficacy of video self modeling (VSM) using feedforward, to teach various goals of a picture exchange communication system (PECS). The participants were two boys with autism and one man with Down syndrome. All three participants were non-verbal with no current functional system of communication; the two children had long…

Measuring Feedforward Inhibition and Its Impact on Local Circuit Function.

Science.gov (United States)

Hull, Court

2017-05-01

This protocol describes a series of approaches to measure feedforward inhibition in acute brain slices from the cerebellar cortex. Using whole-cell voltage and current clamp recordings from Purkinje cells in conjunction with electrical stimulation of the parallel fibers, these methods demonstrate how to measure the relationship between excitation and inhibition in a feedforward circuit. This protocol also describes how to measure the impact of feedforward inhibition on Purkinje cell excitability, with an emphasis on spike timing. © 2017 Cold Spring Harbor Laboratory Press.

A one-layer recurrent neural network for non-smooth convex optimization subject to linear inequality constraints

International Nuclear Information System (INIS)

Liu, Xiaolan; Zhou, Mi

2016-01-01

In this paper, a one-layer recurrent network is proposed for solving a non-smooth convex optimization subject to linear inequality constraints. Compared with the existing neural networks for optimization, the proposed neural network is capable of solving more general convex optimization with linear inequality constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds.

Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.

Science.gov (United States)

Kriegeskorte, Nikolaus

2015-11-24

Recent advances in neural network modeling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain, and their computations could be implemented in biological neurons. Convolutional feedforward networks, which now dominate computer vision, take further inspiration from the architecture of the primate visual hierarchy. However, the current models are designed with engineering goals, not to model brain computations. Nevertheless, initial studies comparing internal representations between these models and primate brains find surprisingly similar representational spaces. With human-level performance no longer out of reach, we are entering an exciting new era, in which we will be able to build biologically faithful feedforward and recurrent computational models of how biological brains perform high-level feats of intelligence, including vision.

Applying neural networks as software sensors for enzyme engineering.

Science.gov (United States)

Linko, S; Zhu, Y H; Linko, P

1999-04-01

The on-line control of enzyme-production processes is difficult, owing to the uncertainties typical of biological systems and to the lack of suitable on-line sensors for key process variables. For example, intelligent methods to predict the end point of fermentation could be of great economic value. Computer-assisted control based on artificial-neural-network models offers a novel solution in such situations. Well-trained feedforward-backpropagation neural networks can be used as software sensors in enzyme-process control; their performance can be affected by a number of factors.

Prediction of pelvic organ prolapse using an artificial neural network.

Science.gov (United States)

Robinson, Christopher J; Swift, Steven; Johnson, Donna D; Almeida, Jonas S

2008-08-01

The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support. Following institutional review board approval, patients with POP (n = 87) and controls with good pelvic organ support (n = 368) were identified from the urogynecology research database. Historical and clinical information was extracted from the database. Data analysis included the training of a feedforward ANN, variable selection, and external validation of the model with an independent data set. Twenty variables were used. The median-performing ANN model used a median of 3 (quartile 1:3 to quartile 3:5) variables and achieved an area under the receiver operator curve of 0.90 (external, independent validation set). Ninety percent sensitivity and 83% specificity were obtained in the external validation by ANN classification. Feedforward ANN modeling is applicable to the identification and prediction of POP.

Neural network-based model reference adaptive control system.

Science.gov (United States)

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.

Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data

Directory of Open Access Journals (Sweden)

Yasir Hassan Ali

2015-01-01

Full Text Available The thickness of an oil film lubricant can contribute to less gear tooth wear and surface failure. The purpose of this research is to use artificial neural network (ANN computational modelling to correlate spur gear data from acoustic emissions, lubricant temperature, and specific film thickness (λ. The approach is using an algorithm to monitor the oil film thickness and to detect which lubrication regime the gearbox is running either hydrodynamic, elastohydrodynamic, or boundary. This monitoring can aid identification of fault development. Feed-forward and recurrent Elman neural network algorithms were used to develop ANN models, which are subjected to training, testing, and validation process. The Levenberg-Marquardt back-propagation algorithm was applied to reduce errors. Log-sigmoid and Purelin were identified as suitable transfer functions for hidden and output nodes. The methods used in this paper shows accurate predictions from ANN and the feed-forward network performance is superior to the Elman neural network.

Nonlinear Feedforward Control for Wind Disturbance Rejection on Autonomous Helicopter

DEFF Research Database (Denmark)

Bisgaard, Morten; la Cour-Harbo, Anders; A. Danapalasingam, Kumeresan

2010-01-01

for the purpose. The model is inverted for the calculation of rotor collective and cyclic pitch angles given the wind disturbance. The control strategy is then applied on a small helicopter in a controlled wind environment and flight tests demonstrates the effectiveness and advantage of the feedforward controller.......This paper presents the design and verification of a model based nonlinear feedforward controller for wind disturbance rejection on autonomous helicopters. The feedforward control is based on a helicopter model that is derived using a number of carefully chosen simplifications to make it suitable...

Nonlinear adaptive inverse control via the unified model neural network

Science.gov (United States)

Jeng, Jin-Tsong; Lee, Tsu-Tian

1999-03-01

In this paper, we propose a new nonlinear adaptive inverse control via a unified model neural network. In order to overcome nonsystematic design and long training time in nonlinear adaptive inverse control, we propose the approximate transformable technique to obtain a Chebyshev Polynomials Based Unified Model (CPBUM) neural network for the feedforward/recurrent neural networks. It turns out that the proposed method can use less training time to get an inverse model. Finally, we apply this proposed method to control magnetic bearing system. The experimental results show that the proposed nonlinear adaptive inverse control architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

Neural networks for sensor validation and plant monitoring

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

1990-01-01

Sensor and process monitoring in power plants require the estimation of one or more process variables. Neural network paradigms are suitable for establishing general nonlinear relationships among a set of plant variables. Multiple-input multiple-output autoassociative networks can follow changes in plant-wide behavior. The backpropagation algorithm has been applied for training feedforward networks. A new and enhanced algorithm for training neural networks (BPN) has been developed and implemented in a VAX workstation. Operational data from the Experimental Breeder Reactor-II (EBR-II) have been used to study the performance of BPN. Several results of application to the EBR-II are presented

Dynamic Pricing in Electronic Commerce Using Neural Network

Science.gov (United States)

Ghose, Tapu Kumar; Tran, Thomas T.

In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.

Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex.

OpenAIRE

Swadlow, Harvey A

2002-01-01

Intracortical inhibition plays a role in shaping sensory cortical receptive fields and is mediated by both feed-forward and feedback mechanisms. Feed-forward inhibition is the faster of the two processes, being generated by inhibitory interneurons driven by monosynaptic thalamocortical (TC) input. In principle, feed-forward inhibition can prevent targeted cortical neurons from ever reaching threshold when TC input is weak. To do so, however, inhibitory interneurons must respond to TC input at...

Backpropagation Neural Ensemble for Localizing and Recognizing Non-Standardized Malaysia’s Car Plates

OpenAIRE

Chin Kim On; Teo Kein Yau; Rayner Alfred; Jason Teo; Patricia Anthony; Wang Cheng

2016-01-01

In this paper, we describe a research project that autonomously localizes and recognizes non-standardized Malaysian’s car plates using conventional Backpropagation algorithm (BPP) in combination with Ensemble Neural Network (ENN). We compared the results with the results obtained using simple Feed-Forward Neural Network (FFNN). This research aims to solve four main issues; (1) localization of car plates that has the same colour with the vehicle colour, (2) detection and recognition of car pla...

Analysis and optimization of gas-centrifugal separation of uranium isotopes by neural networks

Directory of Open Access Journals (Sweden)

Migliavacca S.C.P.

2002-01-01

Full Text Available Neural networks are an attractive alternative for modeling complex problems with too many difficulties to be solved by a phenomenological model. A feed-forward neural network was used to model a gas-centrifugal separation of uranium isotopes. The prediction showed good agreement with the experimental data. An optimization study was carried out. The optimal operational condition was tested by a new experiment and a difference of less than 1% was found.

Feedforward somatosensory inhibition is normal in cervical dystonia.

Science.gov (United States)

Ferrè, Elisa R; Ganos, Christos; Bhatia, Kailash P; Haggard, Patrick

2015-03-01

Insufficient cortical inhibition is a key pathophysiological finding in dystonia. Subliminal sensory stimuli were reported to transiently inhibit somatosensory processing. Here we investigated whether such subliminal feedforward inhibition is reduced in patients with cervical dystonia. Sixteen cervical dystonia patients and 16 matched healthy controls performed a somatosensory detection task. We measured the drop in sensitivity to detect a threshold-level digital nerve shock when it was preceded by a subliminal conditioning shock, compared to when it was not. Subliminal conditioning shocks reduced sensitivity to threshold stimuli to a similar extent in both patients and controls, suggesting that somatosensory subliminal feedforward inhibition is normal in cervical dystonia. Somatosensory feedforward inhibition was normal in this group of cervical dystonia patients. Our results qualify previous concepts of a general dystonic deficit in sensorimotor inhibitory processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control.

Science.gov (United States)

Drop, Frank M; Pool, Daan M; van Paassen, Marinus Rene M; Mulder, Max; Bulthoff, Heinrich H

2018-01-01

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.

Notions of local controllability and optimal feedforward control for quantum systems

International Nuclear Information System (INIS)

Chakrabarti, Raj

2011-01-01

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Notions of local controllability and optimal feedforward control for quantum systems

Energy Technology Data Exchange (ETDEWEB)

Chakrabarti, Raj, E-mail: rchakra@purdue.edu [School of Chemical Engineering, Purdue University, West Lafayette, IN 47907 (United States)

2011-05-06

Local controllability is an essential concept for regulation and control of time-varying nonlinear dynamical systems; in the classical control logic it is at the foundation of neighboring optimal feedback and feedforward control. We introduce notions of local controllability suited to feedforward control of classical input disturbances in bilinear quantum systems evolving on projective spaces and Lie groups. Tests for local controllability based on a Gramian matrix analogous to the nonlinear local controllability Gramian, which allow assessment of which trajectories can be regulated by perturbative feedforward in the presence of classical input noise, are presented. These notions explicitly incorporate system bilinearity and the geometry of quantum states into the definition of local controllability of quantum systems. Associated feedforward strategies are described.

Experimental calibration of forward and inverse neural networks for rotary type magnetorheological damper

DEFF Research Database (Denmark)

Bhowmik, Subrata; Weber, Felix; Høgsberg, Jan Becker

2013-01-01

This paper presents a systematic design and training procedure for the feed-forward backpropagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output...

3D Polygon Mesh Compression with Multi Layer Feed Forward Neural Networks

Directory of Open Access Journals (Sweden)

Emmanouil Piperakis

2003-06-01

Full Text Available In this paper, an experiment is conducted which proves that multi layer feed forward neural networks are capable of compressing 3D polygon meshes. Our compression method not only preserves the initial accuracy of the represented object but also enhances it. The neural network employed includes the vertex coordinates, the connectivity and normal information in one compact form, converting the discrete and surface polygon representation into an analytic, solid colloquial. Furthermore, the 3D object in its compressed neural form can be directly - without decompression - used for rendering. The neural compression - representation is viable to 3D transformations without the need of any anti-aliasing techniques - transformations do not disrupt the accuracy of the geometry. Our method does not su.er any scaling problem and was tested with objects of 300 to 107 polygons - such as the David of Michelangelo - achieving in all cases an order of O(b3 less bits for the representation than any other commonly known compression method. The simplicity of our algorithm and the established mathematical background of neural networks combined with their aptness for hardware implementation can establish this method as a good solution for polygon compression and if further investigated, a novel approach for 3D collision, animation and morphing.

Three-dimensional sound localisation with a lizard peripheral auditory model

DEFF Research Database (Denmark)

Kjær Schmidt, Michael; Shaikh, Danish

the networks learned a transfer function that translated the three-dimensional non-linear mapping into estimated azimuth and elevation values for the acoustic target. The neural network with two hidden layers as expected performed better than that with only one hidden layer. Our approach assumes that for any...... location of an acoustic target in three dimensions. Our approach utilises a model of the peripheral auditory system of lizards [Christensen-Dalsgaard and Manley 2005] coupled with a multi-layer perceptron neural network. The peripheral auditory model’s response to sound input encodes sound direction...... information in a single plane which by itself is insufficient to localise the acoustic target in three dimensions. A multi-layer perceptron neural network is used to combine two independent responses of the model, corresponding to two rotational movements, into an estimate of the sound direction in terms...

Coulomb oscillations in three-layer graphene nanostructures

International Nuclear Information System (INIS)

Guettinger, J; Stampfer, C; Molitor, F; Graf, D; Ihn, T; Ensslin, K

2008-01-01

We present transport measurements on a tunable three-layer graphene single electron transistor (SET). The device consists of an etched three-layer graphene flake with two narrow constrictions separating the island from source and drain contacts. Three lateral graphene gates are used to electrostatically tune the device. An individual three-layer graphene constriction has been investigated separately showing a transport gap near the charge neutrality point. The graphene tunneling barriers show a strongly nonmonotonic coupling as a function of gate voltage indicating the presence of localized states in the constrictions. We show Coulomb oscillations and Coulomb diamond measurements proving the functionality of the graphene SET. A charging energy of ∼0.6 meV is extracted.

Distinct GABAergic targets of feedforward and feedback connections between lower and higher areas of rat visual cortex.

Science.gov (United States)

Gonchar, Yuri; Burkhalter, Andreas

2003-11-26

Processing of visual information is performed in different cortical areas that are interconnected by feedforward (FF) and feedback (FB) pathways. Although FF and FB inputs are excitatory, their influences on pyramidal neurons also depend on the outputs of GABAergic neurons, which receive FF and FB inputs. Rat visual cortex contains at least three different families of GABAergic neurons that express parvalbumin (PV), calretinin (CR), and somatostatin (SOM) (Gonchar and Burkhalter, 1997). To examine whether pathway-specific inhibition (Shao and Burkhalter, 1996) is attributable to distinct connections with GABAergic neurons, we traced FF and FB inputs to PV, CR, and SOM neurons in layers 1-2/3 of area 17 and the secondary lateromedial area in rat visual cortex. We found that in layer 2/3 maximally 2% of FF and FB inputs go to CR and SOM neurons. This contrasts with 12-13% of FF and FB inputs onto layer 2/3 PV neurons. Unlike inputs to layer 2/3, connections to layer 1, which contains CR but lacks SOM and PV somata, are pathway-specific: 21% of FB inputs go to CR neurons, whereas FF inputs to layer 1 and its CR neurons are absent. These findings suggest that FF and FB influences on layer 2/3 pyramidal neurons mainly involve disynaptic connections via PV neurons that control the spike outputs to axons and proximal dendrites. Unlike FF input, FB input in addition makes a disynaptic link via CR neurons, which may influence the excitability of distal pyramidal cell dendrites in layer 1.

Application of artificial neural networks in particle physics

International Nuclear Information System (INIS)

Kolanoski, H.

1995-04-01

The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

Science.gov (United States)

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning feedback and feedforward control in a mirror-reversed visual environment.

Science.gov (United States)

Kasuga, Shoko; Telgen, Sebastian; Ushiba, Junichi; Nozaki, Daichi; Diedrichsen, Jörn

2015-10-01

When we learn a novel task, the motor system needs to acquire both feedforward and feedback control. Currently, little is known about how the learning of these two mechanisms relate to each other. In the present study, we tested whether feedforward and feedback control need to be learned separately, or whether they are learned as common mechanism when a new control policy is acquired. Participants were trained to reach to two lateral and one central target in an environment with mirror (left-right)-reversed visual feedback. One group was allowed to make online movement corrections, whereas the other group only received visual information after the end of the movement. Learning of feedforward control was assessed by measuring the accuracy of the initial movement direction to lateral targets. Feedback control was measured in the responses to sudden visual perturbations of the cursor when reaching to the central target. Although feedforward control improved in both groups, it was significantly better when online corrections were not allowed. In contrast, feedback control only adaptively changed in participants who received online feedback and remained unchanged in the group without online corrections. Our findings suggest that when a new control policy is acquired, feedforward and feedback control are learned separately, and that there may be a trade-off in learning between feedback and feedforward controllers. Copyright © 2015 the American Physiological Society.

Modelling of word usage frequency dynamics using artificial neural network

International Nuclear Information System (INIS)

Maslennikova, Yu S; Bochkarev, V V; Voloskov, D S

2014-01-01

In this paper the method for modelling of word usage frequency time series is proposed. An artificial feedforward neural network was used to predict word usage frequencies. The neural network was trained using the maximum likelihood criterion. The Google Books Ngram corpus was used for the analysis. This database provides a large amount of data on frequency of specific word forms for 7 languages. Statistical modelling of word usage frequency time series allows finding optimal fitting and filtering algorithm for subsequent lexicographic analysis and verification of frequency trend models

Flood routing modelling with Artificial Neural Networks

Directory of Open Access Journals (Sweden)

R. Peters

2006-01-01

Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

Feedforward signal prediction for accurate motion systems using digital filters

NARCIS (Netherlands)

Butler, H.

2012-01-01

A positioning system that needs to accurately track a reference can benefit greatly from using feedforward. When using a force actuator, the feedforward needs to generate a force proportional to the reference acceleration, which can be measured by means of an accelerometer or can be created by

On Optimal Input Design for Feed-forward Control

OpenAIRE

Hägg, Per; Wahlberg, Bo

2013-01-01

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in a system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are an...

Strength of the Three Layer Beam with Two Binding Layers

Directory of Open Access Journals (Sweden)

Smyczyński M. J.

2016-09-01

Full Text Available The paper is devoted to the strength analysis of a simply supported three layer beam. The sandwich beam consists of: two metal facings, the metal foam core and two binding layers between the faces and the core. In consequence, the beam is a five layer beam. The main goal of the study is to elaborate a mathematical model of this beam, analytical description and a solution of the three-point bending problem. The beam is subjected to a transverse load. The nonlinear hypothesis of the deformation of the cross section of the beam is formulated. Based on the principle of the stationary potential energy the system of four equations of equilibrium is derived. Then deflections and stresses are determined. The influence of the binding layers is considered. The results of the solutions of the bending problem analysis are shown in the tables and figures. The analytical model is verified numerically using the finite element analysis, as well as experimentally.

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

... in the subsurface study of the earth takes into account the model parameters in ... and thickness of individual subsurface layers using the trained synthetic data by ... feed-forward neural network with fast back propagation learning algorithm.

Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

Directory of Open Access Journals (Sweden)

Md. Abdullah-al-mamun

2015-08-01

Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

Objective Model Selection for Identifying the Human Feedforward Response in Manual Control

OpenAIRE

Drop, F.M.; Pool, D.M.; van Paassen, M.M.; Mulder, M.; Bülthoff, Heinrich H.

2017-01-01

Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification proce...

Energy efficient three-layer panels and elastic compliance of their middle layer

Directory of Open Access Journals (Sweden)

Petrov Stanislav

2017-01-01

Full Text Available Three-layer panels are referred to light weight energy efficient building envelopes. According to current trends, mineral wool from basalt fiber is preferable to be used as panels middle layer. All three-layers of the construction together account for mechanical properties, though these layers taken separately have very different mechanical properties. The work of such a composite design has a number of features that require careful consideration when calculating the panels for strength. Thus, it has not yet been described how squeeze reduction of a relatively soft middle layer affects the load bearing capacity of a panel. When panels are exposed to external loads, their middle layer is squeezed thus changing the characteristics of the panel. This effect is particularly evident in supporting structures. Besides, squeeze reduction of the middle layer changes its elastic-plastic propeties. The purpose of this work is to study the effect of the middle layer of an energy efficient panel squeeze reduction on its load bearing capacity. When solving this task, the authors worked out a methodology which takes into account squeeze reduction of a middle layer and its effect on load bearing capacity of the panel. The researches introduced an algorithm for solving this task and created a tool that allows to easily receive the exact solution. The paper presents this methodology and describes a computer program for calculating three-layer panels with account of changing elastic compliance of a middle layer. The main result of the work is an extended methodology of calculation of the panels and an obtained engineering tool that allows to quickly obtain an extended solution.

Enhancing the top-quark signal at Fermilab Tevatron using neural nets

International Nuclear Information System (INIS)

Ametller, L.; Garrido, L.; Talavera, P.

1994-01-01

We show, in agreement with previous studies, that neural nets can be useful for top-quark analysis at the Fermilab Tevatron. The main features of t bar t and background events in a mixed sample are projected on a single output, which controls the efficiency, purity, and statistical significance of the t bar t signal. We consider a feed-forward multilayer neural net for the CDF reported top-quark mass, using six kinematical variables as inputs. Our main results are based on the exhaustive comparison of the neural net performances with those obtainable from the standard experimental analysis, by imposing different sets of linear cuts over the same variables, showing how the neural net approach improves the standard analysis results

Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

International Nuclear Information System (INIS)

Wang Jiangfeng; Sun Zhixin; Dai Yiping; Ma Shaolin

2010-01-01

Supercritical CO 2 power cycle shows a high potential to recover low-grade waste heat due to its better temperature glide matching between heat source and working fluid in the heat recovery vapor generator (HRVG). Parametric analysis and exergy analysis are conducted to examine the effects of thermodynamic parameters on the cycle performance and exergy destruction in each component. The thermodynamic parameters of the supercritical CO 2 power cycle is optimized with exergy efficiency as an objective function by means of genetic algorithm (GA) under the given waste heat condition. An artificial neural network (ANN) with the multi-layer feed-forward network type and back-propagation training is used to achieve parametric optimization design rapidly. It is shown that the key thermodynamic parameters, such as turbine inlet pressure, turbine inlet temperature and environment temperature have significant effects on the performance of the supercritical CO 2 power cycle and exergy destruction in each component. It is also shown that the optimum thermodynamic parameters of supercritical CO 2 power cycle can be predicted with good accuracy using artificial neural network under variable waste heat conditions.

USE OF DASHBOARDS IN PREDICTING THE DEVELOPMENT OF THE COMPANY USING NEURAL NETWORKS IN HOTEL MANAGEMENT

Directory of Open Access Journals (Sweden)

Peter GALLO

2018-04-01

Full Text Available Tourism development currently represents a very important part of national economics and its development and growth. To ensure growth, managers are looking for new effective tools to optimize decision making. This paper addresses the issue of dashboards based on neural networks and their utilization in managerial decision-making processes. Dashboard based reporting is oriented towards the tourism sector in Slovakia. The result of the research is the proposed balanced ranking and prediction model using financial and nonfinancial indicators with the application of artificial intelligence which allows to reach high level of efficiency and accuracy in evaluation of financial and nonfinancial health of companies operating in the hospitality sector. The proposed model also brings a new managerial and scientific point of view on the in-depth analysis of performance of these facilities. The main function of the proposed model is to classify health of a hotel. For this purpose, the MLP (Multi-Layer Perceptron feedforward artificial neural network using backward propagation of errors was chosen as a training method.

Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

Science.gov (United States)

Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

2012-04-01

Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily

Hybrid Feedforward-Feedback Noise Control Using Virtual Sensors

Science.gov (United States)

Bean, Jacob; Fuller, Chris; Schiller, Noah

2016-01-01

Several approaches to active noise control using virtual sensors are evaluated for eventual use in an active headrest. Specifically, adaptive feedforward, feedback, and hybrid control structures are compared. Each controller incorporates the traditional filtered-x least mean squares algorithm. The feedback controller is arranged in an internal model configuration to draw comparisons with standard feedforward control theory results. Simulation and experimental results are presented that illustrate each controllers ability to minimize the pressure at both physical and virtual microphone locations. The remote microphone technique is used to obtain pressure estimates at the virtual locations. It is shown that a hybrid controller offers performance benefits over the traditional feedforward and feedback controllers. Stability issues associated with feedback and hybrid controllers are also addressed. Experimental results show that 15-20 dB reduction in broadband disturbances can be achieved by minimizing the measured pressure, whereas 10-15 dB reduction is obtained when minimizing the estimated pressure at a virtual location.

Robust Position Tracking for Electro-Hydraulic Drives Based on Generalized Feedforward Compensation Approach

DEFF Research Database (Denmark)

Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.

2012-01-01

This paper presents a robust tracking control concept based on accurate feedforward compensation for hydraulic valve-cylinder drives. The proposed feedforward compensator is obtained utilizing a generalized description of the valve flow that takes into account any asymmetry of valves and...... constant gain type feedforward compensator, when subjected to strong perturbations in supply pressure and coulomb friction....

Neural network training by Kalman filtering in process system monitoring

International Nuclear Information System (INIS)

Ciftcioglu, Oe.

1996-03-01

Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

A one-layer recurrent neural network for constrained pseudoconvex optimization and its application for dynamic portfolio optimization.

Science.gov (United States)

Liu, Qingshan; Guo, Zhishan; Wang, Jun

2012-02-01

In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application of artificial neural network for medical image recognition and diagnostic decision making

International Nuclear Information System (INIS)

Asada, N.; Eiho, S.; Doi, K.; MacMahon, H.; Montner, S.M.; Giger, M.L.

1989-01-01

An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case

Neural control and transient analysis of the LCL-type resonant converter

Science.gov (United States)

Zouggar, S.; Nait Charif, H.; Azizi, M.

2000-07-01

This paper proposes a generalised inverse learning structure to control the LCL converter. A feedforward neural network is trained to act as an inverse model of the LCL converter then both are cascaded such that the composed system results in an identity mapping between desired response and the LCL output voltage. Using the large signal model, we analyse the transient output response of the controlled LCL converter in the case of large variation of the load. The simulation results show the efficiency of using neural networks to regulate the LCL converter.

Particle identification with neural networks using a rotational invariant moment representation

International Nuclear Information System (INIS)

Sinkus, R.

1997-01-01

A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

Feed-Forward Control of Kite Power Systems

International Nuclear Information System (INIS)

Fechner, Uwe; Schmehl, Roland

2014-01-01

Kite power technology is a novel solution to harvest wind energy from altitudes that can not be reached by conventional wind turbines. The use of a lightweight but strong tether in place of an expensive tower provides an additional cost advantage, next to the higher capacity factor. This paper describes a method to estimate the wind velocity at the kite using measurement data at the kite and at the ground. Focussing on a kite power system, which is converting the traction power of a kite in a pumping mode of operation, a reel-out speed predictor is presented for use in feed-forward control of the tether reel-out speed of the winch. The results show, that the developed feedforward controller improves the force control accuracy by a factor of two compared to the previously used feedback controller. This allows to use a higher set force during the reel-out phase which in turn increases the average power output by more than 4%. Due to its straightforward implementation and low computational requirements feedforward control is considered a promising technique for the reliable and efficient operation of traction-based kite power systems

Design and analysis of three-layer-core optical fiber

Science.gov (United States)

Zheng, Siwen; Liu, Yazhuo; Chang, Guangjian

2018-03-01

A three-layer-core single-mode large-mode-area fiber is investigated. The three-layer structure in the core, which is composed of a core-index layer, a cladding-index layer, and a depression-index layer, could achieve a large effective area Aeff while maintaining an ultralow bending loss without deteriorating cutoff behaviors. The single-mode large mode area of 100 to 330 μm2 could be achieved in the fiber. The effective area Aeff can be further enlarged by adjusting the layer parameters. Furthermore, the bending property could be improved in this three-layer-core structure. The bending loss could decrease by 2 to 4 orders of magnitude compared with the conventional step-index fiber with the same Aeff. These characteristics of three-layer-core fiber suggest that it can be used in large-mode-area wide-bandwidth high-capacity transmission or high-power optical fiber laser and amplifier in optical communications, which could be used for the basic physical layer structure of big data storage, reading, calculation, and transmission applications.

Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm

Directory of Open Access Journals (Sweden)

Jeng-Fung Chen

2014-11-01

Full Text Available The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam’s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO and back-propagation (BP, indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting.

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...

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...

Artificial neural network for suppression of banding artifacts in balanced steady-state free precession MRI.

Science.gov (United States)

Kim, Ki Hwan; Park, Sung-Hong

2017-04-01

The balanced steady-state free precession (bSSFP) MR sequence is frequently used in clinics, but is sensitive to off-resonance effects, which can cause banding artifacts. Often multiple bSSFP datasets are acquired at different phase cycling (PC) angles and then combined in a special way for banding artifact suppression. Many strategies of combining the datasets have been suggested for banding artifact suppression, but there are still limitations in their performance, especially when the number of phase-cycled bSSFP datasets is small. The purpose of this study is to develop a learning-based model to combine the multiple phase-cycled bSSFP datasets for better banding artifact suppression. Multilayer perceptron (MLP) is a feedforward artificial neural network consisting of three layers of input, hidden, and output layers. MLP models were trained by input bSSFP datasets acquired from human brain and knee at 3T, which were separately performed for two and four PC angles. Banding-free bSSFP images were generated by maximum-intensity projection (MIP) of 8 or 12 phase-cycled datasets and were used as targets for training the output layer. The trained MLP models were applied to another brain and knee datasets acquired with different scan parameters and also to multiple phase-cycled bSSFP functional MRI datasets acquired on rat brain at 9.4T, in comparison with the conventional MIP method. Simulations were also performed to validate the MLP approach. Both the simulations and human experiments demonstrated that MLP suppressed banding artifacts significantly, superior to MIP in both banding artifact suppression and SNR efficiency. MLP demonstrated superior performance over MIP for the 9.4T fMRI data as well, which was not used for training the models, while visually preserving the fMRI maps very well. Artificial neural network is a promising technique for combining multiple phase-cycled bSSFP datasets for banding artifact suppression. Copyright © 2016 Elsevier Inc. All

Gaze stabilization in chronic vestibular-loss and in cerebellar ataxia: interactions of feedforward and sensory feedback mechanisms.

Science.gov (United States)

Sağlam, M; Lehnen, N

2014-01-01

During gaze shifts, humans can use visual, vestibular, and proprioceptive feedback, as well as feedforward mechanisms, for stabilization against active and passive head movements. The contributions of feedforward and sensory feedback control, and the role of the cerebellum, are still under debate. To quantify these contributions, we increased the head moment of inertia in three groups (ten healthy, five chronic vestibular-loss and nine cerebellar-ataxia patients) while they performed large gaze shifts to flashed targets in darkness. This induces undesired head oscillations. Consequently, both active (desired) and passive (undesired) head movements had to be compensated for to stabilize gaze. All groups compensated for active and passive head movements, vestibular-loss patients less than the other groups (P feedforward mechanisms substantially contribute to gaze stabilization. Proprioception alone is not sufficient (gain 0.2). Stabilization against active and passive head movements was not impaired in our cerebellar ataxia patients.

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Enhancing feedforward controller tuning via instrumental variables: with application to nanopositioning

NARCIS (Netherlands)

Boeren, F.A.J.; Bruijnen, D.J.H.; Oomen, T.A.E.

2017-01-01

Feedforward control enables high performance of a motion system. Recently, algorithms have been proposed that eliminate bias errors in tuning the parameters of a feedforward controller. The aim of this paper is to develop a new algorithm that combines unbiased parameter estimates with optimal

In vivo temporal property of GABAergic neural transmission in collateral feed-forward inhibition system of hippocampal-prefrontal pathway.

Science.gov (United States)

Takita, Masatoshi; Kuramochi, Masahito; Izaki, Yoshinori; Ohtomi, Michiko

2007-05-30

Anatomical evidence suggests that rat CA1 hippocampal afferents collaterally innervate excitatory projecting pyramidal neurons and inhibitory interneurons, creating a disynaptic, feed-forward inhibition microcircuit in the medial prefrontal cortex (mPFC). We investigated the temporal relationship between the frequency of paired synaptic transmission and gamma-aminobutyric acid (GABA)ergic receptor-mediated modulation of the microcircuit in vivo under urethane anesthesia. Local perfusions of a GABAa antagonist (-)-bicuculline into the mPFC via microdialysis resulted in a statistically significant disinhibitory effect on intrinsic GABA action, increasing the first and second mPFC responses following hippocampal paired stimulation at interstimulus intervals of 100-200 ms, but not those at 25-50 ms. This (-)-bicuculline-induced disinhibition was compensated by the GABAa agonist muscimol, which itself did not attenuate the intrinsic oscillation of the local field potentials. The perfusion of a sub-minimal concentration of GABAb agonist (R)-baclofen slightly enhanced the synaptic transmission, regardless of the interstimulus interval. In addition to the tonic control by spontaneous fast-spiking GABAergic neurons, it is clear the sequential transmission of the hippocampal-mPFC pathway can phasically drive the collateral feed-forward inhibition system through activation of a GABAa receptor, bringing an active signal filter to the various types of impulse trains that enter the mPFC from the hippocampus in vivo.

Frequency-weighted feedforward control for dynamic compensation in ionic polymer–metal composite actuators

International Nuclear Information System (INIS)

Shan, Yingfeng; Leang, Kam K

2009-01-01

Ionic polymer–metal composites (IPMCs) are innovative materials that offer combined sensing and actuating ability in lightweight and flexible package. IPMCs have been exploited in robotics and a wide variety of biomedical devices, for example, as sensors for teleoperation, as actuators for positioning in active endoscopy, as fins for propelling aquatic robots, and as an injector for drug delivery. In the actuation mode, one of the main challenges is precise position control. In particular, IPMC actuators exhibit relaxation behavior and nonlinearities; and at relatively high operating frequencies dynamic effects limit accuracy and positioning bandwidth. A frequency-weighted feedforward controller is designed to account for the IPMC's structural dynamics to enable fast positioning. The control method is applied to a custom-made Nafion-based IPMC actuator. The controller takes into account the magnitude of the control input to avoid generating excessively large voltages which can damage the IPMC actuator. To account for unmodeled effects not captured by the dynamics model, a feedback controller is integrated with the feedforward controller. Experimental results show a significant improvement in the tracking performance when feedforward control is used. For instance, the feedforward controller shows over 75% reduction in the tracking error compared to the case without feedforward compensation. Finally, the integrated feedforward and feedback control system reduces the tracking error to less than 10% for tracking an 18-Hz triangle-like trajectory. Some of the advantages of feedforward control as well as its limitations are also discussed

Experimental evaluation of feedforward control for the trajectory tracking of power in nuclear reactors

International Nuclear Information System (INIS)

Lau, S.H.; Bernard, J.A.; Lanning, D.D.

1991-01-01

This paper reports on an experimental comparison of feedforward control techniques for the trajectory-tracking of neutronic power was performed on the 5-MWt MIT Research Reactor. Included in the comparison were pure feedforward control in which the actuator signal is found solely by processing a demanded output through a system model, hybrid feedforward/feedback control in which the actuator signal is obtained by summing feedforward and feedback components, and period-generated control in which feedback is used to update the demand trajectory prior to its being processed through the system model for calculation of the actuator signal. This latter approach was found to be the most effective. In addition to the experimental results, discussions are given of both the rationale for model-based, feedforward control and the designs of the various controllers

Design of alluvial Egyptian irrigation canals using artificial neural networks method

Directory of Open Access Journals (Sweden)

Hassan Ibrahim Mohamed

2013-06-01

Full Text Available In the present study, artificial neural networks method (ANNs is used to estimate the main parameters which used in design of stable alluvial channels. The capability of ANN models to predict the stable alluvial channels dimensions is investigated, where the flow rate and sediment mean grain size were considered as input variables and wetted perimeter, hydraulic radius, and water surface slope were considered as output variables. The used ANN models are based on a back propagation algorithm to train a multi-layer feed-forward network (Levenberg Marquardt algorithm. The proposed models were verified using 311 data sets of field data collected from 61 manmade canals and drains. Several statistical measures and graphical representation are used to check the accuracy of the models in comparison with previous empirical equations. The results of the developed ANN model proved that this technique is reliable in such field compared with previously developed methods.

Simulation of Natural Convection in the Oxide Layer of Three-Layer Corium Pool in an IVR

Energy Technology Data Exchange (ETDEWEB)

Kim, Su-Hyeon; Park, Hae-Kyun; Chung, Bum-Jin [Kyung Hee University, Yongin (Korea, Republic of)

2016-10-15

This paper describes the three-layer phenomena and preliminary plan to simulate the oxide layer experimentally. We will perform the mass transfer experiments using a copper sulfate-sulfuric acid (CuSO{sub 4}-H{sub 2}SO{sub 4}) electroplating system based on the heat and mass transfer analogy concept. By performing the mass transfer experiments, we can achieve the high buoyancy condition with small facilities. The test facility is semicircular whose bottom is chopped, simulating the oxide pool above the heavy metal layer in a three-layer configuration. We will measure the heat flux at the top plate, side wall and bottom plate, and compare these results with those for a two-layer pool. In a three-layer configuration, the upper light metal layer becomes thinner, increasing the focusing effect. Thus, it is important to evaluate the heat flux from the oxide pool to the upper metallic layer. However, there is few heat transfer studies for a three-layer configuration. This paper is to discuss and to make a plan for the heat transfer experiments of oxide pool in a three- layer system. We will perform the mass transfer experiments based on the heat and mass transfer analogy concept. The test results will be analyzed phenomenologically and compared with two-layer results.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

Science.gov (United States)

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Design Of Combined Stochastic Feedforward/Feedback Control

Science.gov (United States)

Halyo, Nesim

1989-01-01

Methodology accommodates variety of control structures and design techniques. In methodology for combined stochastic feedforward/feedback control, main objectives of feedforward and feedback control laws seen clearly. Inclusion of error-integral feedback, dynamic compensation, rate-command control structure, and like integral element of methodology. Another advantage of methodology flexibility to develop variety of techniques for design of feedback control with arbitrary structures to obtain feedback controller: includes stochastic output feedback, multiconfiguration control, decentralized control, or frequency and classical control methods. Control modes of system include capture and tracking of localizer and glideslope, crab, decrab, and flare. By use of recommended incremental implementation, control laws simulated on digital computer and connected with nonlinear digital simulation of aircraft and its systems.

Blastema cells derived from New Zealand white rabbit's pinna carry stemness properties as shown by differentiation into insulin producing, neural, and osteogenic lineages representing three embryonic germ layers.

Science.gov (United States)

Saeinasab, Morvarid; Matin, Maryam M; Rassouli, Fatemeh B; Bahrami, Ahmad Reza

2016-05-01

Stem cells (SCs) are known as undifferentiated cells with self-renewal and differentiation capacities. Regeneration is a phenomenon that occurs in a limited number of animals after injury, during which blastema tissue is formed. It has been hypothesized that upon injury, the dedifferentiation of surrounding tissues leads into the appearance of cells with SC characteristics. In present study, stem-like cells (SLCs) were obtained from regenerating tissue of New Zealand white rabbit's pinna and their stemness properties were examined by their capacity to differentiate toward insulin producing cells (IPCs), as well as neural and osteogenic lineages. Differentiation was induced by culture of SLCs in defined medium, and cell fates were monitored by specific staining, RT-PCR and flow cytometry assays. Our results revealed that dithizone positive cells, which represent IPCs, and islet-like structures appeared 1 week after induction of SLCs, and this observation was confirmed by the elevated expression of Ins, Pax6 and Glut4 at mRNA level. Furthermore, SLCs were able to express neural markers as early as 1 week after retinoic acid treatment. Finally, SLCs were able to differentiate into osteogenic lineage, as confirmed by Alizarin Red S staining and RT-PCR studies. In conclusion, SLCs, which could successfully differentiate into cells derived from all three germ layers, can be considered as a valuable model to study developmental biology and regenerative medicine.

The application of artificial neural network in radon disaster model of uranium mining

International Nuclear Information System (INIS)

Zhu Yufeng; Zhu Guogen; Zhou Shijian

2012-01-01

The structural features, data analysis and learning process of feed-forward neural network (BP ANN) were analyzed at first. Rodon sample from Fuzhou Jinan Uranium Industry Limited Company were used to training the network and make the forecast then, and a forecasting model was established for the radon disaster in uranium mines. The method and effectiveness of BP neural network in predicting radon disaster was discussed. The test of training samples showed that the BP network had gotten fairly satisfied result in predicting mine radon disaster. (authors)

Psychological Processing in Chronic Pain: A Neural Systems Approach

OpenAIRE

Simons, Laura; Elman, Igor; Borsook, David

2013-01-01

Our understanding of chronic pain involves complex brain circuits that include sensory, emotional, cognitive and interoceptive processing. The feed-forward interactions between physical (e.g., trauma) and emotional pain and the consequences of altered psychological status on the expression of pain have made the evaluation and treatment of chronic pain a challenge in the clinic. By understanding the neural circuits involved in psychological processes, a mechanistic approach to the implementati...

Using an Artificial Neural Network Approach for Supplier Evaluation Process and a Sectoral Application

Directory of Open Access Journals (Sweden)

A. Yeşim Yayla

2011-02-01

Full Text Available In this study, a-three layered feed-forward backpropagation Artificial Neural Network (ANN model is developed for the supplier firms in ceramic sector on the bases of user effectiveness for using concurrent engineering method. The developed model is also questioned for its usability in the supplier evaluation process. The network's independent variables of the developed model are considered as input variables of the network and dependent variables are used as output variables. The values of these variables are determined with factor analysis. For obtaining the date set to be used in the analysis, a questionnaire form with 34 questions explaining the network's input and output variables are prepared and sent out to 52 firms active in related sector. For obtaining more accurate results from the network, the questions having factor load below 0,6 are eliminated from the analysis. With the elimination of the questions from the analysis, the answers given for 22 questions explaining 8 input variables are used for the evaluation the network's inputs, the answers given for 3 questions explaining output variables are used for the evaluation the network's outputs. The data set of the network's are divided into four equal groups with k-fold method in order to get four different alternative network structures. As a conclusion, the forecasted firm scores giving the minimum error from the network test simulation and real firm scores are found to be very close to each other, thus, it is concluded that the developed artificial neural network model can be used effectively in the supplier evaluation process.

Usage of neural network to predict aluminium oxide layer thickness.

Science.gov (United States)

Michal, Peter; Vagaská, Alena; Gombár, Miroslav; Kmec, Ján; Spišák, Emil; Kučerka, Daniel

2015-01-01

This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage) and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A · dm(-2) and 3 A · dm(-2) for creating aluminium oxide layer.

Usage of Neural Network to Predict Aluminium Oxide Layer Thickness

Directory of Open Access Journals (Sweden)

Peter Michal

2015-01-01

Full Text Available This paper shows an influence of chemical composition of used electrolyte, such as amount of sulphuric acid in electrolyte, amount of aluminium cations in electrolyte and amount of oxalic acid in electrolyte, and operating parameters of process of anodic oxidation of aluminium such as the temperature of electrolyte, anodizing time, and voltage applied during anodizing process. The paper shows the influence of those parameters on the resulting thickness of aluminium oxide layer. The impact of these variables is shown by using central composite design of experiment for six factors (amount of sulphuric acid, amount of oxalic acid, amount of aluminium cations, electrolyte temperature, anodizing time, and applied voltage and by usage of the cubic neural unit with Levenberg-Marquardt algorithm during the results evaluation. The paper also deals with current densities of 1 A·dm−2 and 3 A·dm−2 for creating aluminium oxide layer.

Multiharmonic rf feedforward system for compensation of beam loading and periodic transient effects in magnetic-alloy cavities of a proton synchrotron

Directory of Open Access Journals (Sweden)

Fumihiko Tamura

2013-05-01

Full Text Available Beam loading compensation is a key for acceleration of a high intensity proton beam in the main ring (MR of the Japan Proton Accelerator Research Complex (J-PARC. Magnetic alloy loaded rf cavities with a Q value of 22 are used to achieve high accelerating voltages without a tuning bias loop. The cavity is driven by a single harmonic (h=9 rf signal while the cavity frequency response also covers the neighbor harmonics (h=8,10. Therefore the wake voltage induced by the high intensity beam consists of the three harmonics, h=8,9,10. The beam loading of neighbor harmonics is the source of periodic transient effects and a possible source of coupled bunch instabilities. In the article, we analyze the wake voltage induced by the high intensity beam. We employ the rf feedforward method to compensate the beam loading of these three harmonics (h=8,9,10. The full-digital multiharmonic feedforward system was developed for the MR. We describe the system architecture and the commissioning methodology of the feedforward patterns. The commissioning of the feedforward system has been performed by using high intensity beams with 1.0×10^{14} proteins per pulse. The impedance seen by the beam is successfully reduced and the longitudinal oscillations due to the beam loading are reduced. By the beam loading compensation, stable high power beam operation is achieved. We also report the reduction of the momentum loss during the debunching process for the slow extraction by the feedforward.

Artificial neural networks in the nuclear engineering (Part 2)

International Nuclear Information System (INIS)

Baptista Filho, Benedito Dias

2002-01-01

The field of Artificial Neural Networks (ANN), one of the branches of Artificial Intelligence has been waking up a lot of interest in the Nuclear Engineering (NE). ANN can be used to solve problems of difficult modeling, when the data are fail or incomplete and in high complexity problems of control. The first part of this work began a discussion with feed-forward neural networks in back-propagation. In this part of the work, the Multi-synaptic neural networks is applied to control problems. Also, the self-organized maps is presented in a typical pattern classification problem: transients classification. The main purpose of the work is to show that ANN can be successfully used in NE if a carefully choice of its type is done: the application sets this choice. (author)

Neural networks for predicting breeding values and genetic gains

Directory of Open Access Journals (Sweden)

Gabi Nunes Silva

2014-12-01

Full Text Available Analysis using Artificial Neural Networks has been described as an approach in the decision-making process that, although incipient, has been reported as presenting high potential for use in animal and plant breeding. In this study, we introduce the procedure of using the expanded data set for training the network. Wealso proposed using statistical parameters to estimate the breeding value of genotypes in simulated scenarios, in addition to the mean phenotypic value in a feed-forward back propagation multilayer perceptron network. After evaluating artificial neural network configurations, our results showed its superiority to estimates based on linear models, as well as its applicability in the genetic value prediction process. The results further indicated the good generalization performance of the neural network model in several additional validation experiments.

Degraded expression of learned feedforward control in movements released by startle.

Science.gov (United States)

Wright, Zachary A; Carlsen, Anthony N; MacKinnon, Colum D; Patton, James L

2015-08-01

Recent work has shown that preplanned motor programs can be rapidly released via fast conducting pathways using a startling acoustic stimulus. Our question was whether the startle-elicited response might also release a recently learned internal model, which draws on experience to predict and compensate for expected perturbations in a feedforward manner. Our initial investigation using adaptation to robotically produced forces showed some evidence of this, but the results were potentially confounded by co-contraction caused by startle. In this study, we eliminated this confound by asking subjects to make reaching movements in the presence of a visual distortion. Results show that a startle stimulus (1) decreased performance of the recently learned task and (2) reduced after-effect magnitude. Since the recall of learned control was reduced, but not eliminated during startle trials, we suggest that multiple neural centers (cortical and subcortical) are involved in such learning and adaptation. These findings have implications for motor training in areas such as piloting, teleoperation, sports, and rehabilitation.

Combined Effects of Feedforward Inhibition and Excitation in Thalamocortical Circuit on the Transitions of Epileptic Seizures

Science.gov (United States)

Fan, Denggui; Duan, Lixia; Wang, Qian; Luan, Guoming

2017-01-01

The mechanisms underlying electrophysiologically observed two-way transitions between absence and tonic-clonic epileptic seizures in cerebral cortex remain unknown. The interplay within thalamocortical network is believed to give rise to these epileptic multiple modes of activity and transitions between them. In particular, it is thought that in some areas of cortex there exists feedforward inhibition from specific relay nucleus of thalamus (TC) to inhibitory neuronal population (IN) which has even more stronger functions on cortical activities than the known feedforward excitation from TC to excitatory neuronal population (EX). Inspired by this, we proposed a modified computational model by introducing feedforward inhibitory connectivity within thalamocortical circuit, to systematically investigate the combined effects of feedforward inhibition and excitation on transitions of epileptic seizures. We first found that the feedforward excitation can induce the transition from tonic oscillation to spike and wave discharges (SWD) in cortex, i.e., the epileptic tonic-absence seizures, with the fixed weak feedforward inhibition. Thereinto, the phase of absence seizures corresponding to strong feedforward excitation can be further transformed into the clonic oscillations with the increasing of feedforward inhibition, representing the epileptic absence-clonic seizures. We also observed the other fascinating dynamical states, such as periodic 2/3/4-spike and wave discharges, reversed SWD and clonic oscillations, as well as saturated firings. More importantly, we can identify the stable parameter regions representing the tonic-clonic oscillations and SWD discharges of epileptic seizures on the 2-D plane composed of feedforward inhibition and excitation, where the physiologically plausible transition pathways between tonic-clonic and absence seizures can be figured out. These results indicate the functional role of feedforward pathways in controlling epileptic seizures and

Combined Effects of Feedforward Inhibition and Excitation in Thalamocortical Circuit on the Transitions of Epileptic Seizures

Directory of Open Access Journals (Sweden)

Denggui Fan

2017-07-01

Full Text Available The mechanisms underlying electrophysiologically observed two-way transitions between absence and tonic-clonic epileptic seizures in cerebral cortex remain unknown. The interplay within thalamocortical network is believed to give rise to these epileptic multiple modes of activity and transitions between them. In particular, it is thought that in some areas of cortex there exists feedforward inhibition from specific relay nucleus of thalamus (TC to inhibitory neuronal population (IN which has even more stronger functions on cortical activities than the known feedforward excitation from TC to excitatory neuronal population (EX. Inspired by this, we proposed a modified computational model by introducing feedforward inhibitory connectivity within thalamocortical circuit, to systematically investigate the combined effects of feedforward inhibition and excitation on transitions of epileptic seizures. We first found that the feedforward excitation can induce the transition from tonic oscillation to spike and wave discharges (SWD in cortex, i.e., the epileptic tonic-absence seizures, with the fixed weak feedforward inhibition. Thereinto, the phase of absence seizures corresponding to strong feedforward excitation can be further transformed into the clonic oscillations with the increasing of feedforward inhibition, representing the epileptic absence-clonic seizures. We also observed the other fascinating dynamical states, such as periodic 2/3/4-spike and wave discharges, reversed SWD and clonic oscillations, as well as saturated firings. More importantly, we can identify the stable parameter regions representing the tonic-clonic oscillations and SWD discharges of epileptic seizures on the 2-D plane composed of feedforward inhibition and excitation, where the physiologically plausible transition pathways between tonic-clonic and absence seizures can be figured out. These results indicate the functional role of feedforward pathways in controlling epileptic

Extracting functionally feedforward networks from a population of spiking neurons.

Science.gov (United States)

Vincent, Kathleen; Tauskela, Joseph S; Thivierge, Jean-Philippe

2012-01-01

Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV

Theta phase precession and phase selectivity: a cognitive device description of neural coding

Science.gov (United States)

Zalay, Osbert C.; Bardakjian, Berj L.

2009-06-01

Information in neural systems is carried by way of phase and rate codes. Neuronal signals are processed through transformative biophysical mechanisms at the cellular and network levels. Neural coding transformations can be represented mathematically in a device called the cognitive rhythm generator (CRG). Incoming signals to the CRG are parsed through a bank of neuronal modes that orchestrate proportional, integrative and derivative transformations associated with neural coding. Mode outputs are then mixed through static nonlinearities to encode (spatio) temporal phase relationships. The static nonlinear outputs feed and modulate a ring device (limit cycle) encoding output dynamics. Small coupled CRG networks were created to investigate coding functionality associated with neuronal phase preference and theta precession in the hippocampus. Phase selectivity was found to be dependent on mode shape and polarity, while phase precession was a product of modal mixing (i.e. changes in the relative contribution or amplitude of mode outputs resulted in shifting phase preference). Nonlinear system identification was implemented to help validate the model and explain response characteristics associated with modal mixing; in particular, principal dynamic modes experimentally derived from a hippocampal neuron were inserted into a CRG and the neuron's dynamic response was successfully cloned. From our results, small CRG networks possessing disynaptic feedforward inhibition in combination with feedforward excitation exhibited frequency-dependent inhibitory-to-excitatory and excitatory-to-inhibitory transitions that were similar to transitions seen in a single CRG with quadratic modal mixing. This suggests nonlinear modal mixing to be a coding manifestation of the effect of network connectivity in shaping system dynamic behavior. We hypothesize that circuits containing disynaptic feedforward inhibition in the nervous system may be candidates for interpreting upstream rate codes to

Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Aminmohammad Saberian

2014-01-01

Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

Reference-tracking feedforward control design for linear dynamical systems through signal decomposition

NARCIS (Netherlands)

Kasemsinsup, Y.; Romagnoli, R.; Heertjes, M.F.; Weiland, S.; Butler, H.

2017-01-01

In this work, we study a novel approach towards the reference-tracking feedforward control design for linear dynamical systems. By utilizing the superposition property and exploiting signal decomposition together with a quadratic optimization process, we obtain a feedforward design procedure for

Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators

NARCIS (Netherlands)

Beijen, M.A.; Heertjes, M.F.; van Dijk, J.W.; Hakvoort, W.B.J.

2018-01-01

This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward controller is

Self-tuning disturbance feedforward control with drift prevention for air mount systems

NARCIS (Netherlands)

Beijen, M.A.; Heertjes, M.F.; Butler, H.

2015-01-01

A MIMO disturbance feedforward control strategy is presented to isolate an industrial active vibration isolation system with air mounts from broadband floor vibrations. The feedforward controller compensates for the static damping and stiffness of the air mount suspension, leading to significant

Expert music performance: cognitive, neural, and developmental bases.

Science.gov (United States)

Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B

2015-01-01

In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work. © 2015 Elsevier B.V. All rights reserved.

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Science.gov (United States)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Cortical layers, rhythms and BOLD signals.

Science.gov (United States)

Scheeringa, René; Fries, Pascal

2017-11-03

This review investigates how laminar fMRI can complement insights into brain function derived from the study of rhythmic neuronal synchronization. Neuronal synchronization in various frequency bands plays an important role in neuronal communication between brain areas, and it does so on the backbone of layer-specific interareal anatomical projections. Feedforward projections originate predominantly in supragranular cortical layers and terminate in layer 4, and this pattern is reflected in inter-laminar and interareal directed gamma-band influences. Thus, gamma-band synchronization likely subserves feedforward signaling. By contrast, anatomical feedback projections originate predominantly in infragranular layers and terminate outside layer 4, and this pattern is reflected in inter-laminar and interareal directed alpha- and/or beta-band influences. Thus, alpha-beta band synchronization likely subserves feedback signaling. Furthermore, these rhythms explain part of the BOLD signal, with independent contributions of alpha-beta and gamma. These findings suggest that laminar fMRI can provide us with a potentially useful method to test some of the predictions derived from the study of neuronal synchronization. We review central findings regarding the role of layer-specific neuronal synchronization for brain function, and regarding the link between neuronal synchronization and the BOLD signal. We discuss the role that laminar fMRI could play by comparing it to invasive and non-invasive electrophysiological recordings. Compared to direct electrophysiological recordings, this method provides a metric of neuronal activity that is slow and indirect, but that is uniquely non-invasive and layer-specific with potentially whole brain coverage. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Feedforward control of a closed-loop piezoelectric translation stage for atomic force microscope.

Science.gov (United States)

Li, Yang; Bechhoefer, John

2007-01-01

Simple feedforward ideas are shown to lead to a nearly tenfold increase in the effective bandwidth of a closed-loop piezoelectric positioning stage used in scanning probe microscopy. If the desired control signal is known in advance, the feedforward filter can be acausal: the information about the future can be used to make the output of the stage have almost no phase lag with respect to the input. This keeps in register the images assembled from right and left scans. We discuss the design constraints imposed by the need for the feedforward filter to work robustly under a variety of circumstances. Because the feedforward needs only to modify the input signal, it can be added to any piezoelectric stage, whether closed or open loop.

High frequency three-phase PWM grid connected drive using silicon-carbide switches

DEFF Research Database (Denmark)

Kouchaki, Alireza; Pedersen, Jacob Lykke; Nymand, Morten

2016-01-01

This paper presents controller design procedure for a fully silicon-carbide (SiC) based three-phase grid-connected PWM drive. The influence of the feedforward compensation for the presented setup is studied and the transfer function of the system with feedforward is derived and compared with the ......This paper presents controller design procedure for a fully silicon-carbide (SiC) based three-phase grid-connected PWM drive. The influence of the feedforward compensation for the presented setup is studied and the transfer function of the system with feedforward is derived and compared...

Microfluidic engineered high cell density three-dimensional neural cultures

Science.gov (United States)

Cullen, D. Kacy; Vukasinovic, Jelena; Glezer, Ari; La Placa, Michelle C.

2007-06-01

Three-dimensional (3D) neural cultures with cells distributed throughout a thick, bioactive protein scaffold may better represent neurobiological phenomena than planar correlates lacking matrix support. Neural cells in vivo interact within a complex, multicellular environment with tightly coupled 3D cell-cell/cell-matrix interactions; however, thick 3D neural cultures at cell densities approaching that of brain rapidly decay, presumably due to diffusion limited interstitial mass transport. To address this issue, we have developed a novel perfusion platform that utilizes forced intercellular convection to enhance mass transport. First, we demonstrated that in thick (>500 µm) 3D neural cultures supported by passive diffusion, cell densities =104 cells mm-3), continuous medium perfusion at 2.0-11.0 µL min-1 improved viability compared to non-perfused cultures (p death and matrix degradation. In perfused cultures, survival was dependent on proximity to the perfusion source at 2.00-6.25 µL min-1 (p 90% viability in both neuronal cultures and neuronal-astrocytic co-cultures. This work demonstrates the utility of forced interstitial convection in improving the survival of high cell density 3D engineered neural constructs and may aid in the development of novel tissue-engineered systems reconstituting 3D cell-cell/cell-matrix interactions.

Self-tuning MIMO disturbance feedforward control for active hard-mounted vibration isolators

NARCIS (Netherlands)

Beijen, M.A.; Heertjes, M.F.; Van Dijk, J.; Hakvoort, W. B.J.

2018-01-01

© 2017 Elsevier Ltd This paper proposes a multi-input multi-output (MIMO) disturbance feedforward controller to improve the rejection of floor vibrations in active vibration isolation systems for high-precision machinery. To minimize loss of performance due to model uncertainties, the feedforward

Neural network-based run-to-run controller using exposure and resist thickness adjustment

Science.gov (United States)

Geary, Shane; Barry, Ronan

2003-06-01

This paper describes the development of a run-to-run control algorithm using a feedforward neural network, trained using the backpropagation training method. The algorithm is used to predict the critical dimension of the next lot using previous lot information. It is compared to a common prediction algorithm - the exponentially weighted moving average (EWMA) and is shown to give superior prediction performance in simulations. The manufacturing implementation of the final neural network showed significantly improved process capability when compared to the case where no run-to-run control was utilised.

Proinflammatory Cytokine Infusion Attenuates LH's Feedforward on Testosterone Secretion: Modulation by Age

Science.gov (United States)

Yang, Rebecca; Roelfsema, Ferdinand; Takahashi, Paul

2016-01-01

Context: In the experimental animal, inflammatory signals quench LH's feedforward drive of testosterone (T) secretion and appear to impair GnRH-LH output. The degree to which such suppressive effects operate in the human is not known. Objective: To test the hypothesis that IL-2 impairs LH's feedforward drive on T and T's feedback inhibition of LH secretion in healthy men. Setting: Mayo Center for Translational Science Activities. Patients or Other Participants: A total of 35 healthy men, 17 young and 18 older. Interventions: Randomized prospective double-blind saline-controlled study of IL-2 infusion in 2 doses with concurrent 10-minute blood sampling for 24 hours. Main Outcome Measures: Deconvolution analysis of LH and T secretion. Results: After saline injection, older compared with young men exhibited reduced LH feedforward drive on T secretion (P feedback inhibition of LH secretion (P feedforward onto T secretion declined markedly especially in young subjects (P feedback on LH secretion especially in older volunteers. Conclusion: This investigation confirms combined feedforward and feedback deficits in older relative to young men given saline and demonstrates 1) joint mechanisms by which IL-2 enforces biochemical hypogonadism, viz, combined feedforward block and feedback amplification; and 2) unequal absolute inhibition of T and LH secretion by IL-2 in young and older men. These outcomes establish that the male gonadal axis is susceptible to dual-site suppression by a prototypic inflammatory mediator. Thus, we postulate that selected ILs might also enforce male hypogonadism in chronic systemic inflammation. PMID:26600270

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

Energy Technology Data Exchange (ETDEWEB)

Jang, Jin-wook [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: Jinwook@kaeri.re.kr; Seong, Seung-Hwan [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: shseong@kaeri.re.kr; Lee, Un-Chul [Department of Nuclear Engineering, Seoul National University, Shinlim-Dong, Gwanak-Gu, Seoul 151-742 (Korea, Republic of)

2007-09-15

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

International Nuclear Information System (INIS)

Jang, Jin-wook; Seong, Seung-Hwan; Lee, Un-Chul

2007-01-01

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band

A feedforward IMC structure for controlling the charging temperature of a TES system of a solar cooker

International Nuclear Information System (INIS)

Mawire, A.; McPherson, M.

2008-01-01

A feedforward internal model control (IMC) structure for controlling and maintaining the outlet charging temperature of a thermal energy storage (TES) system of a solar cooker is presented. The TES system consists of a packed pebble bed in thermal contact with a heat transfer oil contained in a storage tank. An electrical hot plate simulates the collector/concentrator which heats up the oil circulating in a hollow copper spiral coil thus charging the storage. A model for the collector/concentrator system is developed to enable simulation of the feedforward IMC structure. Using a Simulink block model, the simulation results reveal that a feedforward IMC structure performs better than a feedforward structure. The feedforward IMC structure is tested experimentally and the performance of the control structure is acceptable within a few degrees of the set temperatures. Experimental results are also compared with the simulation results. The simulated responses are found to relate closely to the experimental ones and any discrepancies between the two are discussed. Furthermore, the feedforward IMC structure is also compared experimentally with a combined feedforward and PID feedback structure. Results of the comparison indicate that the feedforward IMC structure performs better than the combined feedforward and PID feedback structure. The thermal profile of the storage during the charging experiment with the feedforward IMC structure is also presented and the results obtained from the storage profile indicate that the storage tank is thermally stratified

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex.

Science.gov (United States)

van Kerkoerle, Timo; Self, Matthew W; Dagnino, Bruno; Gariel-Mathis, Marie-Alice; Poort, Jasper; van der Togt, Chris; Roelfsema, Pieter R

2014-10-07

Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether low-frequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ- and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.

Spike-timing computation properties of a feed-forward neural network model

Directory of Open Access Journals (Sweden)

Drew Benjamin Sinha

2014-01-01

Full Text Available Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g. serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape transformations, we modeled feed-forward networks of 7-22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

Support surface related changes in feedforward and feedback control of standing posture.

Science.gov (United States)

Mohapatra, Sambit; Kukkar, Komal K; Aruin, Alexander S

2014-02-01

The aim of the study was to investigate the effect of different support surfaces on feedforward and feedback components of postural control. Nine healthy subjects were exposed to external perturbations applied to their shoulders while standing on a rigid platform, foam, and wobble board with eyes open or closed. Electrical activity of nine trunk and leg muscles and displacements of the center of pressure were recorded and analyzed during the time frames typical of feedforward and feedback postural adjustments. Feedforward control of posture was characterized by earlier activation of anterior muscles when the subjects stood on foam compared to a wobble board or a firm surface. In addition, the magnitude of feedforward muscle activity was the largest when the foam was used. During the feedback control, anterior muscles were activated prior to posterior muscles irrespective of the nature of surface. Moreover, the largest muscle activity was seen when the supporting surface was foam. Maximum CoP displacement occurred when subjects were standing on a rigid surface. Altering support surface affects both feedforward and feedback components of postural control. This information should be taken into consideration in planning rehabilitation interventions geared towards improvement of balance. Copyright © 2013 Elsevier Ltd. All rights reserved.

A stochastic optimal feedforward and feedback control methodology for superagility

Science.gov (United States)

Halyo, Nesim; Direskeneli, Haldun; Taylor, Deborah B.

1992-01-01

A new control design methodology is developed: Stochastic Optimal Feedforward and Feedback Technology (SOFFT). Traditional design techniques optimize a single cost function (which expresses the design objectives) to obtain both the feedforward and feedback control laws. This approach places conflicting demands on the control law such as fast tracking versus noise atttenuation/disturbance rejection. In the SOFFT approach, two cost functions are defined. The feedforward control law is designed to optimize one cost function, the feedback optimizes the other. By separating the design objectives and decoupling the feedforward and feedback design processes, both objectives can be achieved fully. A new measure of command tracking performance, Z-plots, is also developed. By analyzing these plots at off-nominal conditions, the sensitivity or robustness of the system in tracking commands can be predicted. Z-plots provide an important tool for designing robust control systems. The Variable-Gain SOFFT methodology was used to design a flight control system for the F/A-18 aircraft. It is shown that SOFFT can be used to expand the operating regime and provide greater performance (flying/handling qualities) throughout the extended flight regime. This work was performed under the NASA SBIR program. ICS plans to market the software developed as a new module in its commercial CACSD software package: ACET.

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

Science.gov (United States)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

Science.gov (United States)

Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

2016-11-01

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

Conjugate descent formulation of backpropagation error in ...

African Journals Online (AJOL)

The feedforward neural network architecture uses backpropagation learning to determine optimal weights between dierent interconnected layers. This learning procedure uses a gradient descent technique applied to a sum-of-squares error function for the given input-output pattern. It employs an iterative procedure to ...

A comprehensive inversion approach for feedforward compensation of piezoactuator system at high frequency

Science.gov (United States)

Tian, Lizhi; Xiong, Zhenhua; Wu, Jianhua; Ding, Han

2016-09-01

Motion control of the piezoactuator system over broadband frequencies is limited due to its inherent hysteresis and system dynamics. One of the suggested ways is to use feedforward controller to linearize the input-output relationship of the piezoactuator system. Although there have been many feedforward approaches, it is still a challenge to develop feedforward controller for the piezoactuator system at high frequency. Hence, this paper presents a comprehensive inversion approach in consideration of the coupling of hysteresis and dynamics. In this work, the influence of dynamics compensation on the input-output relationship of the piezoactuator system is investigated first. With system dynamics compensation, the input-output relationship of the piezoactuator system will be further represented as rate-dependent nonlinearity due to the inevitable dynamics compensation error, especially at high frequency. Base on this result, the feedforward controller composed by a cascade of linear dynamics inversion and rate-dependent nonlinearity inversion is developed. Then, the system identification of the comprehensive inversion approach is proposed. Finally, experimental results show that the proposed approach can improve the performance on tracking of both periodic and non-periodic trajectories at medium and high frequency compared with the conventional feedforward approaches.

Modeling of the radiation belt megnetosphere in decisional timeframes

Science.gov (United States)

Koller, Josef; Reeves, Geoffrey D; Friedel, Reiner H.W.

2013-04-23

Systems and methods for calculating L* in the magnetosphere with essentially the same accuracy as with a physics based model at many times the speed by developing a surrogate trained to be a surrogate for the physics-based model. The trained model can then beneficially process input data falling within the training range of the surrogate model. The surrogate model can be a feedforward neural network and the physics-based model can be the TSK03 model. Operatively, the surrogate model can use parameters on which the physics-based model was based, and/or spatial data for the location where L* is to be calculated. Surrogate models should be provided for each of a plurality of pitch angles. Accordingly, a surrogate model having a closed drift shell can be used from the plurality of models. The feedforward neural network can have a plurality of input-layer units, there being at least one input-layer unit for each physics-based model parameter, a plurality of hidden layer units and at least one output unit for the value of L*.

Inverse kinematics problem in robotics using neural networks

Science.gov (United States)

Choi, Benjamin B.; Lawrence, Charles

1992-01-01

In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.

Application of artificial neural network in radiographic diagnosis

International Nuclear Information System (INIS)

Piraino, D.; Amartur, S.; Richmond, B.; Schils, J.; Belhobek, G.

1990-01-01

This paper reports on an artificial neural network trained to rate the likelihood of different bone neoplasms when given a standard description of a radiograph. A three-layer back propagation algorithm was trained with descriptions of examples of bone neoplasms obtained from standard radiographic textbooks. Fifteen bone neoplasms obtained from clinical material were used as unknowns to test the trained artificial neural network. The artificial neural network correctly rated the pathologic diagnosis as the most likely diagnosis in 10 of the 15 unknown cases

Glucocorticoid and cytokine crosstalk: Feedback, feedforward, and co-regulatory interactions determine repression or resistance.

Science.gov (United States)

Newton, Robert; Shah, Suharsh; Altonsy, Mohammed O; Gerber, Antony N

2017-04-28

Inflammatory signals induce feedback and feedforward systems that provide temporal control. Although glucocorticoids can repress inflammatory gene expression, glucocorticoid receptor recruitment increases expression of negative feedback and feedforward regulators, including the phosphatase, DUSP1, the ubiquitin-modifying enzyme, TNFAIP3, or the mRNA-destabilizing protein, ZFP36. Moreover, glucocorticoid receptor cooperativity with factors, including nuclear factor-κB (NF-κB), may enhance regulator expression to promote repression. Conversely, MAPKs, which are inhibited by glucocorticoids, provide feedforward control to limit expression of the transcription factor IRF1, and the chemokine, CXCL10. We propose that modulation of feedback and feedforward control can determine repression or resistance of inflammatory gene expression toglucocorticoid. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Three Dimensional Double Layers in Magnetized Plasmas

DEFF Research Database (Denmark)

Jovanovic, D.; Lynov, Jens-Peter; Michelsen, Poul

1982-01-01

Experimental results are presented which demonstrate the formation of fully three dimensional double layers in a magnetized plasma. The measurements are performed in a magnetized stationary plasma column with radius 1.5 cm. Double layers are produced by introducing an electron beam with radius 0.......4 cm along the magnetic field from one end of the column. The voltage drop across the double layer is found to be determined by the energy of the incoming electron beam. In general we find that the width of the double layer along the external magnetic field is determined by plasma density and beam...

High-speed linear optics quantum computing using active feed-forward.

Science.gov (United States)

Prevedel, Robert; Walther, Philip; Tiefenbacher, Felix; Böhi, Pascal; Kaltenbaek, Rainer; Jennewein, Thomas; Zeilinger, Anton

2007-01-04

As information carriers in quantum computing, photonic qubits have the advantage of undergoing negligible decoherence. However, the absence of any significant photon-photon interaction is problematic for the realization of non-trivial two-qubit gates. One solution is to introduce an effective nonlinearity by measurements resulting in probabilistic gate operations. In one-way quantum computation, the random quantum measurement error can be overcome by applying a feed-forward technique, such that the future measurement basis depends on earlier measurement results. This technique is crucial for achieving deterministic quantum computation once a cluster state (the highly entangled multiparticle state on which one-way quantum computation is based) is prepared. Here we realize a concatenated scheme of measurement and active feed-forward in a one-way quantum computing experiment. We demonstrate that, for a perfect cluster state and no photon loss, our quantum computation scheme would operate with good fidelity and that our feed-forward components function with very high speed and low error for detected photons. With present technology, the individual computational step (in our case the individual feed-forward cycle) can be operated in less than 150 ns using electro-optical modulators. This is an important result for the future development of one-way quantum computers, whose large-scale implementation will depend on advances in the production and detection of the required highly entangled cluster states.

Feedforward/feedback control synthesis for performance and robustness

Science.gov (United States)

Wie, Bong; Liu, Qiang

1990-01-01

Both feedforward and feedback control approaches for uncertain dynamical systems are investigated. The control design objective is to achieve a fast settling time (high performance) and robustness (insensitivity) to plant modeling uncertainty. Preshapong of an ideal, time-optimal control input using a 'tapped-delay' filter is shown to provide a rapid maneuver with robust performance. A robust, non-minimum-phase feedback controller is synthesized with particular emphasis on its proper implementation for a non-zero set-point control problem. The proposed feedforward/feedback control approach is robust for a certain class of uncertain dynamical systems, since the control input command computed for a given desired output does not depend on the plant parameters.

Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

International Nuclear Information System (INIS)

Gering, Stefan; Adamy, Jürgen

2014-01-01

Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis

Feedforward Tracking Control of Flat Recurrent Fuzzy Systems

Science.gov (United States)

Gering, Stefan; Adamy, Jürgen

2014-12-01

Flatness based feedforward control has proven to be a feasible solution for the problem of tracking control, which may be applied to a broad class of nonlinear systems. If a flat output of the system is known, the control is often based on a feedforward controller generating a nominal input in combination with a linear controller stabilizing the linearized error dynamics around the trajectory. We show in this paper that the very same idea may be incorporated for tracking control of MIMO recurrent fuzzy systems. Their dynamics is given by means of linguistic differential equations but may be converted into a hybrid system representation, which then serves as the basis for controller synthesis.

Distinct roles of the cortical layers of area v1 in figure-ground segregation

NARCIS (Netherlands)

Self, Matthew W.; van Kerkoerle, Timo; Supèr, Hans; Roelfsema, Pieter R.

2013-01-01

What roles do the different cortical layers play in visual processing? We recorded simultaneously from all layers of the primary visual cortex while monkeys performed a figure-ground segregation task. This task can be divided into different subprocesses that are thought to engage feedforward,

Meta-modeling of the pesticide fate model MACRO for groundwater exposure assessments using artificial neural networks

Science.gov (United States)

Stenemo, Fredrik; Lindahl, Anna M. L.; Gärdenäs, Annemieke; Jarvis, Nicholas

2007-08-01

Several simple index methods that use easily accessible data have been developed and included in decision-support systems to estimate pesticide leaching across larger areas. However, these methods often lack important process descriptions (e.g. macropore flow), which brings into question their reliability. Descriptions of macropore flow have been included in simulation models, but these are too complex and demanding for spatial applications. To resolve this dilemma, a neural network simulation meta-model of the dual-permeability macropore flow model MACRO was created for pesticide groundwater exposure assessment. The model was parameterized using pedotransfer functions that require as input the clay and sand content of the topsoil and subsoil, and the topsoil organic carbon content. The meta-model also requires the topsoil pesticide half-life and the soil organic carbon sorption coefficient as input. A fully connected feed-forward multilayer perceptron classification network with two hidden layers, linked to fully connected feed-forward multilayer perceptron neural networks with one hidden layer, trained on sub-sets of the target variable, was shown to be a suitable meta-model for the intended purpose. A Fourier amplitude sensitivity test showed that the model output (the 80th percentile average yearly pesticide concentration at 1 m depth for a 20 year simulation period) was sensitive to all input parameters. The two input parameters related to pesticide characteristics (i.e. soil organic carbon sorption coefficient and topsoil pesticide half-life) were the most influential, but texture in the topsoil was also quite important since it was assumed to control the mass exchange coefficient that regulates the strength of macropore flow. This is in contrast to models based on the advection-dispersion equation where soil texture is relatively unimportant. The use of the meta-model is exemplified with a case-study where the spatial variability of pesticide leaching is

Power level control of the TRIGA Mark-II research reactor using the multifeedback layer neural network and the particle swarm optimization

International Nuclear Information System (INIS)

Coban, Ramazan

2014-01-01

Highlights: • A multifeedback-layer neural network controller is presented for a research reactor. • Off-line learning of the MFLNN is accomplished by the PSO algorithm. • The results revealed that the MFLNN–PSO controller has a remarkable performance. - Abstract: In this paper, an artificial neural network controller is presented using the Multifeedback-Layer Neural Network (MFLNN), which is a recently proposed recurrent neural network, for neutronic power level control of a nuclear research reactor. Off-line learning of the MFLNN is accomplished by the Particle Swarm Optimization (PSO) algorithm. The MFLNN-PSO controller design is based on a nonlinear model of the TRIGA Mark-II research reactor. The learning and the test processes are implemented by means of a computer program at different power levels. The simulation results obtained reveal that the MFLNN-PSO controller has a remarkable performance on the neutronic power level control of the reactor for tracking the step reference power trajectories

Using function approximation to determine neural network accuracy

International Nuclear Information System (INIS)

Wichman, R.F.; Alexander, J.

2013-01-01

Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)

Neuro-Based Artificial Intelligence Model for Loan Decisions

OpenAIRE

Shorouq F. Eletter; Saad G. Yaseen; Ghaleb A. Elrefae

2010-01-01

Problem statement: Despite the increase in consumer loans defaults and competition in the banking market, most of the Jordanian commercial banks are reluctant to use artificial intelligence software systems for supporting loan decisions. Approach: This study developed a proposed model that identifies artificial neural network as an enabling tool for evaluating credit applications to support loan decisions in the Jordanian Commercial banks. A multi-layer feed-forward neural network with backpr...

Early tube leak detection system for steam boiler at KEV power plant

Directory of Open Access Journals (Sweden)

Ismail Firas B.

2016-01-01

Full Text Available Tube leakage in boilers has been a major contribution to trips which eventually leads to power plant shut downs. Training of network and developing artificial neural network (ANN models are essential in fault detection in critically large systems. This research focusses on the ANN modelling through training and validation of real data acquired from a sub-critical boiler unit. The artificial neural network (ANN was used to develop a compatible model and to evaluate the working properties and behaviour of boiler. The training and validation of real data has been applied using the feed-forward with back-propagation (BP. The right combination of number of neurons, number of hidden layers, training algorithms and training functions was run to achieve the best ANN model with lowest error. The ANN was trained and validated using real site data acquired from a coal fired power plant in Malaysia. The results showed that the Neural Network (NN with one hidden layers performed better than two hidden layer using feed-forward back-propagation network. The outcome from this study give us the best ANN model which eventually allows for early detection of boiler tube leakages, and forecast of a trip before the real shutdown. This will eventually reduce shutdowns in power plants.

Using a neural network in the search for the Higgs boson

International Nuclear Information System (INIS)

Hultqvist, K.; Jacobsson, R.; Johansson, K.E.

1995-01-01

The search for the Standard Model Higgs boson in high energy e + e - collisions requires analysis techniques which efficiently discriminate against the very large background. A classifier based on a feed-forward neural network has been extensively used in a search in the channel where the Higgs boson is produced in association with neutrinos. The method has significantly improved the sensitivity of the search. With a simple preselection based on event topology followed by a neural network we have obtained a combined background rejection factor of more than 29 000 and a selection efficiency for Higgs particle events of 54%, assuming a mass of 55 GeV/c 2 for the Higgs boson. We describe here the details of the analysis with emphasis on the neural network. (orig.)

Turbulent boundary layer under the control of different schemes.

Science.gov (United States)

Qiao, Z X; Zhou, Y; Wu, Z

2017-06-01

This work explores experimentally the control of a turbulent boundary layer over a flat plate based on wall perturbation generated by piezo-ceramic actuators. Different schemes are investigated, including the feed-forward, the feedback, and the combined feed-forward and feedback strategies, with a view to suppressing the near-wall high-speed events and hence reducing skin friction drag. While the strategies may achieve a local maximum drag reduction slightly less than their counterpart of the open-loop control, the corresponding duty cycles are substantially reduced when compared with that of the open-loop control. The results suggest a good potential to cut down the input energy under these control strategies. The fluctuating velocity, spectra, Taylor microscale and mean energy dissipation are measured across the boundary layer with and without control and, based on the measurements, the flow mechanism behind the control is proposed.

Joint input shaping and feedforward for point-to-point motion : automated tuning for an industrial nanopositioning system

NARCIS (Netherlands)

Boeren, F.A.J.; Bruijnen, D.J.H.; Dijk, van N.J.M.; Oomen, T.A.E.

2014-01-01

Feedforward control can effectively compensate for the servo error induced by the reference signal if it is tuned appropriately. This paper aims to introduce a new joint input shaping and feedforward parametrization in iterative feedforward control. Such a parametrization has the potential to

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration.

Science.gov (United States)

Parrell, Benjamin; Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B

2017-09-20

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of

Cortical feedback signals generalise across different spatial frequencies of feedforward inputs

OpenAIRE

Revina, Yulia; Petro, Lucy S.; Muckli, Lars

2017-01-01

Visual processing in cortex relies on feedback projections contextualising feedforward information flow. Primary visual cortex (V1) has small receptive fields and processes feedforward information at a fine-grained spatial scale, whereas higher visual areas have larger, spatially invariant receptive fields. Therefore, feedback could provide coarse information about the global scene structure or alternatively recover fine-grained structure by targeting small receptive fields in V1. We tested i...

Structural Analysis of Three-dimensional Human Neural Tissue derived from Induced Pluripotent Stem Cells

DEFF Research Database (Denmark)

Terrence Brooks, Patrick; Rasmussen, Mikkel Aabech; Hyttel, Poul

2016-01-01

Objective: The present study aimed at establishing a method for production of a three-dimensional (3D) human neural tissue derived from induced pluripotent stem cells (iPSCs) and analyzing the outcome by a combination of tissue ultrastructure and expression of neural markers. Methods: A two......-step cell culture procedure was implemented by subjecting human iPSCs to a 3D scaffoldbased neural differentiation protocol. First, neural fate-inducing small molecules were used to create a neuroepithelial monolayer. Second, the monolayer was trypsinized into single cells and seeded into a porous...... polystyrene scaffold and further cultured to produce a 3D neural tissue. The neural tissue was characterized by a combination of immunohistochemistry and transmission electron microscopy (TEM). Results: iPSCs developed into a 3D neural tissue expressing markers for neural progenitor cells, early neural...

Automatic detection of photoresist residual layer in lithography using a neural classification approach

KAUST Repository

Gereige, Issam

2012-09-01

Photolithography is a fundamental process in the semiconductor industry and it is considered as the key element towards extreme nanoscale integration. In this technique, a polymer photo sensitive mask with the desired patterns is created on the substrate to be etched. Roughly speaking, the areas to be etched are not covered with polymer. Thus, no residual layer should remain on these areas in order to insure an optimal transfer of the patterns on the substrate. In this paper, we propose a nondestructive method based on a classification approach achieved by artificial neural network for automatic residual layer detection from an ellipsometric signature. Only the case of regular defect, i.e. homogenous residual layer, will be considered. The limitation of the method will be discussed. Then, an experimental result on a 400 nm period grating manufactured with nanoimprint lithography is analyzed with our method. © 2012 Elsevier B.V. All rights reserved.

Design and Validation of Optimized Feedforward with Robust Feedback Control of a Nuclear Reactor

International Nuclear Information System (INIS)

Shaffer, Roman; He Weidong; Edwards, Robert M.

2004-01-01

Design applications for robust feedback and optimized feedforward control, with confirming results from experiments conducted on the Pennsylvania State University TRIGA reactor, are presented. The combination of feedforward and feedback control techniques complement each other in that robust control offers guaranteed closed-loop stability in the presence of uncertainties, and optimized feedforward offers an approach to achieving performance that is sometimes limited by overly conservative robust feedback control. The design approach taken in this work combines these techniques by first designing robust feedback control. Alternative methods for specifying a low-order linear model and uncertainty specifications, while seeking as much performance as possible, are discussed and evaluated. To achieve desired performance characteristics, the optimized feedforward control is then computed by using the nominal nonlinear plant model that incorporates the robust feedback control

Welding and manufacture technology of three-layer bellows

International Nuclear Information System (INIS)

Dou Zhengping; Jiang Yunbo; Xie Zhiqiang; Pei Qiusheng; Wang Jindong; Zhang Zhiqiang

2005-01-01

Using argon tungsten arc welding the technology research and manufacture was carried out for three-layer bellows with the diameter of 350 mm or 230 mm. The theory rigidity and fatigue intensity of welded bellows were experimentally confirmed, the reasonable welding parameters was selected, and three-layer welded bellows with the diameter of 350 mm or 230 mm were manufactured. Quality of welded bellows accords with techniques standard. (authors)

MIMO feed-forward design in wafer scanners using a gradient approximation-based algorithm

NARCIS (Netherlands)

Heertjes, M.F.; Hennekens, D.W.T.; Steinbuch, M.

2010-01-01

An experimental demonstration is given of a data-based multi-input multi-output (MIMO) feed-forward control design applied to the motion systems of a wafer scanner. Atop a nominal single-input single-output (SISO) feed-forward controller, a MIMO controller is designed having a finite impulse

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Science.gov (United States)

Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

2017-10-01

To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Implementation of a feed-forward artificial neural network in VHDL on FPGA

NARCIS (Netherlands)

Dondon, P.; Carvalho, J.; Gardere, R.; Lahalle, P.; Tsenov, G.; Mladenov, V.M.; Reljin, B.; Stankovic, S.

2014-01-01

Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and

Prediction of geomagnetic storms from solar wind data with the use of a neural network

Directory of Open Access Journals (Sweden)

H. Lundstedt

Full Text Available An artificial feed-forward neural network with one hidden layer and error back-propagation learning is used to predict the geomagnetic activity index (D_st one hour in advance. The B_z-component and Σ_Bz, the density, and the velocity of the solar wind are used as input to the network. The network is trained on data covering a total of 8700 h, extracted from the 25-year period from 1963 to 1987, taken from the NSSDC data base. The performance of the network is examined with test data, not included in the training set, which covers 386 h and includes four different storms. Whilst the network predicts the initial and main phase well, the recovery phase is not modelled correctly, implying that a single hidden layer error back-propagation network is not enough, if the measured D_st is not available instantaneously. The performance of the network is independent of whether the raw parameters are used, or the electric field and square root of the dynamical pressure.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

Science.gov (United States)

Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-12-01

This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Planning Training Loads for The 400 M Hurdles in Three-Month Mesocycles Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Przednowek Krzysztof

2017-12-01

Full Text Available This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Classification of Atrial Septal Defect and Ventricular Septal Defect with Documented Hemodynamic Parameters via Cardiac Catheterization by Genetic Algorithms and Multi-Layered Artificial Neural Network

Directory of Open Access Journals (Sweden)

Mustafa Yıldız

2012-08-01

Full Text Available Introduction: We aimed to develop a classification method to discriminate ventricular septal defect and atrial septal defect by using severalhemodynamic parameters.Patients and Methods: Forty three patients (30 atrial septal defect, 13 ventricular septal defect; 26 female, 17 male with documentedhemodynamic parameters via cardiac catheterization are included to study. Such parameters as blood pressure values of different areas,gender, age and Qp/Qs ratios are used for classification. Parameters, we used in classification are determined by divergence analysismethod. Those parameters are; i pulmonary artery diastolic pressure, ii Qp/Qs ratio, iii right atrium pressure, iv age, v pulmonary arterysystolic pressure, vi left ventricular sistolic pressure, vii aorta mean pressure, viii left ventricular diastolic pressure, ix aorta diastolicpressure, x aorta systolic pressure. Those parameters detected from our study population, are uploaded to multi-layered artificial neuralnetwork and the network was trained by genetic algorithm.Results: Trained cluster consists of 14 factors (7 atrial septal defect and 7 ventricular septal defect. Overall success ratio is 79.2%, andwith a proper instruction of artificial neural network this ratio increases up to 89%.Conclusion: Parameters, belonging to artificial neural network, which are needed to be detected by the investigator in classical methods,can easily be detected with the help of genetic algorithms. During the instruction of artificial neural network by genetic algorithms, boththe topology of network and factors of network can be determined. During the test stage, elements, not included in instruction cluster, areassumed as in test cluster, and as a result of this study, we observed that multi-layered artificial neural network can be instructed properly,and neural network is a successful method for aimed classification.

Prediction by artificial neural networks of the physicochemical quality of cane molasses vinegar by time-temperature effect of food to flash evaporator-distiller

Directory of Open Access Journals (Sweden)

Víctor Vásquez V

2010-03-01

Full Text Available It was predicted via Artificial Neural Networks (ANN important physicochemical characteristics of molasses vinegar: pH, density, total acidity, ethanol, total aldehydes and furfural, obtained by flash evaporation operations and flash distillation clarification. Alcoholic and acetic fermented molasses were fed to a flash evaporator at four temperatures (61, 66, 71 and 76 ° C and in three times (25, 35 and 45 min. The prediction was made with two networks: ANN and ANN-A-B, both with good performance. The ANN-A was of the feedforward (FF type with Backpropagation (BP training algorithms and set of Levenberg-Marquardt (LM weights adjustment, topology: 6 inputs (operations data of flash evaporation-distillation, 7 linear outputs (physicochemical characteristics, 9 tangent sigmoidal neurons in 1 hidden layer, 0.5 moment coefficient, 0.01 learning rate, 0.0001 error goal and 20 training stages. The ANN-A showed better performance than a statistical model of first order. The ANN-B also FF, BP and LM algorithms, topology: 2 inputs (data from flash evaporation, 7 linear outputs (physical and chemical characteristics, 84 logarithm sigmoid neurons in 1 hidden layer, 0.5 moment coefficient, 0.01 learning rate, 0.0001 error goal and 300 training stages. The ANN-B showed the same predictive capacity as a statistical model of the first-order with interaction of terms.

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

Science.gov (United States)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

LEARNING ALGORITHM EFFECT ON MULTILAYER FEED FORWARD ARTIFICIAL NEURAL NETWORK PERFORMANCE IN IMAGE CODING

Directory of Open Access Journals (Sweden)

OMER MAHMOUD

2007-08-01

Full Text Available One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.

Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

International Nuclear Information System (INIS)

Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh, V. R.; Seyedkashi, S. M. H.

2011-01-01

Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit.

Directory of Open Access Journals (Sweden)

Tiffany Kee

2015-10-01

Full Text Available Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs, provide feed-forward inhibition onto Kenyon cells (KCs to maintain their sparse firing--a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN, is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.

A Feed-forward Geometrical Compensation and Adaptive Feedback Control Algorithm for Hydraulic Robot Manipulators

DEFF Research Database (Denmark)

Conrad, Finn; Zhou, Jianjun; Gabacik, Andrzej

1998-01-01

Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control.......Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control....

Distinct patterns of corticogeniculate feedback to different layers of the lateral geniculate nucleus

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Ichida JM

2014-09-01

Full Text Available Jennifer M Ichida,1 Julia A Mavity-Hudson,2 Vivien A Casagrande1–3 1Department of Psychology, 2Department of Cell and Developmental Biology, 3Department of Ophthalmology and Visual Sciences, Vanderbilt University, Nashville, TN, USA Abstract: In primates, feedforward visual pathways from retina to lateral geniculate nucleus (LGN are segregated to different layers. These layers also receive strong reciprocal feedback pathways from cortex. The degree to which feedforward streams in primates are segregated from feedback streams remains unclear. Here, we asked whether corticogeniculate cells that innervate the magnocellular (M, parvocellular (P, and koniocellular (K layers of the LGN in the prosimian primate bush baby (Otolemur garnettii can be distinguished based on either the laminar distribution or morphological characteristics of their axons and synaptic contacts in LGN, or on their cell body position, size, and dendritic distribution in cortex. Corticogeniculate axons and synapses were labeled anterogradely with biotinylated dextran injections in layer 6 of cortex. Corticogeniculate cell bodies were first labeled with fluorescent dextran injections limited to individual M, P, or K LGN layers and then filled with biotinylated Lucifer yellow. Results showed that feedback to the M or P LGN layers arises from cells with dendrites primarily confined to cortical layer 6 and axons restricted to either M or P LGN layers, but not both. Feedback to K LGN layers arises from cells: 1 whose dendrites distribute rather evenly across cortical layers 5 and 6; 2 whose dendrites always extend into layer 4; and 3 whose axons are never confined to K layers but always overlap with either P or M layers. Corticogeniculate axons also showed distributions that were retinotopically precise based on known receptive field sizes of layer 6 cells, and these axons mainly made synapses with glutamatergic projection neurons in the LGN in all layers. Taken together with prior

A combined stochastic feedforward and feedback control design methodology with application to autoland design

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Halyo, Nesim

1987-01-01

A combined stochastic feedforward and feedback control design methodology was developed. The objective of the feedforward control law is to track the commanded trajectory, whereas the feedback control law tries to maintain the plant state near the desired trajectory in the presence of disturbances and uncertainties about the plant. The feedforward control law design is formulated as a stochastic optimization problem and is embedded into the stochastic output feedback problem where the plant contains unstable and uncontrollable modes. An algorithm to compute the optimal feedforward is developed. In this approach, the use of error integral feedback, dynamic compensation, control rate command structures are an integral part of the methodology. An incremental implementation is recommended. Results on the eigenvalues of the implemented versus designed control laws are presented. The stochastic feedforward/feedback control methodology is used to design a digital automatic landing system for the ATOPS Research Vehicle, a Boeing 737-100 aircraft. The system control modes include localizer and glideslope capture and track, and flare to touchdown. Results of a detailed nonlinear simulation of the digital control laws, actuator systems, and aircraft aerodynamics are presented.

Back-propagation neural network-based approximate analysis of true stress-strain behaviors of high-strength metallic material

International Nuclear Information System (INIS)

Doh, Jaeh Yeok; Lee, Jong Soo; Lee, Seung Uk

2016-01-01

In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material's behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity's effect on the material property through a first-order sensitivity analysis.

LIDAR Wind Speed Measurement Analysis and Feed-Forward Blade Pitch Control for Load Mitigation in Wind Turbines: January 2010--January 2011

Energy Technology Data Exchange (ETDEWEB)

Dunne, F.; Simley, E.; Pao, L.Y.

2011-10-01

This report examines the accuracy of measurements that rely on Doppler LIDAR systems to determine their applicability to wind turbine feed-forward control systems and discusses feed-forward control system designs that use preview wind measurements. Light Detection and Ranging (LIDAR) systems are able to measure the speed of incoming wind before it interacts with a wind turbine rotor. These preview wind measurements can be used in feed-forward control systems designed to reduce turbine loads. However, the degree to which such preview-based control techniques can reduce loads by reacting to turbulence depends on how accurately the incoming wind field can be measured. The first half of this report examines the accuracy of different measurement scenarios that rely on coherent continuous-wave or pulsed Doppler LIDAR systems to determine their applicability to feed-forward control. In particular, the impacts of measurement range and angular offset from the wind direction are studied for various wind conditions. A realistic case involving a scanning LIDAR unit mounted in the spinner of a wind turbine is studied in depth with emphasis on choices for scan radius and preview distance. The effects of turbulence parameters on measurement accuracy are studied as well. Continuous-wave and pulsed LIDAR models based on typical commercially available units were used in the studies present in this report. The second half of this report discusses feed-forward control system designs that use preview wind measurements. Combined feedback/feed-forward blade pitch control is compared to industry standard feedback control when simulated in realistic turbulent above-rated winds. The feed-forward controllers are designed to reduce fatigue loads, increasing turbine lifetime and therefore reducing the cost of energy. Three feed-forward designs are studied: non-causal series expansion, Preview Control, and optimized FIR filter. The input to the feed-forward controller is a measurement of

Rule extraction from minimal neural networks for credit card screening.

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Setiono, Rudy; Baesens, Bart; Mues, Christophe

2011-08-01

While feedforward neural networks have been widely accepted as effective tools for solving classification problems, the issue of finding the best network architecture remains unresolved, particularly so in real-world problem settings. We address this issue in the context of credit card screening, where it is important to not only find a neural network with good predictive performance but also one that facilitates a clear explanation of how it produces its predictions. We show that minimal neural networks with as few as one hidden unit provide good predictive accuracy, while having the added advantage of making it easier to generate concise and comprehensible classification rules for the user. To further reduce model size, a novel approach is suggested in which network connections from the input units to this hidden unit are removed by a very straightaway pruning procedure. In terms of predictive accuracy, both the minimized neural networks and the rule sets generated from them are shown to compare favorably with other neural network based classifiers. The rules generated from the minimized neural networks are concise and thus easier to validate in a real-life setting.

A new on-chip all-digital three-phase full-bridge dc/ac power inverter with feedforward and frequency control techniques.

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Chen, Jiann-Jong; Kung, Che-Min

2010-09-01

The communication speed between components is far from satisfactory. To achieve high speed, simple control system configuration, and low cost, a new on-chip all-digital three-phase dc/ac power inverter using feedforward and frequency control techniques is proposed. The controller of the proposed power inverter, called the shift register, consists of six-stage D-latch flip-flops with a goal of achieving low-power consumption and area efficiency. Variable frequency is achieved by controlling the clocks of the shift register. One advantage regarding the data signal (D) and the common clock (CK) is that, regardless of the phase difference between the two, all of the D-latch flip-flops are capable of delaying data by one CK period. To ensure stability, the frequency of CK must be six times higher than that of D. The operation frequency of the proposed power inverter ranges from 10 Hz to 2 MHz, and the maximum output loading current is 0.8 A. The prototype of the proposed circuit has been fabricated with TSMC 0.35 μm 2P4M CMOS processes. The total chip area is 2.333 x 1.698 mm2. The three-phase dc/ac power inverter is applicable in uninterrupted power supplies, cold cathode fluorescent lamps, and motors, because of its ability to convert the dc supply voltage into the three-phase ac power sources.

Elastodynamic behavior of the three dimensional layer-by-layer metamaterial structure

International Nuclear Information System (INIS)

Aravantinos-Zafiris, N.; Sigalas, M. M.; Economou, E. N.

2014-01-01

In this work, we numerically investigate for the first time the elastodynamic behavior of a three dimensional layer-by-layer rod structure, which is easy to fabricate and has already proved to be very efficient as a photonic crystal. The Finite Difference Time Domain method was used for the numerical calculations. For the rods, several materials were examined and the effects of all the geometric parameters of the structure were also numerically investigated. Additionally, two modifications of the structure were included in our calculations. The results obtained here (for certain geometric parameters), exhibiting a high ratio of longitudinal over transverse sound velocity and therefore a close approach to ideal pentamode behavior over a frequency range, clearly show that the layer-by-layer rod structure, besides being an efficient photonic crystal, is a very serious contender as an elastodynamic metamaterial.

Perceptual decision-making difficulty modulates feedforward effective connectivity to the dorsolateral prefrontal cortex

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Bidhan eLamichhane

2015-09-01

Full Text Available Diverse cortical structures are known to coordinate activity as a network in relaying and processing of visual information to discriminate visual objects. However, how this discrimination is achieved is still largely unknown. To contribute to answering this question, we used face-house categorization tasks with three levels of noise in face and house images in functional magnetic resonance imaging (fMRI experiments involving thirty-three participants. The behavioral performance error and response time (RT were correlated with noise in face-house images. We then built dynamical causal models (DCM of fMRI blood-oxygenation level dependent (BOLD signals from the face and house category-specific regions in ventral temporal cortex, the fusiform face area (FFA and parahippocampal place area (PPA, and the dorsolateral prefrontal cortex (dlPFC. We found a strong feed-forward intrinsic connectivity pattern from FFA and PPA to dlPFC. Importantly, the feed-forward connectivity to dlPFC was significantly modulated by the perception of both faces and houses. The dlPFC-BOLD activity, the connectivity from FFA and PPA to the dlPFC all increased with noise level. These results suggest that the FFA-PPA-dlPFC network plays an important role for relaying and integrating competing sensory information to arrive at perceptual decisions.

Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

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Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

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Jianzheng Liu

2016-01-01

Full Text Available This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.

Stochastic Feedforward Control Technique

Science.gov (United States)

Halyo, Nesim

1990-01-01

Class of commanded trajectories modeled as stochastic process. Advanced Transport Operating Systems (ATOPS) research and development program conducted by NASA Langley Research Center aimed at developing capabilities for increases in capacities of airports, safe and accurate flight in adverse weather conditions including shear, winds, avoidance of wake vortexes, and reduced consumption of fuel. Advances in techniques for design of modern controls and increased capabilities of digital flight computers coupled with accurate guidance information from Microwave Landing System (MLS). Stochastic feedforward control technique developed within context of ATOPS program.

Proinflammatory Cytokine Infusion Attenuates LH's Feedforward on Testosterone Secretion: Modulation by Age.

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Veldhuis, Johannes; Yang, Rebecca; Roelfsema, Ferdinand; Takahashi, Paul

2016-02-01

In the experimental animal, inflammatory signals quench LH's feedforward drive of testosterone (T) secretion and appear to impair GnRH-LH output. The degree to which such suppressive effects operate in the human is not known. To test the hypothesis that IL-2 impairs LH's feedforward drive on T and T's feedback inhibition of LH secretion in healthy men. Mayo Center for Translational Science Activities. A total of 35 healthy men, 17 young and 18 older. Randomized prospective double-blind saline-controlled study of IL-2 infusion in 2 doses with concurrent 10-minute blood sampling for 24 hours. Deconvolution analysis of LH and T secretion. After saline injection, older compared with young men exhibited reduced LH feedforward drive on T secretion (P enforces biochemical hypogonadism, viz, combined feedforward block and feedback amplification; and 2) unequal absolute inhibition of T and LH secretion by IL-2 in young and older men. These outcomes establish that the male gonadal axis is susceptible to dual-site suppression by a prototypic inflammatory mediator. Thus, we postulate that selected ILs might also enforce male hypogonadism in chronic systemic inflammation.

Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking

International Nuclear Information System (INIS)

Yildiz, Nihat; Akkoyun, Serkan

2013-01-01

Highlights: ► Detector responses in neutron–gamma discrimination were estimated by neural networks. ► Novel consistent neural network empirical physical formulas (EPFs) were constructed for detector responses. ► The EPFs are of explicit mathematical functional form. ► The EPFs can be used to derive various physical functions relevant to neutron–gamma discrimination in gamma ray tracking. -- Abstract: Gamma ray tracking is an efficient detection technique in studying exotic nuclei which lies far from beta stability line. To achieve very powerful and extraordinary resolution ability, new detectors based on gamma ray tracking are currently being developed. To reach this achievement, the neutron–gamma discrimination in these detectors is also an important task. In this paper, by suitable layered feedforward neural networks (LFNNs), we have constructed novel and consistent empirical physical formulas (EPFs) for some highly nonlinear detector counts measured in neutron–gamma discrimination. The detector counts data used in the discrimination was actually borrowed from our previous paper. The counts used here had been originally measured versus the following parameters: energy deposited in the first interaction points, difference in the incoming direction of initial gamma rays, and finally figure of merit values of the clusters determined by tracking. The LFNN–EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN–EPFs can be used to derivate further physical functions which might be potentially relevant to neutron–gamma discrimination performance of gamma ray tracking.

Layers and Multilayers of Self-Assembled Polymers: Tunable Engineered Extracellular Matrix Coatings for Neural Cell Growth.

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Landry, Michael J; Rollet, Frédéric-Guillaume; Kennedy, Timothy E; Barrett, Christopher J

2018-03-12

Growing primary cells and tissue in long-term cultures, such as primary neural cell culture, presents many challenges. A critical component of any environment that supports neural cell growth in vivo is an appropriate 2-D surface or 3-D scaffold, typically in the form of a thin polymer layer that coats an underlying plastic or glass substrate and aims to mimic critical aspects of the extracellular matrix. A fundamental challenge to mimicking a hydrophilic, soft natural cell environment is that materials with these properties are typically fragile and are difficult to adhere to and stabilize on an underlying plastic or glass cell culture substrate. In this review, we highlight the current state of the art and overview recent developments of new artificial extracellular matrix (ECM) surfaces for in vitro neural cell culture. Notably, these materials aim to strike a balance between being hydrophilic and soft while also being thick, stable, robust, and bound well to the underlying surface to provide an effective surface to support long-term cell growth. We focus on improved surface and scaffold coating systems that can mimic the natural physicochemical properties that enhance neuronal survival and growth, applied as soft hydrophilic polymer coatings for both in vitro cell culture and for implantable neural probes and 3-D matrixes that aim to enhance stability and longevity to promote neural biocompatibility in vivo. With respect to future developments, we outline four emerging principles that serve to guide the development of polymer assemblies that function well as artificial ECMs: (a) design inspired by biological systems and (b) the employment of principles of aqueous soft bonding and self-assembly to achieve (c) a high-water-content gel-like coating that is stable over time in a biological environment and possesses (d) a low modulus to more closely mimic soft, compliant real biological tissue. We then highlight two emerging classes of thick material coatings that

On-line validation of feedwater flow rate in nuclear power plants using neural networks

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1994-01-01

On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%

Supervised learning of probability distributions by neural networks

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Baum, Eric B.; Wilczek, Frank

1988-01-01

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

Sliding-mode control combined with improved adaptive feedforward for wafer scanner

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Li, Xiaojie; Wang, Yiguang

2018-03-01

In this paper, a sliding-mode control method combined with improved adaptive feedforward is proposed for wafer scanner to improve the tracking performance of the closed-loop system. Particularly, In addition to the inverse model, the nonlinear force ripple effect which may degrade the tracking accuracy of permanent magnet linear motor (PMLM) is considered in the proposed method. The dominant position periodicity of force ripple is determined by using the Fast Fourier Transform (FFT) analysis for experimental data and the improved feedforward control is achieved by the online recursive least-squares (RLS) estimation of the inverse model and the force ripple. The improved adaptive feedforward is given in a general form of nth-order model with force ripple effect. This proposed method is motivated by the motion controller design of the long-stroke PMLM and short-stroke voice coil motor for wafer scanner. The stability of the closed-loop control system and the convergence of the motion tracking are guaranteed by the proposed sliding-mode feedback and adaptive feedforward methods theoretically. Comparative experiments on a precision linear motion platform can verify the correctness and effectiveness of the proposed method. The experimental results show that comparing to traditional method the proposed one has better performance of rapidity and robustness, especially for high speed motion trajectory. And, the improvements on both tracking accuracy and settling time can be achieved.

Neural Network Models for Free Radical Polymerization of Methyl Methacrylate

International Nuclear Information System (INIS)

Curteanu, S.; Leon, F.; Galea, D.

2003-01-01