Jiang, Guangli; Liu, Leibo; Zhu, Wenping; Yin, Shouyi; Wei, Shaojun
2015-09-04
This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.
Directory of Open Access Journals (Sweden)
Guangli Jiang
2015-09-01
Full Text Available This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.
Hybrid discrete-time neural networks.
Cao, Hongjun; Ibarz, Borja
2010-11-13
Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.
Cycles of a discrete time bipolar artificial neural network
International Nuclear Information System (INIS)
Cheng Suisun; Chen, J.-S.; Yueh, W.-C.
2009-01-01
A discrete time bipolar neural network depending on two parameters is studied. It is observed that its dynamical behaviors can be classified into six cases. For each case, the long time behaviors can be summarized in terms of fixed points, periodic points, basin of attractions, and related initial distributions. Mathematical reasons are supplied for these observations and applications in cellular automata are illustrated.
International Nuclear Information System (INIS)
Huang Zhenkun; Wang Xinghua; Gao Feng
2006-01-01
In this Letter, we discuss discrete-time analogue of a continuous-time cellular neural network. Sufficient conditions are obtained for the existence of a unique almost periodic sequence solution which is globally attractive. Our results demonstrate dynamics of the formulated discrete-time analogue as mathematical models for the continuous-time cellular neural network in almost periodic case. Finally, a computer simulation illustrates the suitability of our discrete-time analogue as numerical algorithms in simulating the continuous-time cellular neural network conveniently
Discrete-time BAM neural networks with variable delays
Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi
2007-07-01
This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.
Discrete-time BAM neural networks with variable delays
International Nuclear Information System (INIS)
Liu Xinge; Tang Meilan; Martin, Ralph; Liu Xinbi
2007-01-01
This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development
Discrete-time bidirectional associative memory neural networks with variable delays
International Nuclear Information System (INIS)
Liang Jinling; Cao Jinde; Ho, Daniel W.C.
2005-01-01
Based on the linear matrix inequality (LMI), some sufficient conditions are presented in this Letter for the existence, uniqueness and global exponential stability of the equilibrium point of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Some of the stability criteria obtained in this Letter are delay-dependent, and some of them are delay-independent, they are less conservative than the ones reported so far in the literature. Furthermore, the results provide one more set of easily verified criteria for determining the exponential stability of discrete-time BAM neural networks
Discrete-time bidirectional associative memory neural networks with variable delays
Liang, variable delays [rapid communication] J.; Cao, J.; Ho, D. W. C.
2005-02-01
Based on the linear matrix inequality (LMI), some sufficient conditions are presented in this Letter for the existence, uniqueness and global exponential stability of the equilibrium point of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Some of the stability criteria obtained in this Letter are delay-dependent, and some of them are delay-independent, they are less conservative than the ones reported so far in the literature. Furthermore, the results provide one more set of easily verified criteria for determining the exponential stability of discrete-time BAM neural networks.
A new criterion for global robust stability of interval neural networks with discrete time delays
International Nuclear Information System (INIS)
Li Chuandong; Chen Jinyu; Huang Tingwen
2007-01-01
This paper further studies global robust stability of a class of interval neural networks with discrete time delays. By introducing an equivalent transformation of interval matrices, a new criterion on global robust stability is established. In comparison with the results reported in the literature, the proposed approach leads to results with less restrictive conditions. Numerical examples are also worked through to illustrate our results
Global stability of discrete-time recurrent neural networks with impulse effects
International Nuclear Information System (INIS)
Zhou, L; Li, C; Wan, J
2008-01-01
This paper formulates and studies a class of discrete-time recurrent neural networks with impulse effects. A stability criterion, which characterizes the effects of impulse and stability property of the corresponding impulse-free networks on the stability of the impulsive networks in an aggregate form, is established. Two simplified and numerically tractable criteria are also provided
Qi, Donglian; Liu, Meiqin; Qiu, Meikang; Zhang, Senlin
2010-08-01
This brief studies exponential H(infinity) synchronization of a class of general discrete-time chaotic neural networks with external disturbance. On the basis of the drive-response concept and H(infinity) control theory, and using Lyapunov-Krasovskii (or Lyapunov) functional, state feedback controllers are established to not only guarantee exponential stable synchronization between two general chaotic neural networks with or without time delays, but also reduce the effect of external disturbance on the synchronization error to a minimal H(infinity) norm constraint. The proposed controllers can be obtained by solving the convex optimization problems represented by linear matrix inequalities. Most discrete-time chaotic systems with or without time delays, such as Hopfield neural networks, cellular neural networks, bidirectional associative memory networks, recurrent multilayer perceptrons, Cohen-Grossberg neural networks, Chua's circuits, etc., can be transformed into this general chaotic neural network to be H(infinity) synchronization controller designed in a unified way. Finally, some illustrated examples with their simulations have been utilized to demonstrate the effectiveness of the proposed methods.
Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks
International Nuclear Information System (INIS)
Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.
2012-01-01
This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.
Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs
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Jidong Wang
2017-01-01
Full Text Available In this paper, the adaptive neural controllers of subsystems are proposed for a class of discrete-time switched nonlinear systems with dead zone inputs under arbitrary switching signals. Due to the complicated framework of the discrete-time switched nonlinear systems and the existence of the dead zone, it brings about difficulties for controlling such a class of systems. In addition, the radial basis function neural networks are employed to approximate the unknown terms of each subsystem. Switched update laws are designed while the parameter estimation is invariable until its corresponding subsystem is active. Then, the closed-loop system is stable and all the signals are bounded. Finally, to illustrate the effectiveness of the proposed method, an example is employed.
Global exponential stability for discrete-time neural networks with variable delays
International Nuclear Information System (INIS)
Chen Wuhua; Lu Xiaomei; Liang Dongying
2006-01-01
This Letter provides new exponential stability criteria for discrete-time neural networks with variable delays. The main technique is to reduce exponential convergence estimation of the neural network solution to that of one component of the corresponding solution by constructing Lyapunov function based on M-matrix. By introducing the tuning parameter diagonal matrix, the delay-independent and delay-dependent exponential stability conditions have been unified in the same mathematical formula. The effectiveness of the new results are illustrated by three examples
International Nuclear Information System (INIS)
Liu Yurong; Wang Zidong; Liu Xiaohui
2008-01-01
In this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov-Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions
Directory of Open Access Journals (Sweden)
Yan-Ke Du
2013-09-01
Full Text Available We study a class of discrete-time bidirectional ring neural network model with delay. We discuss the asymptotic stability of the origin and the existence of Neimark-Sacker bifurcations, by analyzing the corresponding characteristic equation. Employing M-matrix theory and the Lyapunov functional method, global asymptotic stability of the origin is derived. Applying the normal form theory and the center manifold theorem, the direction of the Neimark-Sacker bifurcation and the stability of bifurcating periodic solutions are obtained. Numerical simulations are given to illustrate the main results.
Global exponential stability of BAM neural networks with time-varying delays: The discrete-time case
Raja, R.; Marshal Anthoni, S.
2011-02-01
This paper deals with the problem of stability analysis for a class of discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. By employing the Lyapunov functional and linear matrix inequality (LMI) approach, a new sufficient conditions is proposed for the global exponential stability of discrete-time BAM neural networks. The proposed LMI based results can be easily checked by LMI control toolbox. Moreover, an example is also provided to demonstrate the effectiveness of the proposed method.
Impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks.
Chen, Wu-Hua; Lu, Xiaomei; Zheng, Wei Xing
2015-04-01
This paper investigates the problems of impulsive stabilization and impulsive synchronization of discrete-time delayed neural networks (DDNNs). Two types of DDNNs with stabilizing impulses are studied. By introducing the time-varying Lyapunov functional to capture the dynamical characteristics of discrete-time impulsive delayed neural networks (DIDNNs) and by using a convex combination technique, new exponential stability criteria are derived in terms of linear matrix inequalities. The stability criteria for DIDNNs are independent of the size of time delay but rely on the lengths of impulsive intervals. With the newly obtained stability results, sufficient conditions on the existence of linear-state feedback impulsive controllers are derived. Moreover, a novel impulsive synchronization scheme for two identical DDNNs is proposed. The novel impulsive synchronization scheme allows synchronizing two identical DDNNs with unknown delays. Simulation results are given to validate the effectiveness of the proposed criteria of impulsive stabilization and impulsive synchronization of DDNNs. Finally, an application of the obtained impulsive synchronization result for two identical chaotic DDNNs to a secure communication scheme is presented.
Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.
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.
Choi, Hyun Duck; Ahn, Choon Ki; Karimi, Hamid Reza; Lim, Myo Taeg
2017-10-01
This paper studies delay-dependent exponential dissipative and l 2 - l ∞ filtering problems for discrete-time switched neural networks (DSNNs) including time-delayed states. By introducing a novel discrete-time inequality, which is a discrete-time version of the continuous-time Wirtinger-type inequality, we establish new sets of linear matrix inequality (LMI) criteria such that discrete-time filtering error systems are exponentially stable with guaranteed performances in the exponential dissipative and l 2 - l ∞ senses. The design of the desired exponential dissipative and l 2 - l ∞ filters for DSNNs can be achieved by solving the proposed sets of LMI conditions. Via numerical simulation results, we show the validity of the desired discrete-time filter design approach.
Liu, Hongjian; Wang, Zidong; Shen, Bo; Huang, Tingwen; Alsaadi, Fuad E
2018-06-01
This paper is concerned with the globally exponential stability problem for a class of discrete-time stochastic memristive neural networks (DSMNNs) with both leakage delays as well as probabilistic time-varying delays. For the probabilistic delays, a sequence of Bernoulli distributed random variables is utilized to determine within which intervals the time-varying delays fall at certain time instant. The sector-bounded activation function is considered in the addressed DSMNN. By taking into account the state-dependent characteristics of the network parameters and choosing an appropriate Lyapunov-Krasovskii functional, some sufficient conditions are established under which the underlying DSMNN is globally exponentially stable in the mean square. The derived conditions are made dependent on both the leakage and the probabilistic delays, and are therefore less conservative than the traditional delay-independent criteria. A simulation example is given to show the effectiveness of the proposed stability criterion. Copyright © 2018 Elsevier Ltd. All rights reserved.
Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis
International Nuclear Information System (INIS)
Liu, Yurong; Wang, Zidong; Serrano, Alan; Liu, Xiaohui
2007-01-01
This Letter is concerned with the analysis problem of exponential stability for a class of discrete-time recurrent neural networks (DRNNs) with time delays. The delay is of the time-varying nature, and the activation functions are assumed to be neither differentiable nor strict monotonic. Furthermore, the description of the activation functions is more general than the recently commonly used Lipschitz conditions. Under such mild conditions, we first prove the existence of the equilibrium point. Then, by employing a Lyapunov-Krasovskii functional, a unified linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the DRNNs to be globally exponentially stable. It is shown that the delayed DRNNs are globally exponentially stable if a certain LMI is solvable, where the feasibility of such an LMI can be easily checked by using the numerically efficient Matlab LMI Toolbox. A simulation example is presented to show the usefulness of the derived LMI-based stability condition
Directory of Open Access Journals (Sweden)
Yiming Jiang
2016-01-01
Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.
Chen, Xiaofeng; Song, Qiankun; Li, Zhongshan; Zhao, Zhenjiang; Liu, Yurong
2018-07-01
This paper addresses the problem of stability for continuous-time and discrete-time quaternion-valued neural networks (QVNNs) with linear threshold neurons. Applying the semidiscretization technique to the continuous-time QVNNs, the discrete-time analogs are obtained, which preserve the dynamical characteristics of their continuous-time counterparts. Via the plural decomposition method of quaternion, homeomorphic mapping theorem, as well as Lyapunov theorem, some sufficient conditions on the existence, uniqueness, and global asymptotical stability of the equilibrium point are derived for the continuous-time QVNNs and their discrete-time analogs, respectively. Furthermore, a uniform sufficient condition on the existence, uniqueness, and global asymptotical stability of the equilibrium point is obtained for both continuous-time QVNNs and their discrete-time version. Finally, two numerical examples are provided to substantiate the effectiveness of the proposed results.
Jin, Long; Liao, Bolin; Liu, Mei; Xiao, Lin; Guo, Dongsheng; Yan, Xiaogang
2017-01-01
By incorporating the physical constraints in joint space, a different-level simultaneous minimization scheme, which takes both the robot kinematics and robot dynamics into account, is presented and investigated for fault-tolerant motion planning of redundant manipulator in this paper. The scheme is reformulated as a quadratic program (QP) with equality and bound constraints, which is then solved by a discrete-time recurrent neural network. Simulative verifications based on a six-link planar redundant robot manipulator substantiate the efficacy and accuracy of the presented acceleration fault-tolerant scheme, the resultant QP and the corresponding discrete-time recurrent neural network.
International Nuclear Information System (INIS)
Zhou Tiejun; Liu Yuehua; Li Xiaoping; Liu Yirong
2009-01-01
The discrete-time bidirectional associative memory neural network with periodic coefficients and infinite delays is studied. And not by employing the continuation theorem of coincidence degree theory as other literatures, but by constructing suitable Liapunov function, using fixed point theorem and some analysis techniques, a sufficient criterion is obtained which ensures the existence and global exponential stability of periodic solution for the type of discrete-time BAM neural network. The obtained result is less restrictive to the BAM neural networks than previously known criteria. Furthermore, it can be applied to the BAM neural network which signal transfer functions are neither bounded nor differentiable. In addition, an example and its numerical simulation are given to illustrate the effectiveness of the obtained result
Delay-Dependent Exponential Stability for Discrete-Time BAM Neural Networks with Time-Varying Delays
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Yonggang Chen
2008-01-01
Full Text Available This paper considers the delay-dependent exponential stability for discrete-time BAM neural networks with time-varying delays. By constructing the new Lyapunov functional, the improved delay-dependent exponential stability criterion is derived in terms of linear matrix inequality (LMI. Moreover, in order to reduce the conservativeness, some slack matrices are introduced in this paper. Two numerical examples are presented to show the effectiveness and less conservativeness of the proposed method.
Directory of Open Access Journals (Sweden)
Fei Chen
2013-01-01
Full Text Available This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabilization of the closed-loop system. A numerical example is illustrated to verify the efficiency of the proposed technique.
Dynamical Properties of Discrete-Time Background Neural Networks with Uniform Firing Rate
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Min Wan
2013-01-01
Full Text Available The dynamics of a discrete-time background network with uniform firing rate and background input is investigated. The conditions for stability are firstly derived. An invariant set is then obtained so that the nondivergence of the network can be guaranteed. In the invariant set, it is proved that all trajectories of the network starting from any nonnegative value will converge to a fixed point under some conditions. In addition, bifurcation and chaos are discussed. It is shown that the network can engender bifurcation and chaos with the increase of background input. The computations of Lyapunov exponents confirm the chaotic behaviors.
Directory of Open Access Journals (Sweden)
O. M. Kwon
2012-01-01
Full Text Available The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delay-dependent criteria for the stability of the networks are derived in terms of linear matrix inequalities (LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method.
Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks
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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.
International Nuclear Information System (INIS)
Kaslik, E.; Balint, St.
2009-01-01
In this paper, a bifurcation analysis is undertaken for a discrete-time Hopfield neural network of two neurons with two different delays and self-connections. Conditions ensuring the asymptotic stability of the null solution are found, with respect to two characteristic parameters of the system. It is shown that for certain values of these parameters, Fold or Neimark-Sacker bifurcations occur, but Flip and codimension 2 (Fold-Neimark-Sacker, double Neimark-Sacker, resonance 1:1 and Flip-Neimark-Sacker) bifurcations may also be present. The direction and the stability of the Neimark-Sacker bifurcations are investigated by applying the center manifold theorem and the normal form theory
Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L
2013-12-01
In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.
Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.
2016-07-01
This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.
Liu, Yan-Jun; Gao, Ying; Tong, Shaocheng; Chen, C L Philip
2016-01-01
In this paper, an effective adaptive control approach is constructed to stabilize a class of nonlinear discrete-time systems, which contain unknown functions, unknown dead-zone input, and unknown control direction. Different from linear dead zone, the dead zone, in this paper, is a kind of nonlinear dead zone. To overcome the noncausal problem, which leads to the control scheme infeasible, the systems can be transformed into a m -step-ahead predictor. Due to nonlinear dead-zone appearance, the transformed predictor still contains the nonaffine function. In addition, it is assumed that the gain function of dead-zone input and the control direction are unknown. These conditions bring about the difficulties and the complicacy in the controller design. Thus, the implicit function theorem is applied to deal with nonaffine dead-zone appearance, the problem caused by the unknown control direction can be resolved through applying the discrete Nussbaum gain, and the neural networks are used to approximate the unknown function. Based on the Lyapunov theory, all the signals of the resulting closed-loop system are proved to be semiglobal uniformly ultimately bounded. Moreover, the tracking error is proved to be regulated to a small neighborhood around zero. The feasibility of the proposed approach is demonstrated by a simulation example.
International Nuclear Information System (INIS)
Song, Qiankun; Wang, Zidong
2007-01-01
In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for a class of general discrete-time recurrent neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, and the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By employing the latest free-weighting matrix method, an appropriate Lyapunov-Krasovskii functional is constructed and several sufficient conditions are established to ensure the existence, uniqueness, and globally exponential stability of the periodic solution for the addressed neural network. The conditions are dependent on both the lower bound and upper bound of the time-varying time delays. Furthermore, the conditions are expressed in terms of the linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. Two simulation examples are given to show the effectiveness and less conservatism of the proposed criteria
Liu, Tao; Huang, Jie
2017-04-17
This paper presents a discrete-time recurrent neural network approach to solving systems of linear equations with two features. First, the system of linear equations may not have a unique solution. Second, the system matrix is not known precisely, but a sequence of matrices that converges to the unknown system matrix exponentially is known. The problem is motivated from solving the output regulation problem for linear systems. Thus, an application of our main result leads to an online solution to the output regulation problem for linear systems.
Indian Academy of Sciences (India)
We also describe discrete-time systems in terms of difference ... A more modern alternative, especially for larger systems, is to convert ... In other words, ..... picture?) State-variable equations are also called state-space equations because the ...
Yan, Huaicheng; Zhang, Hao; Yang, Fuwen; Zhan, Xisheng; Peng, Chen
2017-08-18
This paper is concerned with the guaranteed cost control problem for a class of Markov jump discrete-time neural networks (NNs) with event-triggered mechanism, asynchronous jumping, and fading channels. The Markov jump NNs are introduced to be close to reality, where the modes of the NNs and guaranteed cost controller are determined by two mutually independent Markov chains. The asynchronous phenomenon is considered, which increases the difficulty of designing required mode-dependent controller. The event-triggered mechanism is designed by comparing the relative measurement error with the last triggered state at the process of data transmission, which is used to eliminate dispensable transmission and reduce the networked energy consumption. In addition, the signal fading is considered for the effect of signal reflection and shadow in wireless networks, which is modeled by the novel Rice fading models. Some novel sufficient conditions are obtained to guarantee that the closed-loop system reaches a specified cost value under the designed jumping state feedback control law in terms of linear matrix inequalities. Finally, some simulation results are provided to illustrate the effectiveness of the proposed method.
Liu, Lei; Wang, Zhanshan; Zhang, Huaguang
2018-04-01
This paper is concerned with the robust optimal tracking control strategy for a class of nonlinear multi-input multi-output discrete-time systems with unknown uncertainty via adaptive critic design (ACD) scheme. The main purpose is to establish an adaptive actor-critic control method, so that the cost function in the procedure of dealing with uncertainty is minimum and the closed-loop system is stable. Based on the neural network approximator, an action network is applied to generate the optimal control signal and a critic network is used to approximate the cost function, respectively. In contrast to the previous methods, the main features of this paper are: 1) the ACD scheme is integrated into the controllers to cope with the uncertainty and 2) a novel cost function, which is not in quadric form, is proposed so that the total cost in the design procedure is reduced. It is proved that the optimal control signals and the tracking errors are uniformly ultimately bounded even when the uncertainty exists. Finally, a numerical simulation is developed to show the effectiveness of the present approach.
International Nuclear Information System (INIS)
Balasubramaniam, P.; Kalpana, M.; Rakkiyappan, R.
2012-01-01
Fuzzy cellular neural networks (FCNNs) are special kinds of cellular neural networks (CNNs). Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality (LMI) approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov—Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method. (interdisciplinary physics and related areas of science and technology)
Image Encryption and Chaotic Cellular Neural Network
Peng, Jun; Zhang, Du
Machine learning has been playing an increasingly important role in information security and assurance. One of the areas of new applications is to design cryptographic systems by using chaotic neural network due to the fact that chaotic systems have several appealing features for information security applications. In this chapter, we describe a novel image encryption algorithm that is based on a chaotic cellular neural network. We start by giving an introduction to the concept of image encryption and its main technologies, and an overview of the chaotic cellular neural network. We then discuss the proposed image encryption algorithm in details, which is followed by a number of security analyses (key space analysis, sensitivity analysis, information entropy analysis and statistical analysis). The comparison with the most recently reported chaos-based image encryption algorithms indicates that the algorithm proposed in this chapter has a better security performance. Finally, we conclude the chapter with possible future work and application prospects of the chaotic cellular neural network in other information assurance and security areas.
Principles of discrete time mechanics
Jaroszkiewicz, George
2014-01-01
Could time be discrete on some unimaginably small scale? Exploring the idea in depth, this unique introduction to discrete time mechanics systematically builds the theory up from scratch, beginning with the historical, physical and mathematical background to the chronon hypothesis. Covering classical and quantum discrete time mechanics, this book presents all the tools needed to formulate and develop applications of discrete time mechanics in a number of areas, including spreadsheet mechanics, classical and quantum register mechanics, and classical and quantum mechanics and field theories. A consistent emphasis on contextuality and the observer-system relationship is maintained throughout.
Alfa, Attahiru S
2016-01-01
This book introduces the theoretical fundamentals for modeling queues in discrete-time, and the basic procedures for developing queuing models in discrete-time. There is a focus on applications in modern telecommunication systems. It presents how most queueing models in discrete-time can be set up as discrete-time Markov chains. Techniques such as matrix-analytic methods (MAM) that can used to analyze the resulting Markov chains are included. This book covers single node systems, tandem system and queueing networks. It shows how queues with time-varying parameters can be analyzed, and illustrates numerical issues associated with computations for the discrete-time queueing systems. Optimal control of queues is also covered. Applied Discrete-Time Queues targets researchers, advanced-level students and analysts in the field of telecommunication networks. It is suitable as a reference book and can also be used as a secondary text book in computer engineering and computer science. Examples and exercises are includ...
Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias
2008-12-01
We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.
Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection
Directory of Open Access Journals (Sweden)
Kang Xie
2015-01-01
Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.
Novel paths towards neural cellular products for neurological disorders.
Daadi, Marcel M
2011-11-01
The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.
Exponential stability of delayed fuzzy cellular neural networks with diffusion
International Nuclear Information System (INIS)
Huang Tingwen
2007-01-01
The exponential stability of delayed fuzzy cellular neural networks (FCNN) with diffusion is investigated. Exponential stability, significant for applications of neural networks, is obtained under conditions that are easily verified by a new approach. Earlier results on the exponential stability of FCNN with time-dependent delay, a special case of the model studied in this paper, are improved without using the time-varying term condition: dτ(t)/dt < μ
Cellular neural networks for the stereo matching problem
International Nuclear Information System (INIS)
Taraglio, S.; Zanela, A.
1997-03-01
The applicability of the Cellular Neural Network (CNN) paradigm to the problem of recovering information on the tridimensional structure of the environment is investigated. The approach proposed is the stereo matching of video images. The starting point of this work is the Zhou-Chellappa neural network implementation for the same problem. The CNN based system we present here yields the same results as the previous approach, but without the many existing drawbacks
Stability analysis for cellular neural networks with variable delays
International Nuclear Information System (INIS)
Zhang Qiang; Wei Xiaopeng; Xu Jin
2006-01-01
Some sufficient conditions for the global exponential stability of cellular neural networks with variable delay are obtained by means of a method based on delay differential inequality. The method, which does not make use of Lyapunov functionals, is simple and effective for the stability analysis of neural networks with delay. Some previously established results in the literature are shown to be special cases of the presented result
Boltzmann learning of parameters in cellular neural networks
DEFF Research Database (Denmark)
Hansen, Lars Kai
1992-01-01
The use of Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann machine learning rule for parameter estimation is discussed. The learning rule can be used for models with hidden units, or for completely unsupervised learning. The latter is exemplified...
Global exponential stability for nonautonomous cellular neural networks with delays
International Nuclear Information System (INIS)
Zhang Qiang; Wei Xiaopeng; Xu Jin
2006-01-01
In this Letter, by utilizing Lyapunov functional method and Halanay inequalities, we analyze global exponential stability of nonautonomous cellular neural networks with delay. Several new sufficient conditions ensuring global exponential stability of the network are obtained. The results given here extend and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results
Discrete-time nonlinear sliding mode controller
African Journals Online (AJOL)
user
Keywords: Discrete-time delay system, Sliding mode control, nonlinear sliding ... of engineering systems such as chemical process control, delay in the actuator ...... instrumentation from Motilal Nehru National Institute of Technology (MNNIT),.
Quadratic Term Structure Models in Discrete Time
Marco Realdon
2006-01-01
This paper extends the results on quadratic term structure models in continuos time to the discrete time setting. The continuos time setting can be seen as a special case of the discrete time one. Recursive closed form solutions for zero coupon bonds are provided even in the presence of multiple correlated underlying factors. Pricing bond options requires simple integration. Model parameters may well be time dependent without scuppering such tractability. Model estimation does not require a r...
Symmetries in discrete-time mechanics
International Nuclear Information System (INIS)
Khorrami, M.
1996-01-01
Based on a general formulation for discrete-time quantum mechanics, introduced by M. Khorrami (Annals Phys. 224 (1995), 101), symmetries in discrete-time quantum mechanics are investigated. It is shown that any classical continuous symmetry leads to a conserved quantity in classical mechanics, as well as quantum mechanics. The transformed wave function, however, has the correct evolution if and only if the symmetry is nonanomalous. Copyright copyright 1996 Academic Press, Inc
Application of neural networks and cellular automata to calorimetric problems
Energy Technology Data Exchange (ETDEWEB)
Brenton, V; Fonvieille, H; Guicheney, C; Jousset, J; Roblin, Y; Tamin, F; Grenier, P
1994-09-01
Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author). 9 refs.; Submitted to Nuclear Instruments and Methods (NL).
Application of neural networks and cellular automata to calorimetric problems
International Nuclear Information System (INIS)
Brenton, V.; Fonvieille, H.; Guicheney, C.; Jousset, J.; Roblin, Y.; Tamin, F.; Grenier, P.
1994-09-01
Computing techniques based on parallel processing have been used to treat the information from the electromagnetic calorimeters in SLAC experiments E142/E143. Cluster finding and separation of overlapping showers are performed by a cellular automaton, pion and electron identification is done by using a multilayered neural network. Both applications are presented and their resulting performances are shown to be improved compared to more standard approaches. (author)
A learning algorithm for oscillatory cellular neural networks.
Ho, C Y.; Kurokawa, H
1999-07-01
We present a cellular type oscillatory neural network for temporal segregation of stationary input patterns. The model comprises an array of locally connected neural oscillators with connections limited to a 4-connected neighborhood. The architecture is reminiscent of the well-known cellular neural network that consists of local connection for feature extraction. By means of a novel learning rule and an initialization scheme, global synchronization can be accomplished without incurring any erroneous synchrony among uncorrelated objects. Each oscillator comprises two mutually coupled neurons, and neurons share a piecewise-linear activation function characteristic. The dynamics of traditional oscillatory models is simplified by using only one plastic synapse, and the overall complexity for hardware implementation is reduced. Based on the connectedness of image segments, it is shown that global synchronization and desynchronization can be achieved by means of locally connected synapses, and this opens up a tremendous application potential for the proposed architecture. Furthermore, by using special grouping synapses it is demonstrated that temporal segregation of overlapping gray-level and color segments can also be achieved. Finally, simulation results show that the learning rule proposed circumvents the problem of component mismatches, and hence facilitates a large-scale integration.
Discrete-time rewards model-checked
Larsen, K.G.; Andova, S.; Niebert, Peter; Hermanns, H.; Katoen, Joost P.
2003-01-01
This paper presents a model-checking approach for analyzing discrete-time Markov reward models. For this purpose, the temporal logic probabilistic CTL is extended with reward constraints. This allows to formulate complex measures – involving expected as well as accumulated rewards – in a precise and
Integration of Neural Networks and Cellular Automata for Urban Planning
Institute of Scientific and Technical Information of China (English)
Anthony Gar-on Yeh; LI Xia
2004-01-01
This paper presents a new type of cellular automata (CA) model for the simulation of alternative land development using neural networks for urban planning. CA models can be regarded as a planning tool because they can generate alternative urban growth. Alternative development patterns can be formed by using different sets of parameter values in CA simulation. A critical issue is how to define parameter values for realistic and idealized simulation. This paper demonstrates that neural networks can simplify CA models but generate more plausible results. The simulation is based on a simple three-layer network with an output neuron to generate conversion probability. No transition rules are required for the simulation. Parameter values are automatically obtained from the training of network by using satellite remote sensing data. Original training data can be assessed and modified according to planning objectives. Alternative urban patterns can be easily formulated by using the modified training data sets rather than changing the model.
Discrete time analysis of a repairable machine
Alfa, Attahiru Sule; Castro, I. T.
2002-01-01
We consider, in discrete time, a single machine system that operates for a period of time represented by a general distribution. This machine is subject to failures during operations and the occurrence of these failures depends on how many times the machine has previously failed. Some failures are repairable and the repair times may or may not depend on the number of times the machine was previously repaired. Repair times also have a general distribution. The operating times...
Single-crossover recombination in discrete time.
von Wangenheim, Ute; Baake, Ellen; Baake, Michael
2010-05-01
Modelling the process of recombination leads to a large coupled nonlinear dynamical system. Here, we consider a particular case of recombination in discrete time, allowing only for single crossovers. While the analogous dynamics in continuous time admits a closed solution (Baake and Baake in Can J Math 55:3-41, 2003), this no longer works for discrete time. A more general model (i.e. without the restriction to single crossovers) has been studied before (Bennett in Ann Hum Genet 18:311-317, 1954; Dawson in Theor Popul Biol 58:1-20, 2000; Linear Algebra Appl 348:115-137, 2002) and was solved algorithmically by means of Haldane linearisation. Using the special formalism introduced by Baake and Baake (Can J Math 55:3-41, 2003), we obtain further insight into the single-crossover dynamics and the particular difficulties that arise in discrete time. We then transform the equations to a solvable system in a two-step procedure: linearisation followed by diagonalisation. Still, the coefficients of the second step must be determined in a recursive manner, but once this is done for a given system, they allow for an explicit solution valid for all times.
Speeding Up Network Simulations Using Discrete Time
Lucas, Aaron; Armbruster, Benjamin
2013-01-01
We develop a way of simulating disease spread in networks faster at the cost of some accuracy. Instead of a discrete event simulation (DES) we use a discrete time simulation. This aggregates events into time periods. We prove a bound on the accuracy attained. We also discuss the choice of step size and do an analytical comparison of the computational costs. Our error bound concept comes from the theory of numerical methods for SDEs and the basic proof structure comes from the theory of numeri...
Construction of Discrete Time Shadow Price
International Nuclear Information System (INIS)
Rogala, Tomasz; Stettner, Lukasz
2015-01-01
In the paper expected utility from consumption over finite time horizon for discrete time markets with bid and ask prices and strictly concave utility function is considered. The notion of weak shadow price, i.e. an illiquid price, depending on the portfolio, under which the model without bid and ask price is equivalent to the model with bid and ask price is introduced. Existence and the form of weak shadow price is shown. Using weak shadow price usual (called in the paper strong) shadow price is then constructed
Discrete-Time Biomedical Signal Encryption
Directory of Open Access Journals (Sweden)
Victor Grigoraş
2017-12-01
Full Text Available Chaotic modulation is a strong method of improving communication security. Analog and discrete chaotic systems are presented in actual literature. Due to the expansion of digital communication, discrete-time systems become more efficient and closer to actual technology. The present contribution offers an in-depth analysis of the effects chaos encryption produce on 1D and 2D biomedical signals. The performed simulations show that modulating signals are precisely recovered by the synchronizing receiver if discrete systems are digitally implemented and the coefficients precisely correspond. Channel noise is also applied and its effects on biomedical signal demodulation are highlighted.
Cellular Neural Networks for NP-Hard Optimization
Directory of Open Access Journals (Sweden)
Mária Ercsey-Ravasz
2009-02-01
Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.
Fuzzy-cellular neural network for face recognition HCI Authentication
Hoomod, Haider K.; ali, Ahmed abd
2018-05-01
Because of the rapid development of mobile devices technology, ease of use and interact with humans. May have found a mobile device most uses in our communications. Mobile devices can carry large amounts of personal and sensitive data, but often left not guaranteed (pin) locks are inconvenient to use and thus have seen low adoption while biometrics is more convenient and less susceptible to fraud and manipulation. Were propose in this paper authentication technique for using a mobile face recognition based on cellular neural networks [1] and fuzzy rules control. The good speed and get recognition rate from applied the proposed system in Android system. The images obtained in real time for 60 persons each person has 20 t0 60 different shot face images (about 3600 images), were the results for (FAR = 0), (FRR = 1.66%), (FER = 1.66) and accuracy = 98.34
Neural networks and cellular automata in experimental high energy physics
Energy Technology Data Exchange (ETDEWEB)
Denby, B
1988-06-01
Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high energy physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hard-wired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions.
Neural networks and cellular automata in experimental high energy physics
International Nuclear Information System (INIS)
Denby, B.
1987-11-01
Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hardwired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions
Neural networks and cellular automata in experimental high energy physics
International Nuclear Information System (INIS)
Denby, B.
1988-01-01
Within the past few years, two novel computing techniques, cellular automata and neural networks, have shown considerable promise in the solution of problems of a very high degree of complexity, such as turbulent fluid flow, image processing, and pattern recognition. Many of the problems faced in experimental high energy physics are also of this nature. Track reconstruction in wire chambers and cluster finding in cellular calorimeters, for instance, involve pattern recognition and high combinatorial complexity since many combinations of hits or cells must be considered in order to arrive at the final tracks or clusters. Here we examine in what way connective network methods can be applied to some of the problems of experimental high energy physics. It is found that such problems as track and cluster finding adapt naturally to these approaches. When large scale hard-wired connective networks become available, it will be possible to realize solutions to such problems in a fraction of the time required by traditional methods. For certain types of problems, faster solutions are already possible using model networks implemented on vector or other massively parallel machines. It should also be possible, using existing technology, to build simplified networks that will allow detailed reconstructed event information to be used in fast trigger decisions. (orig.)
Integrals of Motion for Discrete-Time Optimal Control Problems
Torres, Delfim F. M.
2003-01-01
We obtain a discrete time analog of E. Noether's theorem in Optimal Control, asserting that integrals of motion associated to the discrete time Pontryagin Maximum Principle can be computed from the quasi-invariance properties of the discrete time Lagrangian and discrete time control system. As corollaries, results for first-order and higher-order discrete problems of the calculus of variations are obtained.
Cellular neural networks for motion estimation and obstacle detection
Directory of Open Access Journals (Sweden)
D. Feiden
2003-01-01
Full Text Available Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN. It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.
International Nuclear Information System (INIS)
Liang Jinling; Cao Jinde
2004-01-01
First, convergence of continuous-time Bidirectional Associative Memory (BAM) neural networks are studied. By using Lyapunov functionals and some analysis technique, the delay-independent sufficient conditions are obtained for the networks to converge exponentially toward the equilibrium associated with the constant input sources. Second, discrete-time analogues of the continuous-time BAM networks are formulated and studied. It is shown that the convergence characteristics of the continuous-time systems are preserved by the discrete-time analogues without any restriction imposed on the uniform discretionary step size. An illustrative example is given to demonstrate the effectiveness of the obtained results
Chaos Control and Synchronization of Cellular Neural Network with Delays Based on OPNCL Control
International Nuclear Information System (INIS)
Qian, Tang; Xing-Yuan, Wang
2010-01-01
The problem of chaos control and complete synchronization of cellular neural network with delays is studied. Based on the open plus nonlinear closed loop (OPNCL) method, the control scheme and synchronization scheme are designed. Both the schemes can achieve the chaos control and complete synchronization of chaotic neural network respectively, and their validity is further verified by numerical simulation experiments. (general)
International Nuclear Information System (INIS)
Wang Jian; Lu Junguo
2008-01-01
In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits
Process algebra with timing : real time and discrete time
Baeten, J.C.M.; Middelburg, C.A.; Bergstra, J.A.; Ponse, A.J.; Smolka, S.A.
2001-01-01
We present real time and discrete time versions of ACP with absolute timing and relative timing. The starting-point is a new real time version with absolute timing, called ACPsat, featuring urgent actions and a delay operator. The discrete time versions are conservative extensions of the discrete
On the application of Discrete Time Optimal Control Concepts to ...
African Journals Online (AJOL)
On the application of Discrete Time Optimal Control Concepts to Economic Problems. ... Journal of the Nigerian Association of Mathematical Physics ... Abstract. An extension of the use of the maximum principle to solve Discrete-time Optimal Control Problems (DTOCP), in which the state equations are in the form of general ...
Process algebra with timing: Real time and discrete time
Baeten, J.C.M.; Middelburg, C.A.
1999-01-01
We present real time and discrete time versions of ACP with absolute timing and relative timing. The startingpoint is a new real time version with absolute timing, called ACPsat , featuring urgent actions and a delay operator. The discrete time versions are conservative extensions of the discrete
Cellular therapy after spinal cord injury using neural progenitor cells
Vroemen, Maurice
2006-01-01
In this thesis, the possibilities and limitations of cell-based therapies after spinal cord injury are explored. Particularly, the potential of adult derived neural progenitor cell (NPC) grafts to function as a permissive substrate for axonal regeneration was investigated. It was found that syngenic
Discrete-time inverse optimal control for nonlinear systems
Sanchez, Edgar N
2013-01-01
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). Th
On synchronized regions of discrete-time complex dynamical networks
International Nuclear Information System (INIS)
Duan Zhisheng; Chen Guanrong
2011-01-01
In this paper, the local synchronization of discrete-time complex networks is studied. First, it is shown that for any natural number n, there exists a discrete-time network which has at least left floor n/2 right floor +1 disconnected synchronized regions for local synchronization, which implies the possibility of intermittent synchronization behaviors. Different from the continuous-time networks, the existence of an unbounded synchronized region is impossible for discrete-time networks. The convexity of the synchronized regions is also characterized based on the stability of a class of matrix pencils, which is useful for enlarging the stability region so as to improve the network synchronizability.
Further results on passivity analysis of delayed cellular neural networks
International Nuclear Information System (INIS)
Park, Ju H.
2007-01-01
The passivity condition for delayed neural networks with uncertainties is considered in this article. From simple extension of a recent work for stability analysis of the system, a new criterion for the passivity of the system is derived in terms of linear matrix inequalities (LMIs), which can be easily solved by using various convex optimization algorithms. A numerical example is given to show the usefulness of our result
Global exponential stability of mixed discrete and distributively delayed cellular neural network
International Nuclear Information System (INIS)
Yao Hong-Xing; Zhou Jia-Yan
2011-01-01
This paper concernes analysis for the global exponential stability of a class of recurrent neural networks with mixed discrete and distributed delays. It first proves the existence and uniqueness of the balance point, then by employing the Lyapunov—Krasovskii functional and Young inequality, it gives the sufficient condition of global exponential stability of cellular neural network with mixed discrete and distributed delays, in addition, the example is provided to illustrate the applicability of the result. (general)
International Nuclear Information System (INIS)
Huang Zaitang; Yang Qigui
2009-01-01
The paper considers the problems of existence of quadratic mean almost periodic and global exponential stability for stochastic cellular neural networks with delays. By employing the Holder's inequality and fixed points principle, we present some new criteria ensuring existence and uniqueness of a quadratic mean almost periodic and global exponential stability. These criteria are important in signal processing and the design of networks. Moreover, these criteria are also applied in others stochastic biological neural systems.
Cryptanalysis of a discrete-time synchronous chaotic encryption system
International Nuclear Information System (INIS)
Arroyo, David; Alvarez, Gonzalo; Li Shujun; Li Chengqing; Nunez, Juana
2008-01-01
Recently a chaotic cryptosystem based on discrete-time synchronization has been proposed. Some weaknesses of that new encryption system are addressed and exploited in order to successfully cryptanalyze the system
On periodic orbits in discrete-time cascade systems
Directory of Open Access Journals (Sweden)
Huimin Li
2006-01-01
Full Text Available We present some results on existence, minimum period, number of periodic orbits, and stability of periodic orbits in discrete-time cascade systems. Some examples are presented to illustrate these results.
Discrete-time optimal control and games on large intervals
Zaslavski, Alexander J
2017-01-01
Devoted to the structure of approximate solutions of discrete-time optimal control problems and approximate solutions of dynamic discrete-time two-player zero-sum games, this book presents results on properties of approximate solutions in an interval that is independent lengthwise, for all sufficiently large intervals. Results concerning the so-called turnpike property of optimal control problems and zero-sum games in the regions close to the endpoints of the time intervals are the main focus of this book. The description of the structure of approximate solutions on sufficiently large intervals and its stability will interest graduate students and mathematicians in optimal control and game theory, engineering, and economics. This book begins with a brief overview and moves on to analyze the structure of approximate solutions of autonomous nonconcave discrete-time optimal control Lagrange problems.Next the structures of approximate solutions of autonomous discrete-time optimal control problems that are discret...
International Nuclear Information System (INIS)
Li Zuoan; Li Kelin
2009-01-01
In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.
Discrete-Time Filter Synthesis using Product of Gegenbauer Polynomials
N. Stojanovic; N. Stamenkovic; I. Krstic
2016-01-01
A new approximation to design continuoustime and discrete-time low-pass filters, presented in this paper, based on the product of Gegenbauer polynomials, provides the ability of more flexible adjustment of passband and stopband responses. The design is achieved taking into account a prescribed specification, leading to a better trade-off among the magnitude and group delay responses. Many well-known continuous-time and discrete-time transitional filter based on the classical polynomial approx...
Stabilization of discrete-time LTI positive systems
Directory of Open Access Journals (Sweden)
Krokavec Dušan
2017-12-01
Full Text Available The paper mitigates the existing conditions reported in the previous literature for control design of discrete-time linear positive systems. Incorporating an associated structure of linear matrix inequalities, combined with the Lyapunov inequality guaranteing asymptotic stability of discrete-time positive system structures, new conditions are presented with which the state-feedback controllers and the system state observers can be designed. Associated solutions of the proposed design conditions are illustrated by numerical illustrative examples.
Autonomous learning by simple dynamical systems with a discrete-time formulation
Bilen, Agustín M.; Kaluza, Pablo
2017-05-01
We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.
International Nuclear Information System (INIS)
Chen Ling; Zhao Hongyong
2008-01-01
The paper investigates the almost periodicity of shunting inhibitory cellular neural networks with delays and variable coefficients. Several sufficient conditions are established for the existence and globally exponential stability of almost periodic solutions by employing fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results
International Nuclear Information System (INIS)
Liu Bingwen
2008-01-01
In this Letter, we consider a class of delayed cellular neural networks with time-varying coefficients. By applying Lyapunov functional method and differential inequality techniques, we establish new results to ensure that all solutions of the networks converge exponentially to zero point
New results of almost periodic solutions for cellular neural networks with mixed delays
International Nuclear Information System (INIS)
Zhao Weirui; Zhang Huanshui
2009-01-01
In this paper, for cellular neural networks with mixed delays, we prove some new results on the existence of almost periodic solutions by contraction principle. The global exponential stability of almost periodic solutions is discussed further, and conditions for exponential convergence are given. The conditions we obtained are weaker than the previously known ones and can be easily reduced to several special cases.
Almost sure exponential stability of stochastic fuzzy cellular neural networks with delays
International Nuclear Information System (INIS)
Zhao Hongyong; Ding Nan; Chen Ling
2009-01-01
This paper is concerned with the problem of exponential stability analysis for fuzzy cellular neural network with delays. By constructing suitable Lyapunov functional and using stochastic analysis we present some sufficient conditions ensuring almost sure exponential stability for the network. Moreover, an example is given to demonstrate the advantages of our method.
Filtering and spectral processing of 1-D signals using cellular neural networks
Moreira-Tamayo, O.; Pineda de Gyvez, J.
1996-01-01
This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This
Delay-dependent exponential stability of cellular neural networks with time-varying delays
International Nuclear Information System (INIS)
Zhang Qiang; Wei Xiaopeng; Xu Jin
2005-01-01
The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results
Global exponential stability of cellular neural networks with mixed delays and impulses
International Nuclear Information System (INIS)
Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua
2007-01-01
In this paper cellular neural networks with mixed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality technique. The results of this paper are new and they complement previously known results
International Nuclear Information System (INIS)
Wang Yixuan; Xiong Wanmin; Zhou Qiyuan; Xiao Bing; Yu Yuehua
2006-01-01
In this Letter cellular neural networks with continuously distributed delays and impulses are considered. Sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and differential inequality techniques. The results of this Letter are new and they complement previously known results
Energy Technology Data Exchange (ETDEWEB)
Taraglio, S. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dipt. Innovazione; Zanela, A. [Rome Univ. `La Sapienza` (Italy). Dipt. di Fisica
1997-03-01
An optimization method for some of the CNN`s (Cellular Neural Network) parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN`s literature.
International Nuclear Information System (INIS)
Taraglio, S.; Zanela, A.
1997-03-01
An optimization method for some of the CNN's (Cellular Neural Network) parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN's literature
Neural and Cellular Mechanisms of Fear and Extinction Memory Formation
Orsini, Caitlin A.; Maren, Stephen
2012-01-01
Over the course of natural history, countless animal species have evolved adaptive behavioral systems to cope with dangerous situations and promote survival. Emotional memories are central to these defense systems because they are rapidly acquired and prepare organisms for future threat. Unfortunately, the persistence and intrusion of memories of fearful experiences are quite common and can lead to pathogenic conditions, such as anxiety and phobias. Over the course of the last thirty years, neuroscientists and psychologists alike have attempted to understand the mechanisms by which the brain encodes and maintains these aversive memories. Of equal interest, though, is the neurobiology of extinction memory formation as this may shape current therapeutic techniques. Here we review the extant literature on the neurobiology of fear and extinction memory formation, with a strong focus on the cellular and molecular mechanisms underlying these processes. PMID:22230704
Cellular Neural Network for Real Time Image Processing
International Nuclear Information System (INIS)
Vagliasindi, G.; Arena, P.; Fortuna, L.; Mazzitelli, G.; Murari, A.
2008-01-01
Since their introduction in 1988, Cellular Nonlinear Networks (CNNs) have found a key role as image processing instruments. Thanks to their structure they are able of processing individual pixels in a parallel way providing fast image processing capabilities that has been applied to a wide range of field among which nuclear fusion. In the last years, indeed, visible and infrared video cameras have become more and more important in tokamak fusion experiments for the twofold aim of understanding the physics and monitoring the safety of the operation. Examining the output of these cameras in real-time can provide significant information for plasma control and safety of the machines. The potentiality of CNNs can be exploited to this aim. To demonstrate the feasibility of the approach, CNN image processing has been applied to several tasks both at the Frascati Tokamak Upgrade (FTU) and the Joint European Torus (JET)
Partition-based discrete-time quantum walks
Konno, Norio; Portugal, Renato; Sato, Iwao; Segawa, Etsuo
2018-04-01
We introduce a family of discrete-time quantum walks, called two-partition model, based on two equivalence-class partitions of the computational basis, which establish the notion of local dynamics. This family encompasses most versions of unitary discrete-time quantum walks driven by two local operators studied in literature, such as the coined model, Szegedy's model, and the 2-tessellable staggered model. We also analyze the connection of those models with the two-step coined model, which is driven by the square of the evolution operator of the standard discrete-time coined walk. We prove formally that the two-step coined model, an extension of Szegedy model for multigraphs, and the two-tessellable staggered model are unitarily equivalent. Then, selecting one specific model among those families is a matter of taste not generality.
Discrete-Time Filter Synthesis using Product of Gegenbauer Polynomials
Directory of Open Access Journals (Sweden)
N. Stojanovic
2016-09-01
Full Text Available A new approximation to design continuoustime and discrete-time low-pass filters, presented in this paper, based on the product of Gegenbauer polynomials, provides the ability of more flexible adjustment of passband and stopband responses. The design is achieved taking into account a prescribed specification, leading to a better trade-off among the magnitude and group delay responses. Many well-known continuous-time and discrete-time transitional filter based on the classical polynomial approximations(Chebyshev, Legendre, Butterworth are shown to be a special cases of proposed approximation method.
Engineering applications of discrete-time optimal control
DEFF Research Database (Denmark)
Vidal, Rene Victor Valqui; Ravn, Hans V.
1990-01-01
Many problems of design and operation of engineering systems can be formulated as optimal control problems where time has been discretisized. This is also true even if 'time' is not involved in the formulation of the problem, but rather another one-dimensional parameter. This paper gives a review...... of some well-known and new results in discrete time optimal control methods applicable to practical problem solving within engineering. Emphasis is placed on dynamic programming, the classical maximum principle and generalized versions of the maximum principle for optimal control of discrete time systems...
One-way hash function based on hyper-chaotic cellular neural network
International Nuclear Information System (INIS)
Yang Qunting; Gao Tiegang
2008-01-01
The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge–Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability. (general)
Invariant set computation for constrained uncertain discrete-time systems
Athanasopoulos, N.; Bitsoris, G.
2010-01-01
In this article a novel approach to the determination of polytopic invariant sets for constrained discrete-time linear uncertain systems is presented. First, the problem of stabilizing a prespecified initial condition set in the presence of input and state constraints is addressed. Second, the
Discrete time process algebra and the semantics of SDL
J.A. Bergstra; C.A. Middelburg; Y.S. Usenko (Yaroslav)
1998-01-01
htmlabstractWe present an extension of discrete time process algebra with relative timing where recursion, propositional signals and conditions, a counting process creation operator, and the state operator are combined. Except the counting process creation operator, which subsumes the original
Cryptanalyzing a discrete-time chaos synchronization secure communication system
International Nuclear Information System (INIS)
Alvarez, G.; Montoya, F.; Romera, M.; Pastor, G.
2004-01-01
This paper describes the security weakness of a recently proposed secure communication method based on discrete-time chaos synchronization. We show that the security is compromised even without precise knowledge of the chaotic system used. We also make many suggestions to improve its security in future versions
Recursive smoothers for hidden discrete-time Markov chains
Directory of Open Access Journals (Sweden)
Lakhdar Aggoun
2005-01-01
Full Text Available We consider a discrete-time Markov chain observed through another Markov chain. The proposed model extends models discussed by Elliott et al. (1995. We propose improved recursive formulae to update smoothed estimates of processes related to the model. These recursive estimates are used to update the parameter of the model via the expectation maximization (EM algorithm.
Nonparametric volatility density estimation for discrete time models
Es, van Bert; Spreij, P.J.C.; Zanten, van J.H.
2005-01-01
We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed
Stable cycling in discrete-time genetic models.
Hastings, A
1981-01-01
Examples of stable cycling are discussed for two-locus, two-allele, deterministic, discrete-time models with constant fitnesses. The cases that cycle were found by using numerical techniques to search for stable Hopf bifurcations. One consequence of the results is that apparent cases of directional selection may be due to stable cycling.
Stable cycling in discrete-time genetic models.
Hastings, A
1981-11-01
Examples of stable cycling are discussed for two-locus, two-allele, deterministic, discrete-time models with constant fitnesses. The cases that cycle were found by using numerical techniques to search for stable Hopf bifurcations. One consequence of the results is that apparent cases of directional selection may be due to stable cycling.
International Nuclear Information System (INIS)
Park, Ju H.
2007-01-01
This paper considers the robust stability analysis of cellular neural networks with discrete and distributed delays. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, a novel stability criterion guaranteeing the global robust convergence of the equilibrium point is derived. The criterion can be solved easily by various convex optimization algorithms. An example is given to illustrate the usefulness of our results
Almost periodic cellular neural networks with neutral-type proportional delays
Xiao, Songlin
2018-03-01
This paper presents a new result on the existence, uniqueness and generalised exponential stability of almost periodic solutions for cellular neural networks with neutral-type proportional delays and D operator. Based on some novel differential inequality techniques, a testable condition is derived to ensure that all the state trajectories of the system converge to an almost periodic solution with a positive exponential convergence rate. The effectiveness of the obtained result is illustrated by a numerical example.
Dynamics of a class of cellular neural networks with time-varying delays
International Nuclear Information System (INIS)
Huang Lihong; Huang Chuangxia; Liu Bingwen
2005-01-01
Employing Brouwer's fixed point theorem, matrix theory, a continuation theorem of the coincidence degree and inequality analysis, the authors make a further investigation of a class of cellular neural networks with delays (DCNNs) in this Letter. A family of sufficient conditions are given for checking global exponential stability and the existence of periodic solutions of DCNNs. These results have important leading significance in the design and applications of globally stable DCNNs and periodic oscillatory DCNNs. Our results extend and improve some earlier publications
A new method of machine vision reprocessing based on cellular neural networks
International Nuclear Information System (INIS)
Jianhua, W.; Liping, Z.; Fenfang, Z.; Guojian, H.
1996-01-01
This paper proposed a method of image preprocessing in machine vision based on Cellular Neural Network (CNN). CNN is introduced to design image smoothing, image recovering, image boundary detecting and other image preprocessing problems. The proposed methods are so simple that the speed of algorithms are increased greatly to suit the needs of real-time image processing. The experimental results show a satisfactory reply
Discrete time-crystalline order in black diamond
Zhou, Hengyun; Choi, Soonwon; Choi, Joonhee; Landig, Renate; Kucsko, Georg; Isoya, Junichi; Jelezko, Fedor; Onoda, Shinobu; Sumiya, Hitoshi; Khemani, Vedika; von Keyserlingk, Curt; Yao, Norman; Demler, Eugene; Lukin, Mikhail D.
2017-04-01
The interplay of periodic driving, disorder, and strong interactions has recently been predicted to result in exotic ``time-crystalline'' phases, which spontaneously break the discrete time-translation symmetry of the underlying drive. Here, we report the experimental observation of such discrete time-crystalline order in a driven, disordered ensemble of 106 dipolar spin impurities in diamond at room-temperature. We observe long-lived temporal correlations at integer multiples of the fundamental driving period, experimentally identify the phase boundary and find that the temporal order is protected by strong interactions; this order is remarkably stable against perturbations, even in the presence of slow thermalization. Our work opens the door to exploring dynamical phases of matter and controlling interacting, disordered many-body systems.
A discrete-time adaptive control scheme for robot manipulators
Tarokh, M.
1990-01-01
A discrete-time model reference adaptive control scheme is developed for trajectory tracking of robot manipulators. The scheme utilizes feedback, feedforward, and auxiliary signals, obtained from joint angle measurement through simple expressions. Hyperstability theory is utilized to derive the adaptation laws for the controller gain matrices. It is shown that trajectory tracking is achieved despite gross robot parameter variation and uncertainties. The method offers considerable design flexibility and enables the designer to improve the performance of the control system by adjusting free design parameters. The discrete-time adaptation algorithm is extremely simple and is therefore suitable for real-time implementation. Simulations and experimental results are given to demonstrate the performance of the scheme.
Parrondo's game using a discrete-time quantum walk
International Nuclear Information System (INIS)
Chandrashekar, C.M.; Banerjee, Subhashish
2011-01-01
We present a new form of a Parrondo game using discrete-time quantum walk on a line. The two players A and B with different quantum coins operators, individually losing the game can develop a strategy to emerge as joint winners by using their coins alternatively, or in combination for each step of the quantum walk evolution. We also present a strategy for a player A (B) to have a winning probability more than player B (A). Significance of the game strategy in information theory and physical applications are also discussed. - Highlights: → Novel form of Parrondo's game on a single particle discrete-time quantum walk. → Strategies for players to emerge as individual winners or as joint winners. → General framework for controlling and using quantum walk with multiple coins. → Strategies can be used in algorithms and situations involving directed motion.
Direct output feedback control of discrete-time systems
International Nuclear Information System (INIS)
Lin, C.C.; Chung, L.L.; Lu, K.H.
1993-01-01
An optimal direct output feedback control algorithm is developed for discrete-time systems with the consideration of time delay in control force action. Optimal constant output feedback gains are obtained through variational process such that certain prescribed quadratic performance index is minimized. Discrete-time control forces are then calculated from the multiplication of output measurements by these pre-calculated feedback gains. According to the proposed algorithm, structural system is assured to remain stable even in the presence of time delay. The number of sensors and controllers may be very small as compared with the dimension of states. Numerical results show that direct velocity feedback control is more sensitive to time delay than state feedback but, is still quite effective in reducing the dynamic responses under earthquake excitation. (author)
Discrete-Time LPV Current Control of an Induction Motor
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2003-01-01
In this paper we apply a new method for gain-scheduled output feedback control of nonlinear systems to current control of an induction motor. The method relies on recently developed controller synthesis results for linear parameter-varying (LPV) systems, where the controller synthesis is formulated...... as a set of linear matrix inequalities with full-block multipliers. A standard nonlinear model of the motor is constructed and written on LPV form. We then show that, although originally developed in continuous time, the controller synthesis results can be applied to a discrete-time model as well without...... further complications. The synthesis method is applied to the model, yielding an LPV discrete-time controller. Finally, the efficiency of the control scheme is validated via simulations as well as on the actual induction motor, both in open-loop current control and when an outer speed control loop...
Disease Extinction Versus Persistence in Discrete-Time Epidemic Models.
van den Driessche, P; Yakubu, Abdul-Aziz
2018-04-12
We focus on discrete-time infectious disease models in populations that are governed by constant, geometric, Beverton-Holt or Ricker demographic equations, and give a method for computing the basic reproduction number, [Formula: see text]. When [Formula: see text] and the demographic population dynamics are asymptotically constant or under geometric growth (non-oscillatory), we prove global asymptotic stability of the disease-free equilibrium of the disease models. Under the same demographic assumption, when [Formula: see text], we prove uniform persistence of the disease. We apply our theoretical results to specific discrete-time epidemic models that are formulated for SEIR infections, cholera in humans and anthrax in animals. Our simulations show that a unique endemic equilibrium of each of the three specific disease models is asymptotically stable whenever [Formula: see text].
Discrete-Time Nonlinear Control of VSC-HVDC System
Directory of Open Access Journals (Sweden)
TianTian Qian
2015-01-01
Full Text Available Because VSC-HVDC is a kind of strong nonlinear, coupling, and multi-input multioutput (MIMO system, its control problem is always attracting much attention from scholars. And a lot of papers have done research on its control strategy in the continuous-time domain. But the control system is implemented through the computer discrete sampling in practical engineering. It is necessary to study the mathematical model and control algorithm in the discrete-time domain. The discrete mathematical model based on output feedback linearization and discrete sliding mode control algorithm is proposed in this paper. And to ensure the effectiveness of the control system in the quasi sliding mode state, the fast output sampling method is used in the output feedback. The results from simulation experiment in MATLAB/SIMULINK prove that the proposed discrete control algorithm can make the VSC-HVDC system have good static, dynamic, and robust characteristics in discrete-time domain.
Zak Phase in Discrete-Time Quantum Walks
Puentes, G.; Santillán, O.
2015-01-01
We report on a simple scheme that may present a non-trivial geometric Zak phase ($\\Phi_{Zak}$) structure, which is based on a discrete-time quantum walk architecture. By detecting the Zak phase difference between two trajectories connecting adjacent Dirac points where the quasi-energy gap closes for opposite values of quasi-momentum ($k$), it is possible to identify geometric invariants. These geometric invariants correspond to $|\\Phi_{Zak}^{+(-)}-\\Phi_{Zak}^{-(+)}|=\\pi$ and $|\\Phi_{Zak}^{+(-...
Discrete-time control system design with applications
Rabbath, C A
2014-01-01
This book presents practical techniques of discrete-time control system design. In general, the design techniques lead to low-order dynamic compensators that ensure satisfactory closed-loop performance for a wide range of sampling rates. The theory is given in the form of theorems, lemmas, and propositions. The design of the control systems is presented as step-by-step procedures and algorithms. The proposed feedback control schemes are applied to well-known dynamic system models. This book also discusses: Closed-loop performance of generic models of mobile robot and airborne pursuer dynamic systems under discrete-time feedback control with limited computing capabilities Concepts of discrete-time models and sampled-data models of continuous-time systems, for both single- and dual-rate operation Local versus global digital redesign Optimal, closed-loop digital redesign methods Plant input mapping design Generalized holds and samplers for use in feedback control loops, Numerical simulation of fixed-point arithm...
Exponential stability of fuzzy cellular neural networks with constant and time-varying delays
International Nuclear Information System (INIS)
Liu Yanqing; Tang Wansheng
2004-01-01
In this Letter, the global stability of delayed fuzzy cellular neural networks (FCNN) with either constant delays or time varying delays is proposed. Firstly, we give the existence and uniqueness of the equilibrium point by using the theory of topological degree and the properties of nonsingular M-matrix and the sufficient conditions for ascertaining the global exponential stability by constructing a suitable Lyapunov functional. Secondly, the criteria for guaranteeing the global exponential stability of FCNN with time varying delays are given and the estimation of exponential convergence rate with regard to speed of vary of delays is presented by constructing a suitable Lyapunov functional
Global stability and existence of periodic solutions of discrete delayed cellular neural networks
International Nuclear Information System (INIS)
Li Yongkun
2004-01-01
We use the continuation theorem of coincidence degree theory and Lyapunov functions to study the existence and stability of periodic solutions for the discrete cellular neural networks (CNNs) with delays xi(n+1)=xi(n)e-bi(n)h+θi(h)-bar j=1maij(n)fj(xj(n))+θi(h)-bar j=1mbij(n)fj(xj(n- τij(n)))+θi(h)Ii(n),i=1,2,...,m. We obtain some sufficient conditions to ensure that for the networks there exists a unique periodic solution, and all its solutions converge to such a periodic solution
Naqvi, Syed Rameez; Akram, Tallha; Iqbal, Saba; Haider, Sajjad Ali; Kamran, Muhammad; Muhammad, Nazeer
2018-02-01
Considering the lack of optimization support for Quantum-dot Cellular Automata, we propose a dynamically reconfigurable logic cell capable of implementing various logic operations by means of artificial neural networks. The cell can be reconfigured to any 2-input combinational logic gate by altering the strength of connections, called weights and biases. We demonstrate how these cells may appositely be organized to perform multi-bit arithmetic and logic operations. The proposed work is important in that it gives a standard implementation of an 8-bit arithmetic and logic unit for quantum-dot cellular automata with minimal area and latency overhead. We also compare the proposed design with a few existing arithmetic and logic units, and show that it is more area efficient than any equivalent available in literature. Furthermore, the design is adaptable to 16, 32, and 64 bit architectures.
Mapping of uncertainty relations between continuous and discrete time.
Chiuchiù, Davide; Pigolotti, Simone
2018-03-01
Lower bounds on fluctuations of thermodynamic currents depend on the nature of time, discrete or continuous. To understand the physical reason, we compare current fluctuations in discrete-time Markov chains and continuous-time master equations. We prove that current fluctuations in the master equations are always more likely, due to random timings of transitions. This comparison leads to a mapping of the moments of a current between discrete and continuous time. We exploit this mapping to obtain uncertainty bounds. Our results reduce the quests for uncertainty bounds in discrete and continuous time to a single problem.
Global consensus for discrete-time competitive systems
International Nuclear Information System (INIS)
Shih, C.-W.; Tseng, J.-P.
2009-01-01
Grossberg established a remarkable convergence theorem for a class of competitive systems without knowing and using Lyapunov function for the systems. We present the parallel investigations for the discrete-time version of the Grossberg's model. Through developing an extended component-competing analysis for the coupled system, without knowing a Lyapunov function and applying the LaSalle's invariance principle, the global pattern formation or the so-called global consensus for the system can be achieved. A numerical simulation is performed to illustrate the present theory.
Robust performance results for discrete-time systems
Directory of Open Access Journals (Sweden)
Mahmoud Magdi S.
1997-01-01
Full Text Available The problems of robust performance and feedback control synthesis for a class of linear discrete-time systems with time-varying parametric uncertainties are addressed in this paper. The uncertainties are bound and have a linear matrix fractional form. Based on the concept of strongly robust H ∞ -performance criterion, results of robust stability and performance are developed and expressed in easily computable linear matrix inequalities. Synthesis of robust feedback controllers is carried out for several system models of interest.
Sampling rare fluctuations of discrete-time Markov chains
Whitelam, Stephen
2018-03-01
We describe a simple method that can be used to sample the rare fluctuations of discrete-time Markov chains. We focus on the case of Markov chains with well-defined steady-state measures, and derive expressions for the large-deviation rate functions (and upper bounds on such functions) for dynamical quantities extensive in the length of the Markov chain. We illustrate the method using a series of simple examples, and use it to study the fluctuations of a lattice-based model of active matter that can undergo motility-induced phase separation.
Optimal Robust Fault Detection for Linear Discrete Time Systems
Directory of Open Access Journals (Sweden)
Nike Liu
2008-01-01
Full Text Available This paper considers robust fault-detection problems for linear discrete time systems. It is shown that the optimal robust detection filters for several well-recognized robust fault-detection problems, such as ℋ−/ℋ∞, ℋ2/ℋ∞, and ℋ∞/ℋ∞ problems, are the same and can be obtained by solving a standard algebraic Riccati equation. Optimal filters are also derived for many other optimization criteria and it is shown that some well-studied and seeming-sensible optimization criteria for fault-detection filter design could lead to (optimal but useless fault-detection filters.
Discrete-Time LPV Current Control of an Induction Motor
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2001-01-01
In this paper we apply a new method for gain-scheduled output feedback control of nonlinear systems to current control of an induction motor. The method relies on recently developed controller synthesis results for linear parameter-varying (LPV) systems, where the controller synthesis is formulated...... without further complications. The synthesis method is applied to the model, yielding an LPV discrete-time controller. Finally, the efficiency of the control scheme is validated via simulations as well as experimentally on the actual induction motor, both in open-loop current control and when an outer...... speed control loop is closed around the current loop...
Discrete-Time LPV Current Control of an Induction Motor
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Trangbæk, Klaus
2003-01-01
In this paper we apply a new method for gain-scheduled output feedback control of nonlinear systems to current control of an induction motor. The method relies on recently developed controller synthesis results for linear parameter-varying (LPV) systems, where the controller synthesis is formulated...... further complications. The synthesis method is applied to the model, yielding an LPV discrete-time controller. Finally, the efficiency of the control scheme is validated via simulations as well as on the actual induction motor, both in open-loop current control and when an outer speed control loop...... is closed around the current loop....
A parametric LTR solution for discrete-time systems
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Jannerup, Ole Erik
1989-01-01
A parametric LTR (loop transfer recovery) solution for discrete-time compensators incorporating filtering observers which achieve exact recovery is presented for both minimum- and non-minimum-phase systems. First the recovery error, which defines the difference between the target loop transfer...... and the full loop transfer function, is manipulated into a general form involving the target loop transfer matrix and the fundamental recovery matrix. A parametric LTR solution based on the recovery matrix is developed. It is shown that the LQR/LTR (linear quadratic Gaussian/loop transfer recovery) solution...
Frequency interval balanced truncation of discrete-time bilinear systems
DEFF Research Database (Denmark)
Jazlan, Ahmad; Sreeram, Victor; Shaker, Hamid Reza
2016-01-01
This paper presents the development of a new model reduction method for discrete-time bilinear systems based on the balanced truncation framework. In many model reduction applications, it is advantageous to analyze the characteristics of the system with emphasis on particular frequency intervals...... are the solution to a pair of new generalized Lyapunov equations. The conditions for solvability of these new generalized Lyapunov equations are derived and a numerical solution method for solving these generalized Lyapunov equations is presented. Numerical examples which illustrate the usage of the new...... generalized frequency interval controllability and observability gramians as part of the balanced truncation framework are provided to demonstrate the performance of the proposed method....
Hopf Bifurcation in a Cobweb Model with Discrete Time Delays
Directory of Open Access Journals (Sweden)
Luca Gori
2014-01-01
Full Text Available We develop a cobweb model with discrete time delays that characterise the length of production cycle. We assume a market comprised of homogeneous producers that operate as adapters by taking the (expected profit-maximising quantity as a target to adjust production and consumers with a marginal willingness to pay captured by an isoelastic demand. The dynamics of the economy is characterised by a one-dimensional delay differential equation. In this context, we show that (1 if the elasticity of market demand is sufficiently high, the steady-state equilibrium is locally asymptotically stable and (2 if the elasticity of market demand is sufficiently low, quasiperiodic oscillations emerge when the time lag (that represents the length of production cycle is high enough.
Neutrino oscillations in discrete-time quantum walk framework
Energy Technology Data Exchange (ETDEWEB)
Mallick, Arindam; Mandal, Sanjoy; Chandrashekar, C.M. [C. I. T. Campus, The Institute of Mathematical Sciences, Chennai (India); Homi Bhabha National Institute, Training School Complex, Mumbai (India)
2017-02-15
Here we present neutrino oscillation in the framework of quantum walks. Starting from a one spatial dimensional discrete-time quantum walk we present a scheme of evolutions that will simulate neutrino oscillation. The set of quantum walk parameters which is required to reproduce the oscillation probability profile obtained in both, long range and short range neutrino experiment is explicitly presented. Our scheme to simulate three-generation neutrino oscillation from quantum walk evolution operators can be physically realized in any low energy experimental set-up with access to control a single six-level system, a multiparticle three-qubit or a qubit-qutrit system. We also present the entanglement between spins and position space, during neutrino propagation that will quantify the wave function delocalization around instantaneous average position of the neutrino. This work will contribute towards understanding neutrino oscillation in the framework of the quantum information perspective. (orig.)
Formal methods for discrete-time dynamical systems
Belta, Calin; Aydin Gol, Ebru
2017-01-01
This book bridges fundamental gaps between control theory and formal methods. Although it focuses on discrete-time linear and piecewise affine systems, it also provides general frameworks for abstraction, analysis, and control of more general models. The book is self-contained, and while some mathematical knowledge is necessary, readers are not expected to have a background in formal methods or control theory. It rigorously defines concepts from formal methods, such as transition systems, temporal logics, model checking and synthesis. It then links these to the infinite state dynamical systems through abstractions that are intuitive and only require basic convex-analysis and control-theory terminology, which is provided in the appendix. Several examples and illustrations help readers understand and visualize the concepts introduced throughout the book.
Coordination Frictions and Job Heterogeneity: A Discrete Time Analysis
DEFF Research Database (Denmark)
Kennes, John; Le Maire, Christian Daniel
This paper develops and extends a dynamic, discrete time, job to worker matching model in which jobs are heterogeneous in equilibrium. The key assumptions of this economic environment are (i) matching is directed and (ii) coordination frictions lead to heterogeneous local labor markets. We de- rive...... a number of new theoretical results, which are essential for the empirical application of this type of model to matched employer-employee microdata. First, we o¤er a robust equilibrium concept in which there is a continu- ous dispersion of job productivities and wages. Second, we show that our model can...... of these results preserve the essential tractability of the baseline model with aggregate shocks. Therefore, we o¤er a parsimonious, general equilibrium framework in which to study the process by which the contin- uous dispersion of wages and productivities varies over the business cycle for a large population...
Adaptive Control and Function Projective Synchronization in 2D Discrete-Time Chaotic Systems
International Nuclear Information System (INIS)
Li Yin; Chen Yong; Li Biao
2009-01-01
This study addresses the adaptive control and function projective synchronization problems between 2D Rulkov discrete-time system and Network discrete-time system. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate the function projective synchronization of discrete-time chaotic systems. In addition, the adaptive control function is applied to achieve the state synchronization of two discrete-time systems. Numerical results demonstrate the effectiveness of the proposed control scheme.
International Nuclear Information System (INIS)
Wang Xiaohu; Xu Daoyi
2009-01-01
In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.
Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks
Directory of Open Access Journals (Sweden)
Mauro Di Marco
2009-01-01
Full Text Available In several relevant applications to the solution of signal processing tasks in real time, a cellular neural network (CNN is required to be convergent, that is, each solution should tend toward some equilibrium point. The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's invariance principle, for studying convergence and stability of the differential inclusions modeling the dynamics of the full-range (FR model of CNNs. The applicability of the method is demonstrated by obtaining a rigorous proof of convergence for symmetric FR-CNNs. The proof, which is a direct consequence of the fact that a symmetric FR-CNN admits a strict Lyapunov function, is much more simple than the corresponding proof of convergence for symmetric standard CNNs.
Convergence and attractivity of memristor-based cellular neural networks with time delays.
Qin, Sitian; Wang, Jun; Xue, Xiaoping
2015-03-01
This paper presents theoretical results on the convergence and attractivity of memristor-based cellular neural networks (MCNNs) with time delays. Based on a realistic memristor model, an MCNN is modeled using a differential inclusion. The essential boundedness of its global solutions is proven. The state of MCNNs is further proven to be convergent to a critical-point set located in saturated region of the activation function, when the initial state locates in a saturated region. It is shown that the state convergence time period is finite and can be quantitatively estimated using given parameters. Furthermore, the positive invariance and attractivity of state in non-saturated regions are also proven. The simulation results of several numerical examples are provided to substantiate the results. Copyright © 2014 Elsevier Ltd. All rights reserved.
Global detection of live virtual machine migration based on cellular neural networks.
Xie, Kang; Yang, Yixian; Zhang, Ling; Jing, Maohua; Xin, Yang; Li, Zhongxian
2014-01-01
In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM) migration detection algorithm based on the cellular neural networks (CNNs), is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI) implementation allowing the VM migration detection to be performed better.
Global Detection of Live Virtual Machine Migration Based on Cellular Neural Networks
Directory of Open Access Journals (Sweden)
Kang Xie
2014-01-01
Full Text Available In order to meet the demands of operation monitoring of large scale, autoscaling, and heterogeneous virtual resources in the existing cloud computing, a new method of live virtual machine (VM migration detection algorithm based on the cellular neural networks (CNNs, is presented. Through analyzing the detection process, the parameter relationship of CNN is mapped as an optimization problem, in which improved particle swarm optimization algorithm based on bubble sort is used to solve the problem. Experimental results demonstrate that the proposed method can display the VM migration processing intuitively. Compared with the best fit heuristic algorithm, this approach reduces the processing time, and emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation allowing the VM migration detection to be performed better.
Application of cellular neural network (CNN) method to the nuclear reactor dynamics equations
International Nuclear Information System (INIS)
Hadad, K.; Piroozmand, A.
2007-01-01
This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the nuclear reactor dynamic equations. An equivalent electrical circuit is analyzed and the governing equations of a bare, homogeneous reactor core are modeled via CNN. The validity of the CNN result is compared with numerical solution of the system of nonlinear governing partial differential equations (PDE) using MATLAB. Steady state as well as transient simulations, show very good comparison between the two methods. We used our CNN model to simulate space-time response of different reactivity excursions in a typical nuclear reactor. On line solution of reactor dynamic equations is used as an aid to reactor operation decision making. The complete algorithm could also be implemented using very large scale integrated circuit (VLSI) circuitry. The efficiency of the calculation method makes it useful for small size nuclear reactors such as the ones used in space missions
A multi-scale convolutional neural network for phenotyping high-content cellular images.
Godinez, William J; Hossain, Imtiaz; Lazic, Stanley E; Davies, John W; Zhang, Xian
2017-07-01
Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs. The network specifications and solver definitions are provided in Supplementary Software 1. william_jose.godinez_navarro@novartis.com or xian-1.zhang@novartis.com. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
Directory of Open Access Journals (Sweden)
Dirk M. Hermann
2014-09-01
Full Text Available After an ischemic stroke, neural precursor cells (NPCs proliferate within major germinal niches of the brain. Endogenous NPCs subsequently migrate towards the ischemic lesion where they promote tissue remodelling and neural repair. Unfortunately, this restorative process is generally insufficient and thus unable to support a full recovery of lost neurological functions. Supported by solid experimental and preclinical data, the transplantation of exogenous NPCs has emerged as a potential tool for stroke treatment. Transplanted NPCs are thought to act mainly via trophic and immune modulatory effects, thereby complementing the restorative responses initially executed by the endogenous NPC population. Recent studies have attempted to elucidate how the therapeutic properties of transplanted NPCs vary depending on the route of transplantation. Systemic NPC delivery leads to potent immune modulatory actions, which prevent secondary neuronal degeneration, reduces glial scar formation, diminishes oxidative stress and stabilizes blood-brain barrier integrity. On the contrary, local stem cell delivery, allows for the accumulation of large numbers of transplanted NPCs in the brain, thus achieving high levels of locally available tissue trophic factors, which may better induce a strong endogenous NPC proliferative response.Herein we describe the diverse capabilities of exogenous (systemically vs locally transplanted NPCs in enhancing the endogenous neurogenic response after stroke, and how the route of transplantation may affect migration, survival, bystander effects and integration of the cellular graft. It is the authors’ claim that understanding these aspects will be of pivotal importance in discerning how transplanted NPCs exert their therapeutic effects in stroke.
Constant pressure and temperature discrete-time Langevin molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Grønbech-Jensen, Niels [Department of Mechanical and Aerospace Engineering, University of California, Davis, California 95616 (United States); Department of Mathematics, University of California, Davis, California 95616 (United States); Farago, Oded [Department of Biomedical Engineering, Ben Gurion University of the Negev, Be' er Sheva 84105 (Israel); Ilse Katz Institute for Nanoscale Science and Technology, Ben Gurion University of the Negev, Be' er Sheva 84105 (Israel)
2014-11-21
We present a new and improved method for simultaneous control of temperature and pressure in molecular dynamics simulations with periodic boundary conditions. The thermostat-barostat equations are built on our previously developed stochastic thermostat, which has been shown to provide correct statistical configurational sampling for any time step that yields stable trajectories. Here, we extend the method and develop a set of discrete-time equations of motion for both particle dynamics and system volume in order to seek pressure control that is insensitive to the choice of the numerical time step. The resulting method is simple, practical, and efficient. The method is demonstrated through direct numerical simulations of two characteristic model systems—a one-dimensional particle chain for which exact statistical results can be obtained and used as benchmarks, and a three-dimensional system of Lennard-Jones interacting particles simulated in both solid and liquid phases. The results, which are compared against the method of Kolb and Dünweg [J. Chem. Phys. 111, 4453 (1999)], show that the new method behaves according to the objective, namely that acquired statistical averages and fluctuations of configurational measures are accurate and robust against the chosen time step applied to the simulation.
Advanced discrete-time control designs and applications
Abidi, Khalid
2015-01-01
This book covers a wide spectrum of systems such as linear and nonlinear multivariable systems as well as control problems such as disturbance, uncertainty and time-delays. The purpose of this book is to provide researchers and practitioners a manual for the design and application of advanced discrete-time controllers. The book presents six different control approaches depending on the type of system and control problem. The first and second approaches are based on Sliding Mode control (SMC) theory and are intended for linear systems with exogenous disturbances. The third and fourth approaches are based on adaptive control theory and are aimed at linear/nonlinear systems with periodically varying parametric uncertainty or systems with input delay. The fifth approach is based on Iterative learning control (ILC) theory and is aimed at uncertain linear/nonlinear systems with repeatable tasks and the final approach is based on fuzzy logic control (FLC) and is intended for highly uncertain systems with heuristi...
Multiple Estimation Architecture in Discrete-Time Adaptive Mixing Control
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Simone Baldi
2013-05-01
Full Text Available Adaptive mixing control (AMC is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient performance might be sensitive to the initial conditions of the parameter estimator. In particular, for some initial conditions, transient oscillations may not be acceptable in practical applications. In order to account for such a possible phenomenon and to improve the learning capability of the adaptive scheme, in this paper a new mixing architecture is developed, involving the use of parallel parameter estimators, or multi-estimators, each one working on a small subset of the uncertainty set. A supervisory logic, using performance signals based on the past and present estimation error, selects the parameter estimate to determine the mixing of the controllers. The stability and robustness properties of the resulting approach, referred to as multi-estimator adaptive mixing control (Multi-AMC, are analytically established. Besides, extensive simulations demonstrate that the scheme improves the transient performance of the original AMC with a single estimator. The control scheme and the analysis are carried out in a discrete-time framework, for easier implementation of the method in digital control.
Hopf Bifurcation Analysis for a Stochastic Discrete-Time Hyperchaotic System
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Jie Ran
2015-01-01
Full Text Available The dynamics of a discrete-time hyperchaotic system and the amplitude control of Hopf bifurcation for a stochastic discrete-time hyperchaotic system are investigated in this paper. Numerical simulations are presented to exhibit the complex dynamical behaviors in the discrete-time hyperchaotic system. Furthermore, the stochastic discrete-time hyperchaotic system with random parameters is transformed into its equivalent deterministic system with the orthogonal polynomial theory of discrete random function. In addition, the dynamical features of the discrete-time hyperchaotic system with random disturbances are obtained through its equivalent deterministic system. By using the Hopf bifurcation conditions of the deterministic discrete-time system, the specific conditions for the existence of Hopf bifurcation in the equivalent deterministic system are derived. And the amplitude control with random intensity is discussed in detail. Finally, the feasibility of the control method is demonstrated by numerical simulations.
Tectonic modeling of Konya-Beysehir Region (Turkey using cellular neural networks
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D. Aydogan
2007-06-01
Full Text Available In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we applied the Cellular Neural Network (CNN approach to gravity and magnetic anomaly maps. CNN is a stochastic image processing technique, based optimization of templates, which imply relationships of neighborhood pixels in 2-Dimensional (2D potential anomalies. Here, CNN performance in geophysics, tested by various synthetic examples and the results are compared to classical methods such as boundary analysis and second vertical derivatives. After we obtained satisfactory results in synthetic models, we applied CNN to Bouguer anomaly map of Konya-Beysehir Region, which has complex tectonic structure with various fault combinations. We evaluated CNN outputs and 2D/3D models, which are constructed using forward and inversion methods. Then we presented a new tectonic structure of Konya-Beysehir Region. We have denoted (F1, F2, , F7 and (Konya1, Konya2 faults according to our evaluations of CNN outputs. Thus, we have concluded that CNN is a compromising stochastic image processing technique in geophysics.
Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere
2015-04-01
This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation.
Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian
2013-11-30
Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.
Cellular neural networks, the Navier-Stokes equation, and microarray image reconstruction.
Zineddin, Bachar; Wang, Zidong; Liu, Xiaohui
2011-11-01
Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.
Meng, Shukai; Mo, Yu L.
2001-09-01
Image segmentation is one of the most important operations in many image analysis problems, which is the process that subdivides an image into its constituents and extracts those parts of interest. In this paper, we present a new second order difference gray-scale image segmentation algorithm based on cellular neural networks. A 3x3 CNN cloning template is applied, which can make smooth processing and has a good ability to deal with the conflict between the capability of noise resistance and the edge detection of complex shapes. We use second order difference operator to calculate the coefficients of the control template, which are not constant but rather depend on the input gray-scale values. It is similar to Contour Extraction CNN in construction, but there are some different in algorithm. The result of experiment shows that the second order difference CNN has a good capability in edge detection. It is better than Contour Extraction CNN in detail detection and more effective than the Laplacian of Gauss (LOG) algorithm.
Cellular Neural Network Method for Critical Slab with Albedo Boundary Condition
International Nuclear Information System (INIS)
Pirouzmanda, A.; Hadada, K.; Suh, K. Y.
2010-01-01
The neutron transport problems have been studied theoretically and numerically for years. A number of researchers have studied the criticality problems of one-speed neutrons in homogeneous slabs and spheres using various methods. The Chebyshev polynomial approximation method (T N method) has lately been developed and improved for the neutron transport equation in slab geometry. The one-speed time-dependent neutron transport equation using the Cellular Neural Network (CNN) for the vacuum boundary condition has previously been solved. In this paper, we demonstrate the capacity of CNN in calculating the critical slab thickness for different boundary conditions and its variation with moments N. The architecture of the CNN has already been dealt with thoroughly. Essentially, the CNN is used to model a first-order system of the partial differential equations (PDEs). The original equations in the T N approximation are also a set of PDEs. The CNN approach lends itself to analog VLSI implementation. In this study, the CNN model is implemented using the HSpice software package
Equilibrium and response properties of the integrate-and-fire neuron in discrete time
Directory of Open Access Journals (Sweden)
Moritz Helias
2010-01-01
Full Text Available The integrate-and-fire neuron with exponential postsynaptic potentials is a frequently employed model to study neural networks. Simulations in discrete time still have highest performance at moderate numerical errors, which makes them first choice for long-term simulations of plastic networks. Here we extend the population density approach to investigate how the equilibrium and response properties of the leaky integrate-and-fire neuron are affected by time discretization. We present a novel analytical treatment of the boundary condition at threshold, taking both discretization of time and finite synaptic weights into account. We uncover an increased membrane potential density just below threshold as the decisive property that explains the deviations found between simulations and the classical diffusion approximation. Temporal discretization and finite synaptic weights both contribute to this effect. Our treatment improves the standard formula to calculate the neuron’s equilibrium firing rate. Direct solution of the Markov process describing the evolution of the membrane potential density confirms our analysis and yields a method to calculate the firing rate exactly. Knowing the shape of the membrane potential distribution near threshold enables us to devise the transient response properties of the neuron model to synaptic input. We find a pronounced non-linear fast response component that has not been described by the prevailing continuous time theory for Gaussian white noise input.
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.
Wei, Qinglai; Liu, Derong; Lin, Hanquan
2016-03-01
In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.
Kiumarsi, Bahare; Lewis, Frank L
2015-01-01
This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.
Performance analysis of chi models using discrete-time probabilistic reward graphs
Trcka, N.; Georgievska, S.; Markovski, J.; Andova, S.; Vink, de E.P.
2008-01-01
We propose the model of discrete-time probabilistic reward graphs (DTPRGs) for performance analysis of systems exhibiting discrete deterministic time delays and probabilistic behavior, via their interpretation as discrete-time Markov reward chains, full-fledged platform for qualitative and
Yan, Yuanwei; Bejoy, Julie; Xia, Junfei; Guan, Jingjiao; Zhou, Yi; Li, Yan
2016-09-15
different neuronal subtypes in 3-D differentiation from hiPSCs and the differential cellular responses of region-specific neuronal subtypes to various biomolecules have not been fully investigated. By tuning neural patterning of hiPSCs with small molecules targeting sonic hedgehog signaling, this study provides knowledge on the differential susceptibility of region-specific neuronal subtypes derived from hiPSCs to different biomolecules in extracellular matrix remodeling and neurotoxicity. The findings are significant for understanding 3-D neural patterning of hiPSCs for the applications in brain organoid formation, neurological disease modeling, and drug discovery. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
H.J. Alves
2003-02-01
Full Text Available Important advances have been made in understanding the genetic processes that control skeletal muscle formation. Studies conducted on quails detected a delay in the myogenic program of animals selected for high growth rates. These studies have led to the hypothesis that a delay in myogenesis would allow somitic cells to proliferate longer and consequently increase the number of embryonic myoblasts. To test this hypothesis, recently segmented somites and part of the unsegmented paraxial mesoderm were separated from the neural tube/notochord complex in HH12 chicken embryos. In situ hybridization and competitive RT-PCR revealed that MyoD transcripts, which are responsible for myoblast determination, were absent in somites separated from neural tube/notochord (1.06 and 0.06 10-3 attomol MyoD/1 attomol ß-actin for control and separated somites, respectively; P<0.01. However, reapproximation of these structures allowed MyoD to be expressed in somites. Cellular proliferation was analyzed by immunohistochemical detection of incorporated BrdU, a thymidine analogue. A smaller but not significant (P = 0.27 number of proliferating cells was observed in somites that had been separated from neural tube/notochord (27 and 18 for control and separated somites, respectively. These results confirm the influence of the axial structures on MyoD activation but do not support the hypothesis that in the absence of MyoD transcripts the cellular proliferation would be maintained for a longer period of time.
Liu, Yan-Jun; Tong, Shaocheng
2015-03-01
In the paper, an adaptive tracking control design is studied for a class of nonlinear discrete-time systems with dead-zone input. The considered systems are of the nonaffine pure-feedback form and the dead-zone input appears nonlinearly in the systems. The contributions of the paper are that: 1) it is for the first time to investigate the control problem for this class of discrete-time systems with dead-zone; 2) there are major difficulties for stabilizing such systems and in order to overcome the difficulties, the systems are transformed into an n-step-ahead predictor but nonaffine function is still existent; and 3) an adaptive compensative term is constructed to compensate for the parameters of the dead-zone. The neural networks are used to approximate the unknown functions in the transformed systems. Based on the Lyapunov theory, it is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero. Two simulation examples are provided to verify the effectiveness of the control approach in the paper.
International Nuclear Information System (INIS)
Dmitrievskij, S.G.; Gornushkin, Yu.A.; Ososkov, G.A.
2005-01-01
A neural-network (NN) approach for neutrino interaction vertex reconstruction in the OPERA experiment with the help of the Target Tracker (TT) detector is described. A feed-forward NN with the standard back propagation option is used. The energy functional minimization of the network is performed by the method of conjugate gradients. Data preprocessing by means of cellular automaton algorithm is performed. The Hough transform is applied for muon track determination and the robust fitting method is used for shower axis reconstruction. A comparison of the proposed approach with earlier studies, based on the use of the neural network package SNNS, shows their similar performance. The further development of the approach is underway
Yu, Jinpeng; Shi, Peng; Yu, Haisheng; Chen, Bing; Lin, Chong
2015-07-01
This paper considers the problem of discrete-time adaptive position tracking control for a interior permanent magnet synchronous motor (IPMSM) based on fuzzy-approximation. Fuzzy logic systems are used to approximate the nonlinearities of the discrete-time IPMSM drive system which is derived by direct discretization using Euler method, and a discrete-time fuzzy position tracking controller is designed via backstepping approach. In contrast to existing results, the advantage of the scheme is that the number of the adjustable parameters is reduced to two only and the problem of coupling nonlinearity can be overcome. It is shown that the proposed discrete-time fuzzy controller can guarantee the tracking error converges to a small neighborhood of the origin and all the signals are bounded. Simulation results illustrate the effectiveness and the potentials of the theoretic results obtained.
Decentralized control of discrete-time linear time invariant systems with input saturation
Deliu, Ciprian; Deliu, C.; Malek, Babak; Roy, Sandip; Saberi, Ali; Stoorvogel, Antonie Arij
2009-01-01
We study decentralized stabilization of discrete time linear time invariant (LTI) systems subject to actuator saturation, using LTI controllers. The requirement of stabilization under both saturation constraints and decentralization impose obvious necessary conditions on the open-loop plant, namely
Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo
2017-07-01
This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.
Parisian ruin for the dual risk process in discrete-time
Palmowski, Zbigniew; Ramsden, Lewis; Papaioannou, Apostolos D.
2017-01-01
In this paper we consider the Parisian ruin probabilities for the dual risk model in a discrete-time setting. By exploiting the strong Markov property of the risk process we derive a recursive expression for the fnite-time Parisian ruin probability, in terms of classic discrete-time dual ruin probabilities. Moreover, we obtain an explicit expression for the corresponding infnite-time Parisian ruin probability as a limiting case. In order to obtain more analytic results, we employ a conditioni...
Synchronization of discrete-time hyperchaotic systems: An application in communications
International Nuclear Information System (INIS)
Aguilar-Bustos, A.Y.; Cruz-Hernandez, C.
2009-01-01
In this paper, the synchronization problem of discrete-time complex dynamics is presented. In particular, we use the model-matching approach from nonlinear control theory to synchronize two unidirectionally coupled discrete-time hyperchaotic systems. A potential application to secure/private communication of confidential information is also given. By using different (hyperchaotic) encryption schemes with a single and two transmission channels, we show that output synchronization of hyperchaotic maps is indeed suitable for encryption, transmission, and decryption of information.
A continuous-time/discrete-time mixed audio-band sigma delta ADC
International Nuclear Information System (INIS)
Liu Yan; Hua Siliang; Wang Donghui; Hou Chaohuan
2011-01-01
This paper introduces a mixed continuous-time/discrete-time, single-loop, fourth-order, 4-bit audio-band sigma delta ADC that combines the benefits of continuous-time and discrete-time circuits, while mitigating the challenges associated with continuous-time design. Measurement results show that the peak SNR of this ADC reaches 100 dB and the total power consumption is less than 30 mW. (semiconductor integrated circuits)
Function Projective Synchronization in Discrete-Time Chaotic System with Uncertain Parameters
International Nuclear Information System (INIS)
Chen Yong; Li Xin
2009-01-01
The function projective synchronization of discrete-time chaotic systems is presented. Based on backstepping design with three controllers, a systematic, concrete and automatic scheme is developed to investigate function projective synchronization (FPS) of discrete-time chaotic systems with uncertain parameters. With the aid of symbolic-numeric computation, we use the proposed scheme to illustrate FPS between two identical 3D Henon-like maps with uncertain parameters. Numeric simulations are used to verify the effectiveness of our scheme. (general)
Control of the formation of projective synchronisation in lower-dimensional discrete-time systems
International Nuclear Information System (INIS)
Chee, C.Y.; Xu Daolin
2003-01-01
Projective synchronisation was recently observed in partially linear discrete-time systems. The scaling factor that characterises the behaviour of projective synchronisation is however unpredictable. In order to manipulate the ultimate state of the synchronisation, a control algorithm based on Schur-Chon stability criteria is proposed to direct the scaling factor onto any predestined value. In the numerical experiment, we illustrate the application on two chaotic discrete-time systems
Li, Kelin
2010-02-01
In this article, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time-varying delays is formulated and investigated. By employing delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time-varying delays are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.
Zhang, Huaguang; Jiang, He; Luo, Chaomin; Xiao, Geyang
2017-10-01
In this paper, we investigate the nonzero-sum games for a class of discrete-time (DT) nonlinear systems by using a novel policy iteration (PI) adaptive dynamic programming (ADP) method. The main idea of our proposed PI scheme is to utilize the iterative ADP algorithm to obtain the iterative control policies, which not only ensure the system to achieve stability but also minimize the performance index function for each player. This paper integrates game theory, optimal control theory, and reinforcement learning technique to formulate and handle the DT nonzero-sum games for multiplayer. First, we design three actor-critic algorithms, an offline one and two online ones, for the PI scheme. Subsequently, neural networks are employed to implement these algorithms and the corresponding stability analysis is also provided via the Lyapunov theory. Finally, a numerical simulation example is presented to demonstrate the effectiveness of our proposed approach.
Mobile Position Estimation using Artificial Neural Network in CDMA Cellular Systems
Directory of Open Access Journals (Sweden)
Omar Waleed Abdulwahhab
2017-01-01
Full Text Available Using the Neural network as a type of associative memory will be introduced in this paper through the problem of mobile position estimation where mobile estimate its location depending on the signal strength reach to it from several around base stations where the neural network can be implemented inside the mobile. Traditional methods of time of arrival (TOA and received signal strength (RSS are used and compared with two analytical methods, optimal positioning method and average positioning method. The data that are used for training are ideal since they can be obtained based on geometry of CDMA cell topology. The test of the two methods TOA and RSS take many cases through a nonlinear path that MS can move through that region. The results show that the neural network has good performance compared with two other analytical methods which are average positioning method and optimal positioning method.
Directory of Open Access Journals (Sweden)
Yih-Lon Lin
2013-01-01
Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.
International Nuclear Information System (INIS)
Pirouzmand, Ahmad; Hadad, Kamal
2012-01-01
Highlights: ► This paper describes the solution of time-dependent neutron transport equation. ► We use a novel method based on cellular neural networks (CNNs) coupled with the spherical harmonics method. ► We apply the CNN model to simulate step and ramp perturbation transients in a core. ► The accuracy and capabilities of the CNN model are examined for x–y geometry. - Abstract: In an earlier paper we utilized a novel method using cellular neural networks (CNNs) coupled with spherical harmonics method to solve the steady state neutron transport equation in x–y geometry. Here, the previous work is extended to the study of time-dependent neutron transport equation. To achieve this goal, an equivalent electrical circuit based on a second-order form of time-dependent neutron transport equation and one equivalent group of neutron precursor density is obtained by the CNN method. The CNN model is used to simulate step and ramp perturbation transients in a typical 2D core.
Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona
2016-10-01
The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.
Basic Ideas to Approach Metastability in Probabilistic Cellular Automata
Cirillo, Emilio N. M.; Nardi, Francesca R.; Spitoni, Cristian
2016-01-01
Cellular Automata are discrete--time dynamical systems on a spatially extended discrete space which provide paradigmatic examples of nonlinear phenomena. Their stochastic generalizations, i.e., Probabilistic Cellular Automata, are discrete time Markov chains on lattice with finite single--cell
GROENEWEG, JOLIJN W.; WHITE, YVONNE A.R.; KOKEL, DAVID; PETERSON, RANDALL T.; ZUKERBERG, LAWRENCE R.; BERIN, INNA; RUEDA, BO R.; WOOD, ANTONY W.
2014-01-01
SUMMARY In vitro studies have suggested that the Cables1 gene regulates epithelial cell proliferation, whereas other studies suggest a role in promoting neural differentiation. In efforts to clarify the functions of Cables1 in vivo, we conducted gain- and loss-of-function studies targeting its ortholog (cables1) in the zebrafish embryo. Similar to rodents, zebrafish cables1 mRNA expression is detected most robustly in embryonic neural tissues. Antisense knockdown of cables1 leads to increased numbers of apoptotic cells, particularly in brain tissue, in addition to a distinct behavioral phenotype, characterized by hyperactivity in response to stimulation. Apoptosis and the behavioral abnormality could be rescued by co-expression of a morpholino-resistant cables1 construct. Suppression of p53 expression in cables1 morphants partially rescued both apoptosis and the behavioral phenotype, suggesting that the phenotype of cables1 morphants is due in part to p53-dependent apoptosis. Alterations in the expression patterns of several neural transcription factors were observed in cables1 morphants during early neurulation, suggesting that cables1 is required for early neural differentiation. Ectopic overexpression of cables1 strongly disrupted embryonic morphogenesis, while overexpression of a cables1 mutant lacking the C-terminal cyclin box had little effect, suggesting functional importance of the cyclin box. Lastly, marked reductions in p35, but not Cdk5, were observed in cables1 morphants. Collectively, these data suggest that cables1 is important for neural differentiation during embryogenesis, in a mechanism that likely involves interactions with the Cdk5/p35 kinase pathway. PMID:21268180
Stabilization and tracking controller for a class of nonlinear discrete-time systems
International Nuclear Information System (INIS)
Sharma, B.B.; Kar, I.N.
2011-01-01
Highlights: → We present recursive design of stabilizing controller for nonlinear discrete-time systems. → Problem of stabilizing and tracking control of single link manipulator system is addressed. → We extend the proposed results to output tracking problems. → The proposed methodology is applied satisfactorily to discrete-time chaotic maps. - Abstract: In this paper, stabilization and tracking control problem for parametric strict feedback class of discrete time systems is addressed. Recursive design of control function based on contraction theory framework is proposed instead of traditional Lyapunov based method. Explicit structure of controller is derived for the addressed class of nonlinear discrete-time systems. Conditions for exponential stability of system states are derived in terms of controller parameters. At each stage of recursive procedure a specific structure of Jacobian matrix is ensured so as to satisfy conditions of stability. The closed loop dynamics in this case remains nonlinear in nature. The proposed algorithm establishes global stability results in quite a simple manner as it does not require formulation of error dynamics. Problem of stabilization and output tracking control in case of single link manipulator system with actuator dynamics is analyzed using the proposed strategy. The proposed results are further extended to stabilization of discrete time chaotic systems. Numerical simulations presented in the end show the effectiveness of the proposed approach.
Khan, Muhammad Sadiq Ali; Yousuf, Sidrah
2016-03-01
Cardiac Electrical Activity is commonly distributed into three dimensions of Cardiac Tissue (Myocardium) and evolves with duration of time. The indicator of heart diseases can occur randomly at any time of a day. Heart rate, conduction and each electrical activity during cardiac cycle should be monitor non-invasively for the assessment of "Action Potential" (regular) and "Arrhythmia" (irregular) rhythms. Many heart diseases can easily be examined through Automata model like Cellular Automata concepts. This paper deals with the different states of cardiac rhythms using cellular automata with the comparison of neural network also provides fast and highly effective stimulation for the contraction of cardiac muscles on the Atria in the result of genesis of electrical spark or wave. The specific formulated model named as "States of automaton Proposed Model for CEA (Cardiac Electrical Activity)" by using Cellular Automata Methodology is commonly shows the three states of cardiac tissues conduction phenomena (i) Resting (Relax and Excitable state), (ii) ARP (Excited but Absolutely refractory Phase i.e. Excited but not able to excite neighboring cells) (iii) RRP (Excited but Relatively Refractory Phase i.e. Excited and able to excite neighboring cells). The result indicates most efficient modeling with few burden of computation and it is Action Potential during the pumping of blood in cardiac cycle.
Discrete-time sliding mode control for MR vehicle suspension system
Energy Technology Data Exchange (ETDEWEB)
Sohn, J W; Choi, S B [Smart Structures and Systems Laboratory, Department of Mechanical Engineering, Inha University, Incheon 402-751 (Korea, Republic of); Wereley, N M [Smart Structures Laboratory, Department of Aerospace Engineering, University of Maryland, College Park, MD 20742 (United States)], E-mail: seungbok@inha.ac.kr
2009-02-01
This paper presents control performance of a full-vehicle suspension system featuring magnetorheological (MR) dampers via a discrete-time sliding mode control algorithm (DSMC). A cylindrical MR damper is designed by incorporating Bingham model of the MR fluid and the field-dependent damping characteristics of the MR damper are evaluated. A full-vehicle suspension model installed with independent four MR dampers is constructed and the governing equations which include vertical, pitch and roll motion are derived. A discrete-time control model is established with considering system uncertainties and a discrete-time sliding mode controller which has inherent robustness to model uncertainty and external disturbance is formulated. Vibration control performances under bump excitation are evaluated and presented.
Directory of Open Access Journals (Sweden)
Chellaboina Vijaysekhar
2005-01-01
Full Text Available We develop thermodynamic models for discrete-time large-scale dynamical systems. Specifically, using compartmental dynamical system theory, we develop energy flow models possessing energy conservation, energy equipartition, temperature equipartition, and entropy nonconservation principles for discrete-time, large-scale dynamical systems. Furthermore, we introduce a new and dual notion to entropy; namely, ectropy, as a measure of the tendency of a dynamical system to do useful work and grow more organized, and show that conservation of energy in an isolated thermodynamic system necessarily leads to nonconservation of ectropy and entropy. In addition, using the system ectropy as a Lyapunov function candidate, we show that our discrete-time, large-scale thermodynamic energy flow model has convergent trajectories to Lyapunov stable equilibria determined by the system initial subsystem energies.
Discrete-time sliding mode control for MR vehicle suspension system
International Nuclear Information System (INIS)
Sohn, J W; Choi, S B; Wereley, N M
2009-01-01
This paper presents control performance of a full-vehicle suspension system featuring magnetorheological (MR) dampers via a discrete-time sliding mode control algorithm (DSMC). A cylindrical MR damper is designed by incorporating Bingham model of the MR fluid and the field-dependent damping characteristics of the MR damper are evaluated. A full-vehicle suspension model installed with independent four MR dampers is constructed and the governing equations which include vertical, pitch and roll motion are derived. A discrete-time control model is established with considering system uncertainties and a discrete-time sliding mode controller which has inherent robustness to model uncertainty and external disturbance is formulated. Vibration control performances under bump excitation are evaluated and presented.
Stabilisation of discrete-time polynomial fuzzy systems via a polynomial lyapunov approach
Nasiri, Alireza; Nguang, Sing Kiong; Swain, Akshya; Almakhles, Dhafer
2018-02-01
This paper deals with the problem of designing a controller for a class of discrete-time nonlinear systems which is represented by discrete-time polynomial fuzzy model. Most of the existing control design methods for discrete-time fuzzy polynomial systems cannot guarantee their Lyapunov function to be a radially unbounded polynomial function, hence the global stability cannot be assured. The proposed control design in this paper guarantees a radially unbounded polynomial Lyapunov functions which ensures global stability. In the proposed design, state feedback structure is considered and non-convexity problem is solved by incorporating an integrator into the controller. Sufficient conditions of stability are derived in terms of polynomial matrix inequalities which are solved via SOSTOOLS in MATLAB. A numerical example is presented to illustrate the effectiveness of the proposed controller.
Synchronization of cellular neural networks of neutral type via dynamic feedback controller
International Nuclear Information System (INIS)
Park, Ju H.
2009-01-01
In this paper, we aim to study global synchronization for neural networks with neutral delay. A dynamic feedback control scheme is proposed to achieve the synchronization between drive network and response network. By utilizing the Lyapunov function and linear matrix inequalities (LMIs), we derive simple and efficient criterion in terms of LMIs for synchronization. The feedback controllers can be easily obtained by solving the derived LMIs.
Wei, Qinglai; Liu, Derong; Lin, Qiao
In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.
A Novel Analytic Technique for the Service Station Reliability in a Discrete-Time Repairable Queue
Directory of Open Access Journals (Sweden)
Renbin Liu
2013-01-01
Full Text Available This paper presents a decomposition technique for the service station reliability in a discrete-time repairable GeomX/G/1 queueing system, in which the server takes exhaustive service and multiple adaptive delayed vacation discipline. Using such a novel analytic technique, some important reliability indices and reliability relation equations of the service station are derived. Furthermore, the structures of the service station indices are also found. Finally, special cases and numerical examples validate the derived results and show that our analytic technique is applicable to reliability analysis of some complex discrete-time repairable bulk arrival queueing systems.
Continuous and Discrete-Time Optimal Controls for an Isolated Signalized Intersection
Directory of Open Access Journals (Sweden)
Jiyuan Tan
2017-01-01
Full Text Available A classical control problem for an isolated oversaturated intersection is revisited with a focus on the optimal control policy to minimize total delay. The difference and connection between existing continuous-time planning models and recently proposed discrete-time planning models are studied. A gradient descent algorithm is proposed to convert the optimal control plan of the continuous-time model to the plan of the discrete-time model in many cases. Analytic proof and numerical tests for the algorithm are also presented. The findings shed light on the links between two kinds of models.
Observation of Discrete-Time-Crystal Signatures in an Ordered Dipolar Many-Body System
Rovny, Jared; Blum, Robert L.; Barrett, Sean E.
2018-05-01
A discrete time crystal (DTC) is a robust phase of driven systems that breaks the discrete time translation symmetry of the driving Hamiltonian. Recent experiments have observed DTC signatures in two distinct systems. Here we show nuclear magnetic resonance observations of DTC signatures in a third, strikingly different system: an ordered spatial crystal. We use a novel DTC echo experiment to probe the coherence of the driven system. Finally, we show that interactions during the pulse of the DTC sequence contribute to the decay of the signal, complicating attempts to measure the intrinsic lifetime of the DTC.
Adaptive control of discrete-time chaotic systems: a fuzzy control approach
International Nuclear Information System (INIS)
Feng Gang; Chen Guanrong
2005-01-01
This paper discusses adaptive control of a class of discrete-time chaotic systems from a fuzzy control approach. Using the T-S model of discrete-time chaotic systems, an adaptive control algorithm is developed based on some conventional adaptive control techniques. The resulting adaptively controlled chaotic system is shown to be globally stable, and its robustness is discussed. A simulation example of the chaotic Henon map control is finally presented, to illustrate an application and the performance of the proposed control algorithm
Dideriksen, Jakob Lund; Feeney, Daniel F; Almuklass, Awad M; Enoka, Roger M
2017-08-01
Force trajectories during isometric force-matching tasks involving isometric contractions vary substantially across individuals. In this study, we investigated if this variability can be explained by discrete time proportional, integral, derivative (PID) control algorithms with varying model parameters. To this end, we analyzed the pinch force trajectories of 24 subjects performing two rapid force-matching tasks with visual feedback. Both tasks involved isometric contractions to a target force of 10% maximal voluntary contraction. One task involved a single action (pinch) and the other required a double action (concurrent pinch and wrist extension). 50,000 force trajectories were simulated with a computational neuromuscular model whose input was determined by a PID controller with different PID gains and frequencies at which the controller adjusted muscle commands. The goal was to find the best match between each experimental force trajectory and all simulated trajectories. It was possible to identify one realization of the PID controller that matched the experimental force produced during each task for most subjects (average index of similarity: 0.87 ± 0.12; 1 = perfect similarity). The similarities for both tasks were significantly greater than that would be expected by chance (single action: p = 0.01; double action: p = 0.04). Furthermore, the identified control frequencies in the simulated PID controller with the greatest similarities decreased as task difficulty increased (single action: 4.0 ± 1.8 Hz; double action: 3.1 ± 1.3 Hz). Overall, the results indicate that discrete time PID controllers are realistic models for the neural control of force in rapid force-matching tasks involving isometric contractions.
Zheng, Mingwen; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian; Zhang, Yanping; Zhao, Hui
2018-06-01
This paper mainly studies the finite-time stability and synchronization problems of memristor-based fractional-order fuzzy cellular neural network (MFFCNN). Firstly, we discuss the existence and uniqueness of the Filippov solution of the MFFCNN according to the Banach fixed point theorem and give a sufficient condition for the existence and uniqueness of the solution. Secondly, a sufficient condition to ensure the finite-time stability of the MFFCNN is obtained based on the definition of finite-time stability of the MFFCNN and Gronwall-Bellman inequality. Thirdly, by designing a simple linear feedback controller, the finite-time synchronization criterion for drive-response MFFCNN systems is derived according to the definition of finite-time synchronization. These sufficient conditions are easy to verify. Finally, two examples are given to show the effectiveness of the proposed results.
A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services
Sharma, Anuj; Panigrahi, Dr. Prabin Kumar
2013-01-01
Marketing literature states that it is more costly to engage a new customer than to retain an existing loyal customer. Churn prediction models are developed by academics and practitioners to effectively manage and control customer churn in order to retain existing customers. As churn management is an important activity for companies to retain loyal customers, the ability to correctly predict customer churn is necessary. As the cellular network services market becoming more competitive, custom...
Passive Fault-tolerant Control of Discrete-time Piecewise Affine Systems against Actuator Faults
DEFF Research Database (Denmark)
Tabatabaeipour, Seyed Mojtaba; Izadi-Zamanabadi, Roozbeh; Bak, Thomas
2012-01-01
In this paper, we propose a new method for passive fault-tolerant control of discrete time piecewise affine systems. Actuator faults are considered. A reliable piecewise linear quadratic regulator (LQR) state feedback is designed such that it can tolerate actuator faults. A sufficient condition f...... is illustrated on a numerical example and a two degree of freedom helicopter....
Property - preserving convergent sequences of invariant sets for linear discrete - time systems
Athanasopoulos, N.; Lazar, M.; Bitsoris, G.
2014-01-01
Abstract: New sequences of monotonically increasing sets are introduced, for linear discrete-time systems subject to input and state constraints. The elements of the set sequences are controlled invariant and admissible regions of stabilizability. They are generated from the iterative application of
Simulating continuous-time Hamiltonian dynamics by way of a discrete-time quantum walk
International Nuclear Information System (INIS)
Schmitz, A.T.; Schwalm, W.A.
2016-01-01
Much effort has been made to connect the continuous-time and discrete-time quantum walks. We present a method for making that connection for a general graph Hamiltonian on a bigraph. Furthermore, such a scheme may be adapted for simulating discretized quantum models on a quantum computer. A coin operator is found for the discrete-time quantum walk which exhibits the same dynamics as the continuous-time evolution. Given the spectral decomposition of the graph Hamiltonian and certain restrictions, the discrete-time evolution is solved for explicitly and understood at or near important values of the parameters. Finally, this scheme is connected to past results for the 1D chain. - Highlights: • A discrete-time quantum walk is purposed which approximates a continuous-time quantum walk. • The purposed quantum walk could be used to simulate Hamiltonian dynamics on a quantum computer. • Given the spectra decomposition of the Hamiltonian, the quantum walk is solved explicitly. • The method is demonstrated and connected to previous work done on the 1D chain.
Directory of Open Access Journals (Sweden)
Xinggui Liu
2011-01-01
Full Text Available In this paper, by using Mawhin's continuation theorem of coincidence degree theory, we establish the existence of at least four positive periodic solutions for a discrete time Lotka-Volterra competitive system with harvesting terms. An example is given to illustrate the effectiveness of our results.
International Nuclear Information System (INIS)
Chen, S.-F.
2009-01-01
The asymptotic stability problem for discrete-time systems with time-varying delay subject to saturation nonlinearities is addressed in this paper. In terms of linear matrix inequalities (LMIs), a delay-dependent sufficient condition is derived to ensure the asymptotic stability. A numerical example is given to demonstrate the theoretical results.
Nonparametric Estimation of Interval Reliability for Discrete-Time Semi-Markov Systems
DEFF Research Database (Denmark)
Georgiadis, Stylianos; Limnios, Nikolaos
2016-01-01
In this article, we consider a repairable discrete-time semi-Markov system with finite state space. The measure of the interval reliability is given as the probability of the system being operational over a given finite-length time interval. A nonparametric estimator is proposed for the interval...
Naz, Rehana
2018-01-01
Pontrygin-type maximum principle is extended for the present value Hamiltonian systems and current value Hamiltonian systems of nonlinear difference equations for uniform time step $h$. A new method termed as a discrete time current value Hamiltonian method is established for the construction of first integrals for current value Hamiltonian systems of ordinary difference equations arising in Economic growth theory.
The ruin probability of a discrete time risk model under constant interest rate with heavy tails
Tang, Q.
2004-01-01
This paper investigates the ultimate ruin probability of a discrete time risk model with a positive constant interest rate. Under the assumption that the gross loss of the company within one year is subexponentially distributed, a simple asymptotic relation for the ruin probability is derived and
DEFF Research Database (Denmark)
Tabatabaeipour, Seyed Mojtaba; Bak, Thomas
2012-01-01
In this paper we consider the problem of fault estimation and accommodation for discrete time piecewise linear systems. A robust fault estimator is designed to estimate the fault such that the estimation error converges to zero and H∞ performance of the fault estimation is minimized. Then, the es...
Statistical inference for discrete-time samples from affine stochastic delay differential equations
DEFF Research Database (Denmark)
Küchler, Uwe; Sørensen, Michael
2013-01-01
Statistical inference for discrete time observations of an affine stochastic delay differential equation is considered. The main focus is on maximum pseudo-likelihood estimators, which are easy to calculate in practice. A more general class of prediction-based estimating functions is investigated...
From discrete-time models to continuous-time, asynchronous modeling of financial markets
Boer, Katalin; Kaymak, Uzay; Spiering, Jaap
2007-01-01
Most agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modeling of financial markets. We study the behavior of a learning market maker in a market with information
From Discrete-Time Models to Continuous-Time, Asynchronous Models of Financial Markets
K. Boer-Sorban (Katalin); U. Kaymak (Uzay); J. Spiering (Jaap)
2006-01-01
textabstractMost agent-based simulation models of financial markets are discrete-time in nature. In this paper, we investigate to what degree such models are extensible to continuous-time, asynchronous modelling of financial markets. We study the behaviour of a learning market maker in a market with
Discrete-Time Mixing Receiver Architecture for RF-Sampling Software-Defined Radio
Ru, Z.; Klumperink, Eric A.M.; Nauta, Bram
2010-01-01
Abstract—A discrete-time (DT) mixing architecture for RF-sampling receivers is presented. This architecture makes RF sampling more suitable for software-defined radio (SDR) as it achieves wideband quadrature demodulation and wideband harmonic rejection. The paper consists of two parts. In the first
Stochastic ℋ∞ Finite-Time Control of Discrete-Time Systems with Packet Loss
Directory of Open Access Journals (Sweden)
Yingqi Zhang
2012-01-01
Full Text Available This paper investigates the stochastic finite-time stabilization and ℋ∞ control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time system with Markovian jumps to be stochastic ℋ∞ finite-time boundedness and then state feedback controllers are designed to guarantee stochastic ℋ∞ finite-time stabilization of the class of stochastic systems. The stochastic ℋ∞ finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the robust stochastic stabilization of the class of linear systems with packet loss. Finally, simulation examples are presented to illustrate the validity of the developed scheme.
Quasi-stationary distributions for reducible absorbing Markov chains in discrete time
van Doorn, Erik A.; Pollett, P.K.
2009-01-01
We consider discrete-time Markov chains with one coffin state and a finite set $S$ of transient states, and are interested in the limiting behaviour of such a chain as time $n \\to \\infty,$ conditional on survival up to $n$. It is known that, when $S$ is irreducible, the limiting conditional
Controllability of a Class of Bimodal Discrete-Time Piecewise Linear Systems
Yurtseven, E.; Camlibel, M.K.; Heemels, W.P.M.H.
2013-01-01
In this paper we will provide algebraic necessary and sufficient conditions for the controllability/reachability/null controllability of a class of bimodal discrete-time piecewise linear systems including several instances of interest that are not covered by existing works which focus primarily on
Sampled-data and discrete-time H2 optimal control
Trentelman, Harry L.; Stoorvogel, Anton A.
1993-01-01
This paper deals with the sampled-data H2 optimal control problem. Given a linear time-invariant continuous-time system, the problem of minimizing the H2 performance over all sampled-data controllers with a fixed sampling period can be reduced to a pure discrete-time H2 optimal control problem. This
The problem with time in mixed continuous/discrete time modelling
Rovers, K.C.; Kuper, Jan; Smit, Gerardus Johannes Maria
The design of cyber-physical systems requires the use of mixed continuous time and discrete time models. Current modelling tools have problems with time transformations (such as a time delay) or multi-rate systems. We will present a novel approach that implements signals as functions of time,
Discrete-Time receivers for software-defined radio: challenges and solutions
Ru, Z.; Klumperink, Eric A.M.; Nauta, Bram
2007-01-01
Abstract—CMOS radio receiver architectures, based on radio frequency (RF) sampling followed by discrete-time (DT) signal processing via switched-capacitor circuits, have recently been proposed for dedicated radio standards. This paper explores the suitability of such DT receivers for highly flexible
On the Suitability of Discrete-Time Receivers for Software-Defined Radio
Ru, Z.; Klumperink, Eric A.M.; Nauta, Bram
2007-01-01
Abstract—CMOS radio receiver architectures, based on radio frequency (RF) sampling followed by discrete-time (D-T) signal processing via switched-capacitor circuits, have recently been proposed for dedicated radio standards. This paper explores the suitability of such D-T receivers for highly
A theory of Markovian time-inconsistent stochastic control in discrete time
DEFF Research Database (Denmark)
Bjork, Tomas; Murgoci, Agatha
2014-01-01
We develop a theory for a general class of discrete-time stochastic control problems that, in various ways, are time-inconsistent in the sense that they do not admit a Bellman optimality principle. We attack these problems by viewing them within a game theoretic framework, and we look for subgame...
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, R.N.J.; Vries, L.B.
1986-01-01
The authors present an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, Raymond N.J.; Vries, Lodewijk B.
1986-01-01
This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Homogeneous Discrete Time Alternating Compound Renewal Process: A Disability Insurance Application
Directory of Open Access Journals (Sweden)
Guglielmo D’Amico
2015-01-01
Full Text Available Discrete time alternating renewal process is a very simple tool that permits solving many real life problems. This paper, after the presentation of this tool, introduces the compound environment in the alternating process giving a systematization to this important tool. The claim costs for a temporary disability insurance contract are presented. The algorithm and an example of application are also provided.
ON THE ANISOTROPIC NORM OF DISCRETE TIME STOCHASTIC SYSTEMS WITH STATE DEPENDENT NOISE
Directory of Open Access Journals (Sweden)
Isaac Yaesh
2013-01-01
Full Text Available The purpose of this paper is to determine conditions for the bound-edness of the anisotropic norm of discrete-time linear stochastic sys-tems with state dependent noise. It is proved that these conditions canbe expressed in terms of the feasibility of a specific system of matrixinequalities.
Scaled Bilateral Teleoperation Using Discrete-Time Sliding-Mode Controller
Khan, S.; Sabanovic, A.; Nergiz, A.O.
2009-01-01
In this paper, the design of a discrete-time sliding-mode controller based on Lyapunov theory is presented along with a robust disturbance observer and is applied to a piezostage for high-precision motion. A linear model of a piezostage was used with nominal parameters to compensate the disturbance
Less Conservative ℋ∞ Fuzzy Control for Discrete-Time Takagi-Sugeno Systems
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Leonardo Amaral Mozelli
2011-01-01
Full Text Available New analysis and control design conditions of discrete-time fuzzy systems are proposed. Using fuzzy Lyapunov's functions and introducing slack variables, less conservative conditions are obtained. The controller guarantees system stabilization and ℋ∞ performance. Numerical tests and a practical experiment in Chua's circuit are presented to show the effectiveness.
Decentralized control of discrete-time linear time invariant systems with input saturation
Deliu, C.; Deliu, Ciprian; Malek, Babak; Roy, Sandip; Saberi, Ali; Stoorvogel, Antonie Arij
We study decentralized stabilization of discrete-time linear time invariant (LTI) systems subject to actuator saturation, using LTI controllers. The requirement of stabilization under both saturation constraints and decentralization impose obvious necessary conditions on the open-loop plant, namely
Communication scheduling in robust self-triggered MPC for linear discrete-time systems
Brunner, F.D.; Gommans, T.M.P.; Heemels, W.P.M.H.; Allgöwer, F.
2015-01-01
We consider a networked control system consisting of a physical plant, an actuator, a sensor, and a controller that is connected to the actuator and sensor via a communication network. The plant is described by a linear discrete-time system subject to additive disturbances. In order to reduce the
Geometric ergodicity and quasi-stationarity in discrete-time birth-death processes
van Doorn, Erik A.; Schrijner, Pauline
1995-01-01
We study two aspects of discrete-time birth-death processes, the common feature of which is the central role played by the decay parameter of the process. First, conditions for geometric ergodicity and bounds for the decay parameter are obtained. Then the existence and structure of quasi-stationary
It's Deja Vu All over Again: Using Multiple-Spell Discrete-Time Survival Analysis.
Willett, John B.; Singer, Judith D.
1995-01-01
The multiple-spell discrete-time survival analysis method is introduced and illustrated using longitudinal data on exit from and reentry into the teaching profession. The method is applicable to many educational problems involving the sequential occurrence of disparate events or episodes. (SLD)
Direct Adaptive Control of a Class of Nonlinear Discrete-Time Systems
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
2004-01-01
In this paper we deal with direct adaptive control of a specific class of discrete-time SISO systems, where the nonlinearities are convex and an upper bound is known. We use a control law based on a linear combination of a set of globally uniformly bounded basis functions with compact support, wh...
Linear quadratic Gaussian balancing for discrete-time infinite-dimensional linear systems
Opmeer, MR; Curtain, RF
2004-01-01
In this paper, we study the existence of linear quadratic Gaussian (LQG)-balanced realizations for discrete-time infinite-dimensional systems. LQG-balanced realizations are those for which the smallest nonnegative self-adjoint solutions of the control and filter Riccati equations are equal. We show
Matsubara, Takashi; Torikai, Hiroyuki
2016-04-01
Modeling and implementation approaches for the reproduction of input-output relationships in biological nervous tissues contribute to the development of engineering and clinical applications. However, because of high nonlinearity, the traditional modeling and implementation approaches encounter difficulties in terms of generalization ability (i.e., performance when reproducing an unknown data set) and computational resources (i.e., computation time and circuit elements). To overcome these difficulties, asynchronous cellular automaton-based neuron (ACAN) models, which are described as special kinds of cellular automata that can be implemented as small asynchronous sequential logic circuits have been proposed. This paper presents a novel type of such ACAN and a theoretical analysis of its excitability. This paper also presents a novel network of such neurons, which can mimic input-output relationships of biological and nonlinear ordinary differential equation model neural networks. Numerical analyses confirm that the presented network has a higher generalization ability than other major modeling and implementation approaches. In addition, Field-Programmable Gate Array-implementations confirm that the presented network requires lower computational resources.
Ecological monitoring in a discrete-time prey-predator model.
Gámez, M; López, I; Rodríguez, C; Varga, Z; Garay, J
2017-09-21
The paper is aimed at the methodological development of ecological monitoring in discrete-time dynamic models. In earlier papers, in the framework of continuous-time models, we have shown how a systems-theoretical methodology can be applied to the monitoring of the state process of a system of interacting populations, also estimating certain abiotic environmental changes such as pollution, climatic or seasonal changes. In practice, however, there may be good reasons to use discrete-time models. (For instance, there may be discrete cycles in the development of the populations, or observations can be made only at discrete time steps.) Therefore the present paper is devoted to the development of the monitoring methodology in the framework of discrete-time models of population ecology. By monitoring we mean that, observing only certain component(s) of the system, we reconstruct the whole state process. This may be necessary, e.g., when in a complex ecosystem the observation of the densities of certain species is impossible, or too expensive. For the first presentation of the offered methodology, we have chosen a discrete-time version of the classical Lotka-Volterra prey-predator model. This is a minimal but not trivial system where the methodology can still be presented. We also show how this methodology can be applied to estimate the effect of an abiotic environmental change, using a component of the population system as an environmental indicator. Although this approach is illustrated in a simplest possible case, it can be easily extended to larger ecosystems with several interacting populations and different types of abiotic environmental effects. Copyright © 2017 Elsevier Ltd. All rights reserved.
Prodromidou, Kanella; Papastefanaki, Florentia; Sklaviadis, Theodoros; Matsas, Rebecca
2014-06-01
Cellular prion protein (PrP) is prominently expressed in brain, in differentiated neurons but also in neural stem/precursor cells (NPCs). The misfolding of PrP is a central event in prion diseases, yet the physiological function of PrP is insufficiently understood. Although PrP has been reported to associate with the neural cell adhesion molecule (NCAM), the consequences of concerted PrP-NCAM action in NPC physiology are unknown. Here, we generated NPCs from the subventricular zone (SVZ) of postnatal day 5 wild-type and PrP null (-/-) mice and observed that PrP is essential for proper NPC proliferation and neuronal differentiation. Moreover, we found that PrP is required for the NPC response to NCAM-induced neuronal differentiation. In the absence of PrP, NCAM not only fails to promote neuronal differentiation but also induces an accumulation of doublecortin-positive neuronal progenitors at the proliferation stage. In agreement, we noted an increase in cycling neuronal progenitors in the SVZ of PrP-/- mice compared with PrP+/+ mice, as evidenced by double labeling for the proliferation marker Ki67 and doublecortin as well as by 5-bromo-2'-deoxyuridine incorporation experiments. Additionally, fewer newly born neurons were detected in the rostral migratory stream of PrP-/- mice. Analysis of the migration of SVZ cells in microexplant cultures from wild-type and PrP-/- mice revealed no differences between genotypes or a role for NCAM in this process. Our data demonstrate that PrP plays a critical role in neuronal differentiation of NPCs and suggest that this function is, at least in part, NCAM-dependent. © 2014 AlphaMed Press.
Hortos, William S.
1997-04-01
The use of artificial neural networks (NNs) to address the channel assignment problem (CAP) for cellular time-division multiple access and code-division multiple access networks has previously been investigated by this author and many others. The investigations to date have been based on a hexagonal cell structure established by omnidirectional antennas at the base stations. No account was taken of the use of spatial isolation enabled by directional antennas to reduce interference between mobiles. Any reduction in interference translates into increased capacity and consequently alters the performance of the NNs. Previous studies have sought to improve the performance of Hopfield- Tank network algorithms and self-organizing feature map algorithms applied primarily to static channel assignment (SCA) for cellular networks that handle uniformly distributed, stationary traffic in each cell for a single type of service. The resulting algorithms minimize energy functions representing interference constraint and ad hoc conditions that promote convergence to optimal solutions. While the structures of the derived neural network algorithms (NNAs) offer the potential advantages of inherent parallelism and adaptability to changing system conditions, this potential has yet to be fulfilled the CAP for emerging mobile networks. The next-generation communication infrastructures must accommodate dynamic operating conditions. Macrocell topologies are being refined to microcells and picocells that can be dynamically sectored by adaptively controlled, directional antennas and programmable transceivers. These networks must support the time-varying demands for personal communication services (PCS) that simultaneously carry voice, data and video and, thus, require new dynamic channel assignment (DCA) algorithms. This paper examines the impact of dynamic cell sectoring and geometric conditioning on NNAs developed for SCA in omnicell networks with stationary traffic to improve the metrics
Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction
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Lin JianJhong
2009-01-01
Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.
Hierarchical random cellular neural networks for system-level brain-like signal processing.
Kozma, Robert; Puljic, Marko
2013-09-01
Sensory information processing and cognition in brains are modeled using dynamic systems theory. The brain's dynamic state is described by a trajectory evolving in a high-dimensional state space. We introduce a hierarchy of random cellular automata as the mathematical tools to describe the spatio-temporal dynamics of the cortex. The corresponding brain model is called neuropercolation which has distinct advantages compared to traditional models using differential equations, especially in describing spatio-temporal discontinuities in the form of phase transitions. Phase transitions demarcate singularities in brain operations at critical conditions, which are viewed as hallmarks of higher cognition and awareness experience. The introduced Monte-Carlo simulations obtained by parallel computing point to the importance of computer implementations using very large-scale integration (VLSI) and analog platforms. Copyright © 2013 Elsevier Ltd. All rights reserved.
Cellular Analysis of Adult Neural Stem Cells for Investigating Prion Biology.
Haigh, Cathryn L
2017-01-01
Traditional primary and secondary cell cultures have been used for the investigation of prion biology and disease for many years. While both types of cultures produce highly valid and immensely valuable results, they also have their limitations; traditional cell lines are often derived from cancers, therefore subject to numerous DNA changes, and primary cultures are labor-intensive and expensive to produce requiring sacrifice of many animals. Neural stem cell (NSC) cultures are a relatively new technology to be used for the study of prion biology and disease. While NSCs are subject to their own limitations-they are generally cultured ex vivo in environments that artificially force their growth-they also have their own unique advantages. NSCs retain the ability for self-renewal and can therefore be propagated in culture similarly to secondary cultures without genetic manipulation. In addition, NSCs are multipotent; they can be induced to differentiate into mature cells of central nervous system (CNS) linage. The combination of self-renewal and multipotency allows NSCs to be used as a primary cell line over multiple generations saving time, costs, and animal harvests, thus providing a valuable addition to the existing cell culture repertoire used for investigation of prion biology and disease. Furthermore, NSC cultures can be generated from mice of any genotype, either by embryonic harvest or harvest from adult brain, allowing gene expression to be studied without further genetic manipulation. This chapter describes a standard method of culturing adult NSCs and assays for monitoring NSC growth, migration, and differentiation and revisits basic reactive oxygen species detection in the context of NSC cultures.
A Discrete-Time Geo/G/1 Retrial Queue with Two Different Types of Vacations
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Feng Zhang
2015-01-01
Full Text Available We analyze a discrete-time Geo/G/1 retrial queue with two different types of vacations and general retrial times. Two different types of vacation policies are investigated in this model, one of which is nonexhaustive urgent vacation during serving and the other is normal exhaustive vacation. For this model, we give the steady-state analysis for the considered queueing system. Firstly, we obtain the generating functions of the number of customers in our model. Then, we obtain the closed-form expressions of some performance measures and also give a stochastic decomposition result for the system size. Moreover, the relationship between this discrete-time model and the corresponding continuous-time model is also investigated. Finally, some numerical results are provided to illustrate the effect of nonexhaustive urgent vacation on some performance characteristics of the system.
Globally asymptotically stable analysis in a discrete time eco-epidemiological system
International Nuclear Information System (INIS)
Hu, Zengyun; Teng, Zhidong; Zhang, Tailei; Zhou, Qiming; Chen, Xi
2017-01-01
Highlights: • Dynamical behaviors of a discrete time eco-epidemiological system are discussed. • Global asymptotical stability of this system is obtained by an iteration scheme which can be expended to general dimensional discrete system. • More complex dynamical behaviors are obtained by numerical simulations. - Abstract: In this study, the dynamical behaviors of a discrete time eco-epidemiological system are discussed. The local stability, bifurcation and chaos are obtained. Moreover, the global asymptotical stability of this system is explored by an iteration scheme. The numerical simulations illustrate the theoretical results and exhibit the complex dynamical behaviors such as flip bifurcation, Hopf bifurcation and chaotic dynamical behaviors. Our main results provide an efficient method to analyze the global asymptotical stability for general three dimensional discrete systems.
Reliable gain-scheduled control of discrete-time systems and its application to CSTR model
Sakthivel, R.; Selvi, S.; Mathiyalagan, K.; Shi, Y.
2016-10-01
This paper is focused on reliable gain-scheduled controller design for a class of discrete-time systems with randomly occurring nonlinearities and actuator fault. Further, the nonlinearity in the system model is assumed to occur randomly according to a Bernoulli distribution with measurable time-varying probability in real time. The main purpose of this paper is to design a gain-scheduled controller by implementing a probability-dependent Lyapunov function and linear matrix inequality (LMI) approach such that the closed-loop discrete-time system is stochastically stable for all admissible randomly occurring nonlinearities. The existence conditions for the reliable controller is formulated in terms of LMI constraints. Finally, the proposed reliable gain-scheduled control scheme is applied on continuously stirred tank reactor model to demonstrate the effectiveness and applicability of the proposed design technique.
A simple method of chaos control for a class of chaotic discrete-time systems
International Nuclear Information System (INIS)
Jiang Guoping; Zheng Weixing
2005-01-01
In this paper, a simple method is proposed for chaos control for a class of discrete-time chaotic systems. The proposed method is built upon the state feedback control and the characteristic of ergodicity of chaos. The feedback gain matrix of the controller is designed using a simple criterion, so that control parameters can be selected via the pole placement technique of linear control theory. The new controller has a feature that it only uses the state variable for control and does not require the target equilibrium point in the feedback path. Moreover, the proposed control method cannot only overcome the so-called 'odd eigenvalues number limitation' of delayed feedback control, but also control the chaotic systems to the specified equilibrium points. The effectiveness of the proposed method is demonstrated by a two-dimensional discrete-time chaotic system
Sliding mode control-based linear functional observers for discrete-time stochastic systems
Singh, Satnesh; Janardhanan, Sivaramakrishnan
2017-11-01
Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.
Discrete time population dynamics of a two-stage species with recruitment and capture
International Nuclear Information System (INIS)
Ladino, Lilia M.; Mammana, Cristiana; Michetti, Elisabetta; Valverde, Jose C.
2016-01-01
This work models and analyzes the dynamics of a two-stage species with recruitment and capture factors. It arises from the discretization of a previous model developed by Ladino and Valverde (2013), which represents a progress in the knowledge of the dynamics of exploited populations. Although the methods used here are related to the study of discrete-time systems and are different from those related to continuous version, the results are similar in both the discrete and the continuous case what confirm the skill in the selection of the factors to design the model. Unlike for the continuous-time case, for the discrete-time one some (non-negative) parametric constraints are derived from the biological significance of the model and become fundamental for the proofs of such results. Finally, numerical simulations show different scenarios of dynamics related to the analytical results which confirm the validity of the model.
Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.
Wei, Qinglai; Li, Benkai; Song, Ruizhuo
2018-04-01
In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.
Limitations of discrete-time quantum walk on a one-dimensional infinite chain
Lin, Jia-Yi; Zhu, Xuanmin; Wu, Shengjun
2018-04-01
How well can we manipulate the state of a particle via a discrete-time quantum walk? We show that the discrete-time quantum walk on a one-dimensional infinite chain with coin operators that are independent of the position can only realize product operators of the form eiξ A ⊗1p, which cannot change the position state of the walker. We present a scheme to construct all possible realizations of all the product operators of the form eiξ A ⊗1p. When the coin operators are dependent on the position, we show that the translation operators on the position can not be realized via a DTQW with coin operators that are either the identity operator 1 or the Pauli operator σx.
Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach
Energy Technology Data Exchange (ETDEWEB)
Pham, Huyên, E-mail: pham@math.univ-paris-diderot.fr; Wei, Xiaoli, E-mail: tyswxl@gmail.com [Laboratoire de Probabilités et Modèles Aléatoires, CNRS, UMR 7599, Université Paris Diderot (France)
2016-12-15
We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.
Output-Feedback Control for Discrete-Time Spreading Models in Complex Networks
Directory of Open Access Journals (Sweden)
Luis A. Alarcón Ramos
2018-03-01
Full Text Available The problem of stabilizing the spreading process to a prescribed probability distribution over a complex network is considered, where the dynamics of the nodes in the network is given by discrete-time Markov-chain processes. Conditions for the positioning and identification of actuators and sensors are provided, and sufficient conditions for the exponential stability of the desired distribution are derived. Simulations results for a network of N = 10 6 corroborate our theoretical findings.
Robust Active MPC Synchronization for Two Discrete-Time Chaotic Systems with Bounded Disturbance
Directory of Open Access Journals (Sweden)
Longge Zhang
2017-01-01
Full Text Available This paper proposes a synchronization scheme for two discrete-time chaotic systems with bounded disturbance. By using active control method and imposing some restriction on the error state, the computation of controller’s feedback matrix is converted to the min-max optimization problem. The theoretical results are derived with the aid of predictive model predictive paradigm and linear matrix inequality technique. Two example simulations are performed to show the effectiveness of the designed control method.
Discrete time motion model for guiding people in urban areas using multiple robots
Garrell Zulueta, Anais; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc
2009-01-01
We present a new model for people guidance in urban settings using several mobile robots, that overcomes the limitations of existing approaches, which are either tailored to tightly bounded environments, or based on unrealistic human behaviors. Although the robots motion is controlled by means of a standard particle filter formulation, the novelty of our approach resides in how the environment and human and robot motions are modeled. In particular we define a “Discrete-Time-Motion” model, whi...
Period-doubling bifurcation and chaos control in a discrete-time mosquito model
Directory of Open Access Journals (Sweden)
Qamar Din
2017-12-01
Full Text Available This article deals with the study of some qualitative properties of a discrete-time mosquito Model. It is shown that there exists period-doubling bifurcation for wide range of bifurcation parameter for the unique positive steady-state of given system. In order to control the bifurcation we introduced a feedback strategy. For further confirmation of complexity and chaotic behavior largest Lyapunov exponents are plotted.
Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach
International Nuclear Information System (INIS)
Pham, Huyên; Wei, Xiaoli
2016-01-01
We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.
CROSAT: A digital computer program for statistical-spectral analysis of two discrete time series
International Nuclear Information System (INIS)
Antonopoulos Domis, M.
1978-03-01
The program CROSAT computes directly from two discrete time series auto- and cross-spectra, transfer and coherence functions, using a Fast Fourier Transform subroutine. Statistical analysis of the time series is optional. While of general use the program is constructed to be immediately compatible with the ICL 4-70 and H316 computers at AEE Winfrith, and perhaps with minor modifications, with any other hardware system. (author)
Directory of Open Access Journals (Sweden)
Tao Wang
2013-01-01
Full Text Available To obtain reliable transient auditory evoked potentials (AEPs from EEGs recorded using high stimulus rate (HSR paradigm, it is critical to design the stimulus sequences of appropriate frequency properties. Traditionally, the individual stimulus events in a stimulus sequence occur only at discrete time points dependent on the sampling frequency of the recording system and the duration of stimulus sequence. This dependency likely causes the implementation of suboptimal stimulus sequences, sacrificing the reliability of resulting AEPs. In this paper, we explicate the use of continuous-time stimulus sequence for HSR paradigm, which is independent of the discrete electroencephalogram (EEG recording system. We employ simulation studies to examine the applicability of the continuous-time stimulus sequences and the impacts of sampling frequency on AEPs in traditional studies using discrete-time design. Results from these studies show that the continuous-time sequences can offer better frequency properties and improve the reliability of recovered AEPs. Furthermore, we find that the errors in the recovered AEPs depend critically on the sampling frequencies of experimental systems, and their relationship can be fitted using a reciprocal function. As such, our study contributes to the literature by demonstrating the applicability and advantages of continuous-time stimulus sequences for HSR paradigm and by revealing the relationship between the reliability of AEPs and sampling frequencies of the experimental systems when discrete-time stimulus sequences are used in traditional manner for the HSR paradigm.
Discrete-time Calogero-Moser system and Lagrangian 1-form structure
International Nuclear Information System (INIS)
Yoo-Kong, Sikarin; Lobb, Sarah; Nijhoff, Frank
2011-01-01
We study the Lagrange formalism of the (rational) Calogero-Moser (CM) system, both in discrete time and continuous time, as a first example of a Lagrangian 1-form structure in the sense of the recent paper (Lobb and Nijhoff 2009 J. Phys. A: Math. Theor.42 454013). The discrete-time model of the CM system was established some time ago arising as a pole reduction of a semi-discrete version of the Kadomtsev-Petviashvili (KP) equation, and was shown to lead to an exactly integrable correspondence (multivalued map). In this paper, we present the full KP solution based on the commutativity of the discrete-time flows in the two discrete KP variables. The compatibility of the corresponding Lax matrices is shown to lead directly to the relevant closure relation on the level of the Lagrangians. Performing successive continuum limits on both the level of the KP equation and the level of the CM system, we establish the proper Lagrangian 1-form structure for the continuum case of the CM model. We use the example of the three-particle case to elucidate the implementation of the novel least-action principle, which was presented in Lobb and Nijhoff (2009), for the simpler case of Lagrangian 1-forms. (paper)
Local and global dynamics of Ramsey model: From continuous to discrete time.
Guzowska, Malgorzata; Michetti, Elisabetta
2018-05-01
The choice of time as a discrete or continuous variable may radically affect equilibrium stability in an endogenous growth model with durable consumption. In the continuous-time Ramsey model [F. P. Ramsey, Econ. J. 38(152), 543-559 (1928)], the steady state is locally saddle-path stable with monotonic convergence. However, in the discrete-time version, the steady state may be unstable or saddle-path stable with monotonic or oscillatory convergence or periodic solutions [see R.-A. Dana et al., Handbook on Optimal Growth 1 (Springer, 2006) and G. Sorger, Working Paper No. 1505 (2015)]. When this occurs, the discrete-time counterpart of the continuous-time model is not consistent with the initial framework. In order to obtain a discrete-time Ramsey model preserving the main properties of the continuous-time counterpart, we use a general backward and forward discretisation as initially proposed by Bosi and Ragot [Theor. Econ. Lett. 2(1), 10-15 (2012)]. The main result of the study here presented is that, with this hybrid discretisation method, fixed points and local dynamics do not change. For what it concerns global dynamics, i.e., long-run behavior for initial conditions taken on the state space, we mainly perform numerical analysis with the main scope of comparing both qualitative and quantitative evolution of the two systems, also varying some parameters of interest.
On Stochastic Finite-Time Control of Discrete-Time Fuzzy Systems with Packet Dropout
Directory of Open Access Journals (Sweden)
Yingqi Zhang
2012-01-01
Full Text Available This paper is concerned with the stochastic finite-time stability and stochastic finite-time boundedness problems for one family of fuzzy discrete-time systems over networks with packet dropout, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, we present the dynamic model description studied, in which the discrete-time fuzzy T-S systems with packet loss can be described by one class of fuzzy Markovian jump systems. Then, the concepts of stochastic finite-time stability and stochastic finite-time boundedness and problem formulation are given. Based on Lyapunov function approach, sufficient conditions on stochastic finite-time stability and stochastic finite-time boundedness are established for the resulting closed-loop fuzzy discrete-time system with Markovian jumps, and state-feedback controllers are designed to ensure stochastic finite-time stability and stochastic finite-time boundedness of the class of fuzzy systems. The stochastic finite-time stability and stochastic finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the stochastic stability of the class of fuzzy T-S systems with packet loss. Finally, two illustrative examples are presented to show the validity of the developed methodology.
Luo, Biao; Liu, Derong; Wu, Huai-Ning
2018-06-01
Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.
Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan
2015-01-01
Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.
Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai
2014-07-01
In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Hadad, Kamal; Pirouzmand, Ahmad; Ayoobian, Navid
2008-01-01
This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the time dependent one-speed neutron transport equation in slab geometry. We use a neutron angular flux in terms of the Chebyshev polynomials (T N ) of the first kind and then we attempt to implement the equations in an equivalent electrical circuit. We apply this equivalent circuit to analyze the T N moments equation in a uniform finite slab using Marshak type vacuum boundary condition. The validity of the CNN results is evaluated with numerical solution of the steady state T N moments equations by MATLAB. Steady state, as well as transient simulations, shows a very good comparison between the two methods. We used our CNN model to simulate space-time response of total flux and its moments for various c (where c is the mean number of secondary neutrons per collision). The complete algorithm could be implemented using very large-scale integrated circuit (VLSI) circuitry. The efficiency of the calculation method makes it useful for neutron transport calculations
Zakary, Omar; Rachik, Mostafa; Elmouki, Ilias
2017-08-01
First, we devise in this paper, a multi-regions discrete-time model which describes the spatial-temporal spread of an epidemic which starts from one region and enters to regions which are connected with their neighbors by any kind of anthropological movement. We suppose homogeneous Susceptible-Infected-Removed (SIR) populations, and we consider in our simulations, a grid of colored cells, which represents the whole domain affected by the epidemic while each cell can represent a sub-domain or region. Second, in order to minimize the number of infected individuals in one region, we propose an optimal control approach based on a travel-blocking vicinity strategy which aims to control only one cell by restricting movements of infected people coming from all neighboring cells. Thus, we show the influence of the optimal control approach on the controlled cell. We should also note that the cellular modeling approach we propose here, can also describes infection dynamics of regions which are not necessarily attached one to an other, even if no empty space can be viewed between cells. The theoretical method we follow for the characterization of the travel-locking optimal controls, is based on a discrete version of Pontryagin's maximum principle while the numerical approach applied to the multi-points boundary value problems we obtain here, is based on discrete progressive-regressive iterative schemes. We illustrate our modeling and control approaches by giving an example of 100 regions.
Praet, J.; SANTERMANS, Eva; Reekmans, K.; de Vocht, N.; Le Blon, D.; Hoornaert, C.; Daans, J.; Goossens, H.; Berneman, Z.; HENS, Niel; Van der Linden, A.; Ponsaerts, P.
2014-01-01
Preclinical animal studies involving intracerebral (stem) cell grafting are gaining popularity in many laboratories due to the reported beneficial effects of cell grafting on various diseases or traumata of the central nervous system (CNS). In this chapter, we describe a histological workflow to characterize and quantify cellular events following neural and fibroblast(-like) stem cell grafting in healthy and demyelinated CNS tissue. First, we provide standardized protocols to isolate and cult...
Quantum circuit dynamics via path integrals: Is there a classical action for discrete-time paths?
International Nuclear Information System (INIS)
Penney, Mark D; Koh, Dax Enshan; Spekkens, Robert W
2017-01-01
It is straightforward to compute the transition amplitudes of a quantum circuit using the sum-over-paths methodology when the gates in the circuit are balanced, where a balanced gate is one for which all non-zero transition amplitudes are of equal magnitude. Here we consider the question of whether, for such circuits, the relative phases of different discrete-time paths through the configuration space can be defined in terms of a classical action, as they are for continuous-time paths. We show how to do so for certain kinds of quantum circuits, namely, Clifford circuits where the elementary systems are continuous-variable systems or discrete systems of odd-prime dimension. These types of circuit are distinguished by having phase-space representations that serve to define their classical counterparts. For discrete systems, the phase-space coordinates are also discrete variables. We show that for each gate in the generating set, one can associate a symplectomorphism on the phase-space and to each of these one can associate a generating function, defined on two copies of the configuration space. For discrete systems, the latter association is achieved using tools from algebraic geometry. Finally, we show that if the action functional for a discrete-time path through a sequence of gates is defined using the sum of the corresponding generating functions, then it yields the correct relative phases for the path-sum expression. These results are likely to be relevant for quantizing physical theories where time is fundamentally discrete, characterizing the classical limit of discrete-time quantum dynamics, and proving complexity results for quantum circuits. (paper)
Quantum circuit dynamics via path integrals: Is there a classical action for discrete-time paths?
Penney, Mark D.; Enshan Koh, Dax; Spekkens, Robert W.
2017-07-01
It is straightforward to compute the transition amplitudes of a quantum circuit using the sum-over-paths methodology when the gates in the circuit are balanced, where a balanced gate is one for which all non-zero transition amplitudes are of equal magnitude. Here we consider the question of whether, for such circuits, the relative phases of different discrete-time paths through the configuration space can be defined in terms of a classical action, as they are for continuous-time paths. We show how to do so for certain kinds of quantum circuits, namely, Clifford circuits where the elementary systems are continuous-variable systems or discrete systems of odd-prime dimension. These types of circuit are distinguished by having phase-space representations that serve to define their classical counterparts. For discrete systems, the phase-space coordinates are also discrete variables. We show that for each gate in the generating set, one can associate a symplectomorphism on the phase-space and to each of these one can associate a generating function, defined on two copies of the configuration space. For discrete systems, the latter association is achieved using tools from algebraic geometry. Finally, we show that if the action functional for a discrete-time path through a sequence of gates is defined using the sum of the corresponding generating functions, then it yields the correct relative phases for the path-sum expression. These results are likely to be relevant for quantizing physical theories where time is fundamentally discrete, characterizing the classical limit of discrete-time quantum dynamics, and proving complexity results for quantum circuits.
Robust Stabilization of Discrete-Time Systems with Time-Varying Delay: An LMI Approach
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Valter J. S. Leite
2008-01-01
Full Text Available Sufficient linear matrix inequality (LMI conditions to verify the robust stability and to design robust state feedback gains for the class of linear discrete-time systems with time-varying delay and polytopic uncertainties are presented. The conditions are obtained through parameter-dependent Lyapunov-Krasovskii functionals and use some extra variables, which yield less conservative LMI conditions. Both problems, robust stability analysis and robust synthesis, are formulated as convex problems where all system matrices can be affected by uncertainty. Some numerical examples are presented to illustrate the advantages of the proposed LMI conditions.
Game theory to characterize solutions of a discrete-time Hamilton-Jacobi equation
International Nuclear Information System (INIS)
Toledo, Porfirio
2013-01-01
We study the behavior of solutions of a discrete-time Hamilton-Jacobi equation in a minimax framework of game theory. The solutions of this problem represent the optimal payoff of a zero-sum game of two players, where the number of moves between the players converges to infinity. A real number, called the critical value, plays a central role in this work; this number is the asymptotic average action of optimal trajectories. The aim of this paper is to show the existence and characterization of solutions of a Hamilton-Jacobi equation for this kind of games
Guaranteed Cost Finite-Time Control of Discrete-Time Positive Impulsive Switched Systems
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Leipo Liu
2018-01-01
Full Text Available This paper considers the guaranteed cost finite-time boundedness of discrete-time positive impulsive switched systems. Firstly, the definition of guaranteed cost finite-time boundedness is introduced. By using the multiple linear copositive Lyapunov function (MLCLF and average dwell time (ADT approach, a state feedback controller is designed and sufficient conditions are obtained to guarantee that the corresponding closed-loop system is guaranteed cost finite-time boundedness (GCFTB. Such conditions can be solved by linear programming. Finally, a numerical example is provided to show the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
John Cortés-Romero
2013-01-01
Full Text Available The problem of active disturbance rejection control of induction motors is tackled by means of a generalized PI observer based discrete-time control, using the delta operator approach as the methodology of analyzing the sampled time process. In this scheme, model uncertainties and external disturbances are included in a general additive disturbance input which is to be online estimated and subsequently rejected via the controller actions. The observer carries out the disturbance estimation, thus reducing the complexity of the controller design. The controller efficiency is tested via some experimental results, performing a trajectory tracking task under load variations.
Observer-based hyperchaos synchronization in cascaded discrete-time systems
International Nuclear Information System (INIS)
Grassi, Giuseppe
2009-01-01
This paper deals with the observer-based synchronization in a cascade connection of hyperchaotic discrete-time systems. The paper demonstrates that exact synchronization in finite time is achievable between pairs of drive-response systems using only a scalar synchronizing signal. This 'propagated synchronization' starts from the innermost drive-response system pair and propagates toward the outermost drive-system pair. Choosing the drive-system input to be an information signal (encrypted via an arbitrary encryption function) yields a potential application of this architecture in chaos-based communications.
The Iterative Solution to Discrete-Time H∞ Control Problems for Periodic Systems
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Ivan G. Ivanov
2016-03-01
Full Text Available This paper addresses the problem of solving discrete-time H ∞ control problems for periodic systems. The approach for solving such a type of equations is well known in the literature. However, the focus of our research is set on the numerical computation of the stabilizing solution. In particular, two effective methods for practical realization of the known iterative processes are described. Furthermore, a new iterative approach is investigated and applied. On the basis of numerical experiments, we compare the presented methods. A major conclusion is that the new iterative approach is faster than rest of the methods and it uses less RAM memory than other methods.
Parent-child communication and marijuana initiation: evidence using discrete-time survival analysis.
Nonnemaker, James M; Silber-Ashley, Olivia; Farrelly, Matthew C; Dench, Daniel
2012-12-01
This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or - in the case of youth reports of communication - potentially harmful (leading to increased likelihood of marijuana initiation). Copyright © 2012 Elsevier Ltd. All rights reserved.
A VHDL Core for Intrinsic Evolution of Discrete Time Filters with Signal Feedback
Gwaltney, David A.; Dutton, Kenneth
2005-01-01
The design of an Evolvable Machine VHDL Core is presented, representing a discrete-time processing structure capable of supporting control system applications. This VHDL Core is implemented in an FPGA and is interfaced with an evolutionary algorithm implemented in firmware on a Digital Signal Processor (DSP) to create an evolvable system platform. The salient features of this architecture are presented. The capability to implement IIR filter structures is presented along with the results of the intrinsic evolution of a filter. The robustness of the evolved filter design is tested and its unique characteristics are described.
A Spectral Analysis of Discrete-Time Quantum Walks Related to the Birth and Death Chains
Ho, Choon-Lin; Ide, Yusuke; Konno, Norio; Segawa, Etsuo; Takumi, Kentaro
2018-04-01
In this paper, we consider a spectral analysis of discrete time quantum walks on the path. For isospectral coin cases, we show that the time averaged distribution and stationary distributions of the quantum walks are described by the pair of eigenvalues of the coins as well as the eigenvalues and eigenvectors of the corresponding random walks which are usually referred as the birth and death chains. As an example of the results, we derive the time averaged distribution of so-called Szegedy's walk which is related to the Ehrenfest model. It is represented by Krawtchouk polynomials which is the eigenvectors of the model and includes the arcsine law.
Optimal control of LQR for discrete time-varying systems with input delays
Yin, Yue-Zhu; Yang, Zhong-Lian; Yin, Zhi-Xiang; Xu, Feng
2018-04-01
In this work, we consider the optimal control problem of linear quadratic regulation for discrete time-variant systems with single input and multiple input delays. An innovative and simple method to derive the optimal controller is given. The studied problem is first equivalently converted into a problem subject to a constraint condition. Last, with the established duality, the problem is transformed into a static mathematical optimisation problem without input delays. The optimal control input solution to minimise performance index function is derived by solving this optimisation problem with two methods. A numerical simulation example is carried out and its results show that our two approaches are both feasible and very effective.
Directory of Open Access Journals (Sweden)
Yueyang Li
2014-01-01
Full Text Available This paper investigates the H∞ fixed-lag fault estimator design for linear discrete time-varying (LDTV systems with intermittent measurements, which is described by a Bernoulli distributed random variable. Through constructing a novel partially equivalent dynamic system, the fault estimator design is converted into a deterministic quadratic minimization problem. By applying the innovation reorganization technique and the projection formula in Krein space, a necessary and sufficient condition is obtained for the existence of the estimator. The parameter matrices of the estimator are derived by recursively solving two standard Riccati equations. An illustrative example is provided to show the effectiveness and applicability of the proposed algorithm.
On the Solution of the Eigenvalue Assignment Problem for Discrete-Time Systems
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El-Sayed M. E. Mostafa
2017-01-01
Full Text Available The output feedback eigenvalue assignment problem for discrete-time systems is considered. The problem is formulated first as an unconstrained minimization problem, where a three-term nonlinear conjugate gradient method is proposed to find a local solution. In addition, a cut to the objective function is included, yielding an inequality constrained minimization problem, where a logarithmic barrier method is proposed for finding the local solution. The conjugate gradient method is further extended to tackle the eigenvalue assignment problem for the two cases of decentralized control systems and control systems with time delay. The performance of the methods is illustrated through various test examples.
Synchronization of discrete-time spatiotemporal chaos via adaptive fuzzy control
International Nuclear Information System (INIS)
Xue Yueju; Yang Shiyuan
2003-01-01
A discrete-time adaptive fuzzy control scheme is presented to synchronize model-unknown coupled Henon-map lattices (CHMLs). The proposed method is robust to approximate errors, parameter mismatches and disturbances, because it integrates the merits of the adaptive fuzzy systems and the variable structure control with a sector. The simulation results of synchronization of CHMLs show that it not only can synchronize model-unknown CHMLs but also is robust against parameter mismatches and noise of the systems. These merits are advantageous for engineering realization
Synchronization of discrete-time spatiotemporal chaos via adaptive fuzzy control
Energy Technology Data Exchange (ETDEWEB)
Xue Yueju E-mail: xueyj@mail.tsinghua.edu.cn; Yang Shiyuan E-mail: ysy-dau@tsinghua.edu.cn
2003-08-01
A discrete-time adaptive fuzzy control scheme is presented to synchronize model-unknown coupled Henon-map lattices (CHMLs). The proposed method is robust to approximate errors, parameter mismatches and disturbances, because it integrates the merits of the adaptive fuzzy systems and the variable structure control with a sector. The simulation results of synchronization of CHMLs show that it not only can synchronize model-unknown CHMLs but also is robust against parameter mismatches and noise of the systems. These merits are advantageous for engineering realization.
Discrete-Time Domain Modelling of Voltage Source Inverters in Standalone Applications
DEFF Research Database (Denmark)
Federico, de Bosio; de Sousa Ribeiro, Luiz Antonio; Freijedo Fernandez, Francisco Daniel
2017-01-01
modelling of the LC plant with consideration of delay and sample-and-hold effects on the state feedback cross-coupling decoupling is derived. From this plant formulation, current controllers with wide bandwidth and good relative stability properties are developed. Two controllers based on lead compensation......The decoupling of the capacitor voltage and inductor current has been shown to improve significantly the dynamic performance of voltage source inverters in standalone applications. However, the computation and PWM delays still limit the achievable bandwidth. In this paper a discrete-time domain...
Finite-Time Stability Analysis of Discrete-Time Linear Singular Systems
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Songlin Wo
2014-01-01
Full Text Available The finite-time stability (FTS problem of discrete-time linear singular systems (DTLSS is considered in this paper. A necessary and sufficient condition for FTS is obtained, which can be expressed in terms of matrix inequalities. Then, another form of the necessary and sufficient condition for FTS is also given by using matrix-null space technology. In order to solve the stability problem expediently, a sufficient condition for FTS is given via linear matrix inequality (LMI approach; this condition can be expressed in terms of LMIs. Finally, an illustrating example is also given to show the effectiveness of the proposed method.
Persistence of non-Markovian Gaussian stationary processes in discrete time
Nyberg, Markus; Lizana, Ludvig
2018-04-01
The persistence of a stochastic variable is the probability that it does not cross a given level during a fixed time interval. Although persistence is a simple concept to understand, it is in general hard to calculate. Here we consider zero mean Gaussian stationary processes in discrete time n . Few results are known for the persistence P0(n ) in discrete time, except the large time behavior which is characterized by the nontrivial constant θ through P0(n ) ˜θn . Using a modified version of the independent interval approximation (IIA) that we developed before, we are able to calculate P0(n ) analytically in z -transform space in terms of the autocorrelation function A (n ) . If A (n )→0 as n →∞ , we extract θ numerically, while if A (n )=0 , for finite n >N , we find θ exactly (within the IIA). We apply our results to three special cases: the nearest-neighbor-correlated "first order moving average process", where A (n )=0 for n >1 , the double exponential-correlated "second order autoregressive process", where A (n ) =c1λ1n+c2λ2n , and power-law-correlated variables, where A (n ) ˜n-μ . Apart from the power-law case when μ <5 , we find excellent agreement with simulations.
Discrete-time retrial queue with Bernoulli vacation, preemptive resume and feedback customers
Directory of Open Access Journals (Sweden)
Peishu Chen
2015-09-01
Full Text Available Purpose: We consider a discrete-time Geo/G/1 retrial queue where the retrial time follows a general distribution, the server subject to Bernoulli vacation policy and the customer has preemptive resume priority, Bernoulli feedback strategy. The main purpose of this paper is to derive the generating functions of the stationary distribution of the system state, the orbit size and some important performance measures. Design/methodology: Using probability generating function technique, some valuable and interesting performance measures of the system are obtained. We also investigate two stochastic decomposition laws and present some numerical results. Findings: We obtain the probability generating functions of the system state distribution as well as those of the orbit size and the system size distributions. We also obtain some analytical expressions for various performance measures such as idle and busy probabilities, mean orbit and system sizes. Originality/value: The analysis of discrete-time retrial queues with Bernoulli vacation, preemptive resume and feedback customers is interesting and to the best of our knowledge, no other scientific journal paper has dealt with this question. This fact gives the reason why efforts should be taken to plug this gap.
Models for discrete-time self-similar vector processes with application to network traffic
Lee, Seungsin; Rao, Raghuveer M.; Narasimha, Rajesh
2003-07-01
The paper defines self-similarity for vector processes by employing the discrete-time continuous-dilation operation which has successfully been used previously by the authors to define 1-D discrete-time stochastic self-similar processes. To define self-similarity of vector processes, it is required to consider the cross-correlation functions between different 1-D processes as well as the autocorrelation function of each constituent 1-D process in it. System models to synthesize self-similar vector processes are constructed based on the definition. With these systems, it is possible to generate self-similar vector processes from white noise inputs. An important aspect of the proposed models is that they can be used to synthesize various types of self-similar vector processes by choosing proper parameters. Additionally, the paper presents evidence of vector self-similarity in two-channel wireless LAN data and applies the aforementioned systems to simulate the corresponding network traffic traces.
Risk-based design of process systems using discrete-time Bayesian networks
International Nuclear Information System (INIS)
Khakzad, Nima; Khan, Faisal; Amyotte, Paul
2013-01-01
Temporal Bayesian networks have gained popularity as a robust technique to model dynamic systems in which the components' sequential dependency, as well as their functional dependency, cannot be ignored. In this regard, discrete-time Bayesian networks have been proposed as a viable alternative to solve dynamic fault trees without resort to Markov chains. This approach overcomes the drawbacks of Markov chains such as the state-space explosion and the error-prone conversion procedure from dynamic fault tree. It also benefits from the inherent advantages of Bayesian networks such as probability updating. However, effective mapping of the dynamic gates of dynamic fault trees into Bayesian networks while avoiding the consequent huge multi-dimensional probability tables has always been a matter of concern. In this paper, a new general formalism has been developed to model two important elements of dynamic fault tree, i.e., cold spare gate and sequential enforcing gate, with any arbitrary probability distribution functions. Also, an innovative Neutral Dependency algorithm has been introduced to model dynamic gates such as priority-AND gate, thus reducing the dimension of conditional probability tables by an order of magnitude. The second part of the paper is devoted to the application of discrete-time Bayesian networks in the risk assessment and safety analysis of complex process systems. It has been shown how dynamic techniques can effectively be applied for optimal allocation of safety systems to obtain maximum risk reduction.
International Nuclear Information System (INIS)
Gómez de León, F C; Meroño Pérez, P A
2010-01-01
The traditional method for measuring the velocity and the angular vibration in the shaft of rotating machines using incremental encoders is based on counting the pulses at given time intervals. This method is generically called the time interval measurement system (TIMS). A variant of this method that we have developed in this work consists of measuring the corresponding time of each pulse from the encoder and sampling the signal by means of an A/D converter as if it were an analog signal, that is to say, in discrete time. For this reason, we have denominated this method as the discrete time interval measurement system (DTIMS). This measurement system provides a substantial improvement in the precision and frequency resolution compared with the traditional method of counting pulses. In addition, this method permits modification of the width of some pulses in order to obtain a mark-phase on every lap. This paper explains the theoretical fundamentals of the DTIMS and its application for measuring the angular vibrations of rotating machines. It also displays the required relationship between the sampling rate of the signal, the number of pulses of the encoder and the rotating velocity in order to obtain the required resolution and to delimit the methodological errors in the measurement
Directory of Open Access Journals (Sweden)
Mengjuan Cao
2014-01-01
Full Text Available The linear discrete-time descriptor noncausal multirate system is considered for the presentation of a new design approach for optimal preview control. First, according to the characteristics of causal controllability and causal observability, the descriptor noncausal system is constructed into a descriptor causal closed-loop system. Second, by using the characteristics of the causal system and elementary transformation, the descriptor causal closed-loop system is transformed into a normal system. Then, taking advantage of the discrete lifting technique, the normal multirate system is converted to a single-rate system. By making use of the standard preview control method, we construct the descriptor augmented error system. The quadratic performance index for the multirate system is given, which can be changed into one for the single-rate system. In addition, a new single-rate system is obtained, the optimal control law of which is given. Returning to the original system, the optimal preview controller for linear discrete-time descriptor noncausal multirate systems is derived. The stabilizability and detectability of the lifted single-rate system are discussed in detail. The optimal preview control design techniques are illustrated by simulation results for a simple example.
Cai, Chao-Ran; Wu, Zhi-Xi; Guan, Jian-Yue
2014-11-01
Recently, Gómez et al. proposed a microscopic Markov-chain approach (MMCA) [S. Gómez, J. Gómez-Gardeñes, Y. Moreno, and A. Arenas, Phys. Rev. E 84, 036105 (2011)PLEEE81539-375510.1103/PhysRevE.84.036105] to the discrete-time susceptible-infected-susceptible (SIS) epidemic process and found that the epidemic prevalence obtained by this approach agrees well with that by simulations. However, we found that the approach cannot be straightforwardly extended to a susceptible-infected-recovered (SIR) epidemic process (due to its irreversible property), and the epidemic prevalences obtained by MMCA and Monte Carlo simulations do not match well when the infection probability is just slightly above the epidemic threshold. In this contribution we extend the effective degree Markov-chain approach, proposed for analyzing continuous-time epidemic processes [J. Lindquist, J. Ma, P. Driessche, and F. Willeboordse, J. Math. Biol. 62, 143 (2011)JMBLAJ0303-681210.1007/s00285-010-0331-2], to address discrete-time binary-state (SIS) or three-state (SIR) epidemic processes on uncorrelated complex networks. It is shown that the final epidemic size as well as the time series of infected individuals obtained from this approach agree very well with those by Monte Carlo simulations. Our results are robust to the change of different parameters, including the total population size, the infection probability, the recovery probability, the average degree, and the degree distribution of the underlying networks.
A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network
International Nuclear Information System (INIS)
Fan, Dongming; Wang, Zili; Liu, Linlin; Ren, Yi
2016-01-01
Highlights: • Identification of particular causes of failure for common cause failure analysis. • Comparison two formalisms (GO-FLOW and Discrete Time Bayesian network) and establish the correlation between them. • Mapping the GO-FLOW model into Bayesian network model. • Calculated GO-FLOW model with common cause failures based on DTBN. - Abstract: The GO-FLOW methodology is a success-oriented system reliability modelling technique for multi-phase missions involving complex time-dependent, multi-state and common cause failure (CCF) features. However, the analysis algorithm cannot easily handle the multiple shared signals and CCFs. In addition, the simulative algorithm is time consuming when vast multi-state components exist in the model, and the multiple time points of phased mission problems increases the difficulty of the analysis method. In this paper, the Discrete Time Bayesian Network (DTBN) and the GO-FLOW methodology are integrated by the unified mapping rules. Based on these rules, the multi operators can be mapped into DTBN followed by, a complete GO-FLOW model with complex characteristics (e.g. phased mission, multi-state, and CCF) can be converted to the isomorphic DTBN and easily analyzed by utilizing the DTBN. With mature algorithms and tools, the multi-phase mission reliability parameter can be efficiently obtained via the proposed approach without considering the shared signals and the various complex logic operation. Meanwhile, CCF can also arise in the computing process.
Frasca, Mattia; Sharkey, Kieran J
2016-06-21
Understanding the dynamics of spread of infectious diseases between individuals is essential for forecasting the evolution of an epidemic outbreak or for defining intervention policies. The problem is addressed by many approaches including stochastic and deterministic models formulated at diverse scales (individuals, populations) and different levels of detail. Here we consider discrete-time SIR (susceptible-infectious-removed) dynamics propagated on contact networks. We derive a novel set of 'discrete-time moment equations' for the probability of the system states at the level of individual nodes and pairs of nodes. These equations form a set which we close by introducing appropriate approximations of the joint probabilities appearing in them. For the example case of SIR processes, we formulate two types of model, one assuming statistical independence at the level of individuals and one at the level of pairs. From the pair-based model we then derive a model at the level of the population which captures the behavior of epidemics on homogeneous random networks. With respect to their continuous-time counterparts, the models include a larger number of possible transitions from one state to another and joint probabilities with a larger number of individuals. The approach is validated through numerical simulation over different network topologies. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.
A discrete-time queueing system with changes in the vacation times
Directory of Open Access Journals (Sweden)
Atencia Ivan
2016-06-01
Full Text Available This paper considers a discrete-time queueing system in which an arriving customer can decide to follow a last come first served (LCFS service discipline or to become a negative customer that eliminates the one at service, if any. After service completion, the server can opt for a vacation time or it can remain on duty. Changes in the vacation times as well as their associated distribution are thoroughly studied. An extensive analysis of the system is carried out and, using a probability generating function approach, steady-state performance measures such as the first moments of the busy period of the queue content and of customers delay are obtained. Finally, some numerical examples to show the influence of the parameters on several performance characteristics are given.
A Discrete-Time Queue with Balking, Reneging, and Working Vacations
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Veena Goswami
2014-01-01
Full Text Available This paper presents an analysis of balking and reneging in finite-buffer discrete-time single server queue with single and multiple working vacations. An arriving customer may balk with a probability or renege after joining according to a geometric distribution. The server works with different service rates rather than completely stopping the service during a vacation period. The service times during a busy period, vacation period, and vacation times are assumed to be geometrically distributed. We find the explicit expressions for the stationary state probabilities. Various system performance measures and a cost model to determine the optimal service rates are presented. Moreover, some queueing models presented in the literature are derived as special cases of our model. Finally, the influence of various parameters on the performance characteristics is shown numerically.
Design of an optimal preview controller for linear discrete-time descriptor systems with state delay
Cao, Mengjuan; Liao, Fucheng
2015-04-01
In this paper, the linear discrete-time descriptor system with state delay is studied, and a design method for an optimal preview controller is proposed. First, by using the discrete lifting technique, the original system is transformed into a general descriptor system without state delay in form. Then, taking advantage of the first-order forward difference operator, we construct a descriptor augmented error system, including the state vectors of the lifted system, error vectors, and desired target signals. Rigorous mathematical proofs are given for the regularity, stabilisability, causal controllability, and causal observability of the descriptor augmented error system. Based on these, the optimal preview controller with preview feedforward compensation for the original system is obtained by using the standard optimal regulator theory of the descriptor system. The effectiveness of the proposed method is shown by numerical simulation.
Generation of a quantum integrable class of discrete-time or relativistic periodic Toda chains
International Nuclear Information System (INIS)
Kundu, Anjan
1994-01-01
A new integrable class of quantum models representing a family of different discrete-time or relativistic generalisations of the periodic Toda chain (TC), including that of a recently proposed classical model close to TC [Lett. Math. Phys. 29 (1993) 165] is presented. All such models are shown to be obtainable from a single ancestor model at different realisations of the underlying quantised algebra. As a consequence the 2x2 Lax operators and the associated quantum R-matrices for these models are easily derived ensuring their quantum integrability. It is shown that the functional Bethe ansatz developed for the quantum TC is trivially generalised to achieve separation of variables also for the present models. ((orig.))
Sun, Ying; Ding, Derui; Zhang, Sunjie; Wei, Guoliang; Liu, Hongjian
2018-07-01
In this paper, the non-fragile ?-? control problem is investigated for a class of discrete-time stochastic nonlinear systems under event-triggered communication protocols, which determine whether the measurement output should be transmitted to the controller or not. The main purpose of the addressed problem is to design an event-based output feedback controller subject to gain variations guaranteeing the prescribed disturbance attenuation level described by the ?-? performance index. By utilizing the Lyapunov stability theory combined with S-procedure, a sufficient condition is established to guarantee both the exponential mean-square stability and the ?-? performance for the closed-loop system. In addition, with the help of the orthogonal decomposition, the desired controller parameter is obtained in terms of the solution to certain linear matrix inequalities. Finally, a simulation example is exploited to demonstrate the effectiveness of the proposed event-based controller design scheme.
Stability Analysis and H∞ Model Reduction for Switched Discrete-Time Time-Delay Systems
Directory of Open Access Journals (Sweden)
Zheng-Fan Liu
2014-01-01
Full Text Available This paper is concerned with the problem of exponential stability and H∞ model reduction of a class of switched discrete-time systems with state time-varying delay. Some subsystems can be unstable. Based on the average dwell time technique and Lyapunov-Krasovskii functional (LKF approach, sufficient conditions for exponential stability with H∞ performance of such systems are derived in terms of linear matrix inequalities (LMIs. For the high-order systems, sufficient conditions for the existence of reduced-order model are derived in terms of LMIs. Moreover, the error system is guaranteed to be exponentially stable and an H∞ error performance is guaranteed. Numerical examples are also given to demonstrate the effectiveness and reduced conservatism of the obtained results.
Mode locking and quasiperiodicity in a discrete-time Chialvo neuron model
Wang, Fengjuan; Cao, Hongjun
2018-03-01
The two-dimensional parameter spaces of a discrete-time Chialvo neuron model are investigated. Our studies demonstrate that for all our choice of two parameters (i) the fixed point is destabilized via Neimark-Sacker bifurcation; (ii) there exist mode locking structures like Arnold tongues and shrimps, with periods organized in a Farey tree sequence, embedded in quasiperiodic/chaotic region. We determine analytically the location of the parameter sets where Neimark-Sacker bifurcation occurs, and the location on this curve where Arnold tongues of arbitrary period are born. Properties of the transition that follows the so-called two-torus from quasiperiodicity to chaos are presented clearly and proved strictly by using numerical simulations such as bifurcation diagrams, the largest Lyapunov exponent diagram on MATLAB and C++.
Switched periodic systems in discrete time: stability and input-output norms
Bolzern, Paolo; Colaneri, Patrizio
2013-07-01
This paper deals with the analysis of stability and the characterisation of input-output norms for discrete-time periodic switched linear systems. Such systems consist of a network of time-periodic linear subsystems sharing the same state vector and an exogenous switching signal that triggers the jumps between the subsystems. The overall system exhibits a complex dynamic behaviour due to the interplay between the time periodicity of the subsystem parameters and the switching signal. Both arbitrary switching signals and signals satisfying a dwell-time constraint are considered. Linear matrix inequality conditions for stability and guaranteed H2 and H∞ performances are provided. The results heavily rely on the merge of the theory of linear periodic systems and recent developments on switched linear time-invariant systems.
Time-dependent switched discrete-time linear systems control and filtering
Zhang, Lixian; Shi, Peng; Lu, Qiugang
2016-01-01
This book focuses on the basic control and filtering synthesis problems for discrete-time switched linear systems under time-dependent switching signals. Chapter 1, as an introduction of the book, gives the backgrounds and motivations of switched systems, the definitions of the typical time-dependent switching signals, the differences and links to other types of systems with hybrid characteristics and a literature review mainly on the control and filtering for the underlying systems. By summarizing the multiple Lyapunov-like functions (MLFs) approach in which different requirements on comparisons of Lyapunov function values at switching instants, a series of methodologies are developed for the issues on stability and stabilization, and l2-gain performance or tube-based robustness for l∞ disturbance, respectively, in Chapters 2 and 3. Chapters 4 and 5 are devoted to the control and filtering problems for the time-dependent switched linear systems with either polytopic uncertainties or measurable time-varying...
Fault-tolerant Control of Discrete-time LPV systems using Virtual Actuators and Sensors
DEFF Research Database (Denmark)
Tabatabaeipour, Mojtaba; Stoustrup, Jakob; Bak, Thomas
2015-01-01
This paper proposes a new fault-tolerant control (FTC) method for discrete-time linear parameter varying (LPV) systems using a reconfiguration block. The basic idea of the method is to achieve the FTC goal without re-designing the nominal controller by inserting a reconfiguration block between......, it transforms the output of the controller for the faulty system such that the stability and performance goals are preserved. Input-to-state stabilizing LPV gains of the virtual actuator and sensor are obtained by solving linear matrix inequalities (LMIs). We show that separate design of these gains guarantees....... Finally, the effectiveness of the method is demonstrated via a numerical example and stator current control of an induction motor....
Optimal maintenance policy for a system subject to damage in a discrete time process
International Nuclear Information System (INIS)
Chien, Yu-Hung; Sheu, Shey-Huei; Zhang, Zhe George
2012-01-01
Consider a system operating over n discrete time periods (n=1, 2, …). Each operation period causes a random amount of damage to the system which accumulates over time periods. The system fails when the cumulative damage exceeds a failure level ζ and a corrective maintenance (CM) action is immediately taken. To prevent such a failure, a preventive maintenance (PM) may be performed. In an operation period without a CM or PM, a regular maintenance (RM) is conducted at the end of that period to maintain the operation of the system. We propose a maintenance policy which prescribes a PM when the accumulated damage exceeds a pre-specified level δ ( ⁎ and N ⁎ and discuss some useful properties about them. It has been shown that a δ-based PM outperforms a N-based PM in terms of cost minimization. Numerical examples are presented to demonstrate the optimization of this class of maintenance policies.
Simultaneous Robust Fault and State Estimation for Linear Discrete-Time Uncertain Systems
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Feten Gannouni
2017-01-01
Full Text Available We consider the problem of robust simultaneous fault and state estimation for linear uncertain discrete-time systems with unknown faults which affect both the state and the observation matrices. Using transformation of the original system, a new robust proportional integral filter (RPIF having an error variance with an optimized guaranteed upper bound for any allowed uncertainty is proposed to improve robust estimation of unknown time-varying faults and to improve robustness against uncertainties. In this study, the minimization problem of the upper bound of the estimation error variance is formulated as a convex optimization problem subject to linear matrix inequalities (LMI for all admissible uncertainties. The proportional and the integral gains are optimally chosen by solving the convex optimization problem. Simulation results are given in order to illustrate the performance of the proposed filter, in particular to solve the problem of joint fault and state estimation.
Nonlinear Estimation of Discrete-Time Signals Under Random Observation Delay
International Nuclear Information System (INIS)
Caballero-Aguila, R.; Jimenez-Lopez, J. D.; Hermoso-Carazo, A.; Linares-Perez, J.; Nakamori, S.
2008-01-01
This paper presents an approximation to the nonlinear least-squares estimation problem of discrete-time stochastic signals using nonlinear observations with additive white noise which can be randomly delayed by one sampling time. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values, zero or one, indicate that the real observation arrives on time or it is delayed and, hence, the available measurement to estimate the signal is not up-to-date. Assuming that the state-space model generating the signal is unknown and only the covariance functions of the processes involved in the observation equation are ready for use, a filtering algorithm based on linear approximations of the real observations is proposed.
Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems
Mahdi Alavi, S. M.; Saif, Mehrdad
2013-12-01
This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.
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Saïda Bedoui
2013-01-01
Full Text Available This paper addresses the problem of simultaneous identification of linear discrete time delay multivariable systems. This problem involves both the estimation of the time delays and the dynamic parameters matrices. In fact, we suggest a new formulation of this problem allowing defining the time delay and the dynamic parameters in the same estimated vector and building the corresponding observation vector. Then, we use this formulation to propose a new method to identify the time delays and the parameters of these systems using the least square approach. Convergence conditions and statistics properties of the proposed method are also developed. Simulation results are presented to illustrate the performance of the proposed method. An application of the developed approach to compact disc player arm is also suggested in order to validate simulation results.
Consensus of discrete-time multi-agent systems with adversaries and time delays
Wu, Yiming; He, Xiongxiong; Liu, Shuai; Xie, Lihua
2014-05-01
This paper studies the resilient asymptotic consensus problem for discrete-time multi-agent systems in the presence of adversaries and transmission delays. The network is assumed to have ? loyal agents and ? adversarial agents, and each loyal agent in the network has no knowledge of the network topology other than an upper bound on the number of adversarial agents in its neighborhood. For the considered networked system, only locally delayed information is available for each loyal agent, and also the information flow is directed and a control protocol using only local information is designed to guarantee the realization of consensus with respect to communication graph, which satisfies a featured network robustness. Numerical examples are finally given to demonstrate the effectiveness of theoretical results.
Multiple periodic solutions for a discrete time model of plankton allelopathy
Zhang Jianbao; Fang Hui
2006-01-01
We study a discrete time model of the growth of two species of plankton with competitive and allelopathic effects on each other N1(k+1) = N1(k)exp{r1(k)-a11(k)N1(k)-a12(k)N2(k)-b1(k)N1(k)N2(k)}, N2(k+1) = N2(k)exp{r2(k)-a21(k)N2(k)-b2(k)N1(k)N1(k)N2(k)}. A set of sufficient conditions is obtained for the existence of multiple positive periodic solutions for this model. The approach is based on Mawhin's continuation theorem of coincidence degree theory as well as some a priori estimates. Some...
Bifurcations in a discrete time model composed of Beverton-Holt function and Ricker function.
Shang, Jin; Li, Bingtuan; Barnard, Michael R
2015-05-01
We provide rigorous analysis for a discrete-time model composed of the Ricker function and Beverton-Holt function. This model was proposed by Lewis and Li [Bull. Math. Biol. 74 (2012) 2383-2402] in the study of a population in which reproduction occurs at a discrete instant of time whereas death and competition take place continuously during the season. We show analytically that there exists a period-doubling bifurcation curve in the model. The bifurcation curve divides the parameter space into the region of stability and the region of instability. We demonstrate through numerical bifurcation diagrams that the regions of periodic cycles are intermixed with the regions of chaos. We also study the global stability of the model. Copyright © 2015 Elsevier Inc. All rights reserved.
LQR-Based Optimal Distributed Cooperative Design for Linear Discrete-Time Multiagent Systems.
Zhang, Huaguang; Feng, Tao; Liang, Hongjing; Luo, Yanhong
2017-03-01
In this paper, a novel linear quadratic regulator (LQR)-based optimal distributed cooperative design method is developed for synchronization control of general linear discrete-time multiagent systems on a fixed, directed graph. Sufficient conditions are derived for synchronization, which restrict the graph eigenvalues into a bounded circular region in the complex plane. The synchronizing speed issue is also considered, and it turns out that the synchronizing region reduces as the synchronizing speed becomes faster. To obtain more desirable synchronizing capacity, the weighting matrices are selected by sufficiently utilizing the guaranteed gain margin of the optimal regulators. Based on the developed LQR-based cooperative design framework, an approximate dynamic programming technique is successfully introduced to overcome the (partially or completely) model-free cooperative design for linear multiagent systems. Finally, two numerical examples are given to illustrate the effectiveness of the proposed design methods.
Decentralized Observer with a Consensus Filter for Distributed Discrete-Time Linear Systems
Acikmese, Behcet; Mandic, Milan
2011-01-01
This paper presents a decentralized observer with a consensus filter for the state observation of a discrete-time linear distributed systems. In this setup, each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors' estimates. We assume that the communication graph is connected for all times as well as the sensing graph. It is proven that the state estimates of the proposed observer asymptotically converge to the actual plant states under arbitrarily changing, but connected, communication and sensing topologies. As a byproduct of this research, we also obtained a result on the location of eigenvalues, the spectrum, of the Laplacian for a family of graphs with self-loops.
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Yunjie Wu
2013-01-01
Full Text Available In order to improve the tracking accuracy of flight simulator and expend its frequency response, a multirate-sampling-method-based discrete-time chattering free sliding mode control is developed and imported into the systems. By constructing the multirate sampling sliding mode controller, the flight simulator can perfectly track a given reference signal with an arbitrarily small dynamic tracking error, and the problems caused by a contradiction of reference signal period and control period in traditional design method can be eliminated. It is proved by theoretical analysis that the extremely high dynamic tracking precision can be obtained. Meanwhile, the robustness is guaranteed by sliding mode control even though there are modeling mismatch, external disturbances and measure noise. The validity of the proposed method is confirmed by experiments on flight simulator.
Transformation of nonlinear discrete-time system into the extended observer form
Kaparin, V.; Kotta, Ü.
2018-04-01
The paper addresses the problem of transforming discrete-time single-input single-output nonlinear state equations into the extended observer form, which, besides the input and output, also depends on a finite number of their past values. Necessary and sufficient conditions for the existence of both the extended coordinate and output transformations, solving the problem, are formulated in terms of differential one-forms, associated with the input-output equation, corresponding to the state equations. An algorithm for transformation of state equations into the extended observer form is proposed and illustrated by an example. Moreover, the considered approach is compared with the method of dynamic observer error linearisation, which likewise is intended to enlarge the class of systems transformable into an observer form.
Bifurcation and complex dynamics of a discrete-time predator-prey system involving group defense
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S. M. Sohel Rana
2015-09-01
Full Text Available In this paper, we investigate the dynamics of a discrete-time predator-prey system involving group defense. The existence and local stability of positive fixed point of the discrete dynamical system is analyzed algebraically. It is shown that the system undergoes a flip bifurcation and a Neimark-Sacker bifurcation in the interior of R+2 by using bifurcation theory. Numerical simulation results not only show the consistence with the theoretical analysis but also display the new and interesting dynamical behaviors, including phase portraits, period-7, 20-orbits, attracting invariant circle, cascade of period-doubling bifurcation from period-20 leading to chaos, quasi-periodic orbits, and sudden disappearance of the chaotic dynamics and attracting chaotic set. The Lyapunov exponents are numerically computed to characterize the complexity of the dynamical behaviors.
Homogenous polynomially parameter-dependent H∞ filter designs of discrete-time fuzzy systems.
Zhang, Huaguang; Xie, Xiangpeng; Tong, Shaocheng
2011-10-01
This paper proposes a novel H(∞) filtering technique for a class of discrete-time fuzzy systems. First, a novel kind of fuzzy H(∞) filter, which is homogenous polynomially parameter dependent on membership functions with an arbitrary degree, is developed to guarantee the asymptotic stability and a prescribed H(∞) performance of the filtering error system. Second, relaxed conditions for H(∞) performance analysis are proposed by using a new fuzzy Lyapunov function and the Finsler lemma with homogenous polynomial matrix Lagrange multipliers. Then, based on a new kind of slack variable technique, relaxed linear matrix inequality-based H(∞) filtering conditions are proposed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed approach.
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Liyun Su
2011-01-01
Full Text Available In order to suppress the interference of the strong fractional noise signal in discrete-time ultrawideband (UWB systems, this paper presents a new UWB multi-scale Kalman filter (KF algorithm for the interference suppression. This approach solves the problem of the narrowband interference (NBI as nonstationary fractional signal in UWB communication, which does not need to estimate any channel parameter. In this paper, the received sampled signal is transformed through multiscale wavelet to obtain a state transition equation and an observation equation based on the stationarity theory of wavelet coefficients in time domain. Then through the Kalman filter method, fractional signal of arbitrary scale is easily figured out. Finally, fractional noise interference is subtracted from the received signal. Performance analysis and computer simulations reveal that this algorithm is effective to reduce the strong fractional noise when the sampling rate is low.
Fault detection for discrete-time LPV systems using interval observers
Zhang, Zhi-Hui; Yang, Guang-Hong
2017-10-01
This paper is concerned with the fault detection (FD) problem for discrete-time linear parameter-varying systems subject to bounded disturbances. A parameter-dependent FD interval observer is designed based on parameter-dependent Lyapunov and slack matrices. The design method is presented by translating the parameter-dependent linear matrix inequalities (LMIs) into finite ones. In contrast to the existing results based on parameter-independent and diagonal Lyapunov matrices, the derived disturbance attenuation, fault sensitivity and nonnegative conditions lead to less conservative LMI characterisations. Furthermore, without the need to design the residual evaluation functions and thresholds, the residual intervals generated by the interval observers are used directly for FD decision. Finally, simulation results are presented for showing the effectiveness and superiority of the proposed method.
Variable speed wind turbine control by discrete-time sliding mode approach.
Torchani, Borhen; Sellami, Anis; Garcia, Germain
2016-05-01
The aim of this paper is to propose a new design variable speed wind turbine control by discrete-time sliding mode approach. This methodology is designed for linear saturated system. The saturation constraint is reported on inputs vector. To this end, the back stepping design procedure is followed to construct a suitable sliding manifold that guarantees the attainment of a stabilization control objective. It is well known that the mechanisms are investigated in term of the most proposed assumptions to deal with the damping, shaft stiffness and inertia effect of the gear. The objectives are to synthesize robust controllers that maximize the energy extracted from wind, while reducing mechanical loads and rotor speed tracking combined with an electromagnetic torque. Simulation results of the proposed scheme are presented. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
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Jian Ding
2014-01-01
Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.
International Nuclear Information System (INIS)
Banu, L Jarina; Balasubramaniam, P
2015-01-01
This paper investigates the problem of non-fragile observer design for a class of discrete-time genetic regulatory networks (DGRNs) with time-varying delays and randomly occurring uncertainties. A non-fragile observer is designed, for estimating the true concentration of mRNAs and proteins from available measurement outputs. One important feature of the results obtained that are reported here is that the parameter uncertainties are assumed to be random and their probabilities of occurrence are known a priori. On the basis of the Lyapunov–Krasovskii functional approach and using a convex combination technique, a delay-dependent estimation criterion is established for DGRNs in terms of linear matrix inequalities (LMIs) that can be efficiently solved using any available LMI solver. Finally numerical examples are provided to substantiate the theoretical results. (paper)
Frequency-shaped and observer-based discrete-time sliding mode control
Mehta, Axaykumar
2015-01-01
It is well established that the sliding mode control strategy provides an effective and robust method of controlling the deterministic system due to its well-known invariance property to a class of bounded disturbance and parameter variations. Advances in microcomputer technologies have made digital control increasingly popular among the researchers worldwide. And that led to the study of discrete-time sliding mode control design and its implementation. This brief presents, a method for multi-rate frequency shaped sliding mode controller design based on switching and non-switching type of reaching law. In this approach, the frequency dependent compensator dynamics are introduced through a frequency-shaped sliding surface by assigning frequency dependent weighing matrices in a linear quadratic regulator (LQR) design procedure. In this way, the undesired high frequency dynamics or certain frequency disturbance can be eliminated. The states are implicitly obtained by measuring the output at a faster rate than th...
Arnold tongues and the Devil's Staircase in a discrete-time Hindmarsh–Rose neuron model
International Nuclear Information System (INIS)
Felicio, Carolini C.; Rech, Paulo C.
2015-01-01
We investigate a three-dimensional discrete-time dynamical system, described by a three-dimensional map derived from a continuous-time Hindmarsh–Rose neuron model by the forward Euler method. For a fixed integration step size, we report a two-dimensional parameter-space for this system, where periodic structures, the so-called Arnold tongues, can be seen with periods organized in a Farey tree sequence. We also report possible modifications in this parameter-space, as a function of the integration step size. - Highlights: • We investigate the parameter-space of a particular 3D map. • Periodic structures, namely Arnold tongues, can be seen there. • They are organized in a Farey tree sequence. • The map was derived from a continuous-time Hindmarsh–Rose neuron model. • The forward Euler method was used for such purpose.
Identification of a parametric, discrete-time model of ankle stiffness.
Guarin, Diego L; Jalaleddini, Kian; Kearney, Robert E
2013-01-01
Dynamic ankle joint stiffness defines the relationship between the position of the ankle and the torque acting about it and can be separated into intrinsic and reflex components. Under stationary conditions, intrinsic stiffness can described by a linear second order system while reflex stiffness is described by Hammerstein system whose input is delayed velocity. Given that reflex and intrinsic torque cannot be measured separately, there has been much interest in the development of system identification techniques to separate them analytically. To date, most methods have been nonparametric and as a result there is no direct link between the estimated parameters and those of the stiffness model. This paper presents a novel algorithm for identification of a discrete-time model of ankle stiffness. Through simulations we show that the algorithm gives unbiased results even in the presence of large, non-white noise. Application of the method to experimental data demonstrates that it produces results consistent with previous findings.
Robust extended Kalman filter of discrete-time Markovian jump nonlinear system under uncertain noise
International Nuclear Information System (INIS)
Zhu, Jin; Park, Jun Hong; Lee, Kwan Soo; Spiryagin, Maksym
2008-01-01
This paper examines the problem of robust extended Kalman filter design for discrete -time Markovian jump nonlinear systems with noise uncertainty. Because of the existence of stochastic Markovian switching, the state and measurement equations of underlying system are subject to uncertain noise whose covariance matrices are time-varying or un-measurable instead of stationary. First, based on the expression of filtering performance deviation, admissible uncertainty of noise covariance matrix is given. Secondly, two forms of noise uncertainty are taken into account: Non- Structural and Structural. It is proved by applying game theory that this filter design is a robust mini-max filter. A numerical example shows the validity of the method
Attractors of relaxation discrete-time systems with chaotic dynamics on a fast time scale
International Nuclear Information System (INIS)
Maslennikov, Oleg V.; Nekorkin, Vladimir I.
2016-01-01
In this work, a new type of relaxation systems is considered. Their prominent feature is that they comprise two distinct epochs, one is slow regular motion and another is fast chaotic motion. Unlike traditionally studied slow-fast systems that have smooth manifolds of slow motions in the phase space and fast trajectories between them, in this new type one observes, apart the same geometric objects, areas of transient chaos. Alternating periods of slow regular motions and fast chaotic ones as well as transitions between them result in a specific chaotic attractor with chaos on a fast time scale. We formulate basic properties of such attractors in the framework of discrete-time systems and consider several examples. Finally, we provide an important application of such systems, the neuronal electrical activity in the form of chaotic spike-burst oscillations.
Allee effect in a discrete-time predator-prey system
International Nuclear Information System (INIS)
Celik, Canan; Duman, Oktay
2009-01-01
In this paper, we study the stability of a discrete-time predator-prey system with and without Allee effect. By analyzing both systems, we first obtain local stability conditions of the equilibrium points without the Allee effect and then exhibit the impact of the Allee effect on stability when it is imposed on prey population. We also show the stabilizing effect of Allee effect by numerical simulations and verify that when the prey population is subject to an Allee effect, the trajectory of the solutions approximates to the corresponding equilibrium point much faster. Furthermore, for some fixed parameter values satisfying necessary conditions, we show that the corresponding equilibrium point moves from instability to stability under the Allee effect on prey population.
Road maintenance optimization through a discrete-time semi-Markov decision process
International Nuclear Information System (INIS)
Zhang Xueqing; Gao Hui
2012-01-01
Optimization models are necessary for efficient and cost-effective maintenance of a road network. In this regard, road deterioration is commonly modeled as a discrete-time Markov process such that an optimal maintenance policy can be obtained based on the Markov decision process, or as a renewal process such that an optimal maintenance policy can be obtained based on the renewal theory. However, the discrete-time Markov process cannot capture the real time at which the state transits while the renewal process considers only one state and one maintenance action. In this paper, road deterioration is modeled as a semi-Markov process in which the state transition has the Markov property and the holding time in each state is assumed to follow a discrete Weibull distribution. Based on this semi-Markov process, linear programming models are formulated for both infinite and finite planning horizons in order to derive optimal maintenance policies to minimize the life-cycle cost of a road network. A hypothetical road network is used to illustrate the application of the proposed optimization models. The results indicate that these linear programming models are practical for the maintenance of a road network having a large number of road segments and that they are convenient to incorporate various constraints on the decision process, for example, performance requirements and available budgets. Although the optimal maintenance policies obtained for the road network are randomized stationary policies, the extent of this randomness in decision making is limited. The maintenance actions are deterministic for most states and the randomness in selecting actions occurs only for a few states.
Probabilistic cellular automata: Some statistical mechanical considerations
International Nuclear Information System (INIS)
Lebowitz, J.L.; Maes, C.; Speer, E.R.
1990-01-01
Spin systems evolving in continuous or discrete time under the action of stochastic dynamics are used to model phenomena as diverse as the structure of alloys and the functioning of neural networks. While in some cases the dynamics are secondary, designed to produce a specific stationary measure whose properties one is interested in studying, there are other cases in which the only available information is the dynamical rule. Prime examples of the former are computer simulations, via Glauber dynamics, of equilibrium Gibbs measures with a specified interaction potential. Examples of the latter include various types of majority rule dynamics used as models for pattern recognition and for error-tolerant computations. The present note discusses ways in which techniques found useful in equilibrium statistical mechanics can be applied to a particular class of models of the latter types. These are cellular automata with noise: systems in which the spins are updated stochastically at integer times, simultaneously at all sites of some regular lattice. These models were first investigated in detail in the Soviet literature of the late sixties and early seventies. They are now generally referred to as Stochastic or Probabilistic Cellular Automata (PCA), and may be considered to include deterministic automata (CA) as special limits. 16 refs., 3 figs
Chang, Insu
The objective of the thesis is to introduce a relatively general nonlinear controller/estimator synthesis framework using a special type of the state-dependent Riccati equation technique. The continuous time state-dependent Riccati equation (SDRE) technique is extended to discrete-time under input and state constraints, yielding constrained (C) discrete-time (D) SDRE, referred to as CD-SDRE. For the latter, stability analysis and calculation of a region of attraction are carried out. The derivation of the D-SDRE under state-dependent weights is provided. Stability of the D-SDRE feedback system is established using Lyapunov stability approach. Receding horizon strategy is used to take into account the constraints on D-SDRE controller. Stability condition of the CD-SDRE controller is analyzed by using a switched system. The use of CD-SDRE scheme in the presence of constraints is then systematically demonstrated by applying this scheme to problems of spacecraft formation orbit reconfiguration under limited performance on thrusters. Simulation results demonstrate the efficacy and reliability of the proposed CD-SDRE. The CD-SDRE technique is further investigated in a case where there are uncertainties in nonlinear systems to be controlled. First, the system stability under each of the controllers in the robust CD-SDRE technique is separately established. The stability of the closed-loop system under the robust CD-SDRE controller is then proven based on the stability of each control system comprising switching configuration. A high fidelity dynamical model of spacecraft attitude motion in 3-dimensional space is derived with a partially filled fuel tank, assumed to have the first fuel slosh mode. The proposed robust CD-SDRE controller is then applied to the spacecraft attitude control system to stabilize its motion in the presence of uncertainties characterized by the first fuel slosh mode. The performance of the robust CD-SDRE technique is discussed. Subsequently
Kim, Wonhee; Chen, Xu; Lee, Youngwoo; Chung, Chung Choo; Tomizuka, Masayoshi
2018-05-01
A discrete-time backstepping control algorithm is proposed for reference tracking of systems affected by both broadband disturbances at low frequencies and narrow band disturbances at high frequencies. A discrete time DOB, which is constructed based on infinite impulse response filters is applied to compensate for narrow band disturbances at high frequencies. A discrete-time nonlinear damping backstepping controller with an augmented observer is proposed to track the desired output and to compensate for low frequency broadband disturbances along with a disturbance observer, for rejecting narrow band high frequency disturbances. This combination has the merit of simultaneously compensating both broadband disturbances at low frequencies and narrow band disturbances at high frequencies. The performance of the proposed method is validated via experiments.
State control of discrete-time linear systems to be bound in state variables by equality constraints
International Nuclear Information System (INIS)
Filasová, Anna; Krokavec, Dušan; Serbák, Vladimír
2014-01-01
The paper is concerned with the problem of designing the discrete-time equivalent PI controller to control the discrete-time linear systems in such a way that the closed-loop state variables satisfy the prescribed equality constraints. Since the problem is generally singular, using standard form of the Lyapunov function and a symmetric positive definite slack matrix, the design conditions are proposed in the form of the enhanced Lyapunov inequality. The results, offering the conditions of the control existence and the optimal performance with respect to the prescribed equality constraints for square discrete-time linear systems, are illustrated with the numerical example to note effectiveness and applicability of the considered approach
Cali, Corrado; Baghabrah, Jumana; Boges, Daniya; Holst, Glendon; Kreshuk, Anna; Hamprecht, Fred A.; Srinivasan, Madhusudhanan; Lehvä slaiho, Heikki; Magistretti, Pierre J.
2015-01-01
Advances for application of electron microscopy to serial imaging are opening doors to new ways of analyzing cellular structure. New and improved algorithms and workflows for manual and semiautomated segmentation allow to observe the spatial
International Nuclear Information System (INIS)
Pirouzmand, Ahmad; Hadad, Kamal
2011-01-01
Highlights: → This paper describes the solution of time-independent neutron transport equation. → Using a novel method based on cellular neural networks (CNNs) coupled with P N method. → Utilize the CNN model to simulate spatial scalar flux distribution in steady state. → The accuracy, stability, and capabilities of CNN model are examined in x-y geometry. - Abstract: This paper describes a novel method based on using cellular neural networks (CNN) coupled with spherical harmonics method (P N ) to solve the time-independent neutron transport equation in x-y geometry. To achieve this, an equivalent electrical circuit based on second-order form of neutron transport equation and relevant boundary conditions is obtained using CNN method. We use the CNN model to simulate spatial response of scalar flux distribution in the steady state condition for different order of spherical harmonics approximations. The accuracy, stability, and capabilities of CNN model are examined in 2D Cartesian geometry for fixed source and criticality problems.
Persistence versus extinction for a class of discrete-time structured population models.
Jin, Wen; Smith, Hal L; Thieme, Horst R
2016-03-01
We provide sharp conditions distinguishing persistence and extinction for a class of discrete-time dynamical systems on the positive cone of an ordered Banach space generated by a map which is the sum of a positive linear contraction A and a nonlinear perturbation G that is compact and differentiable at zero in the direction of the cone. Such maps arise as year-to-year projections of population age, stage, or size-structure distributions in population biology where typically A has to do with survival and individual development and G captures the effects of reproduction. The threshold distinguishing persistence and extinction is the principal eigenvalue of (II−A)(−1)G'(0) provided by the Krein-Rutman Theorem, and persistence is described in terms of associated eigenfunctionals. Our results significantly extend earlier persistence results of the last two authors which required more restrictive conditions on G. They are illustrated by application of the results to a plant model with a seed bank.
Warren, Joshua L; Gordon-Larsen, Penny
2018-06-01
While there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross-sectional data. Analyses attempting to understand whether the availability of stores across neighborhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006-2012) and tract-level census data in four US cities: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; San Francisco, California. A spatial discrete-time survival model was used to identify factors associated with an earlier and/or later closure time of a store. Sales volume was typically the strongest indicator of store survival. We identified heterogeneity in the association between tract-level poverty and racial composition with respect to store survival. Stores in high poverty, non-White tracts were often at a disadvantage in terms of survival length. The observed patterns of store survival varied by some of the same neighborhood sociodemographic factors associated with lifestyle and health outcomes, which could lead to confusion in interpretation in studies of the estimated effects of introduction of food stores into neighborhoods on health.
A Generalized Stability Theorem for Discrete-Time Nonautonomous Chaos System with Applications
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Mei Zhang
2015-01-01
Full Text Available Firstly, this study introduces a definition of generalized stability (GST in discrete-time nonautonomous chaos system (DNCS, which is an extension for chaos generalized synchronization. Secondly, a constructive theorem of DNCS has been proposed. As an example, a GST DNCS is constructed based on a novel 4-dimensional discrete chaotic map. Numerical simulations show that the dynamic behaviors of this map have chaotic attractor characteristics. As one application, we design a chaotic pseudorandom number generator (CPRNG based on the GST DNCS. We use the SP800-22 test suite to test the randomness of four 100-key streams consisting of 1,000,000 bits generated by the CPRNG, the RC4 algorithm, the ZUC algorithm, and a 6-dimensional CGS-based CPRNG, respectively. The numerical results show that the randomness performances of the two CPRNGs are promising. In addition, theoretically the key space of the CPRNG is larger than 21116. As another application, this study designs a stream avalanche encryption scheme (SAES in RGB image encryption. The results show that the GST DNCS is able to generate the avalanche effects which are similar to those generated via ideal CPRNGs.
Rational solutions of the discrete time Toda lattice and the alternate discrete Painleve II equation
International Nuclear Information System (INIS)
Common, Alan K; Hone, Andrew N W
2008-01-01
The Yablonskii-Vorob'ev polynomials y n (t), which are defined by a second-order bilinear differential-difference equation, provide rational solutions of the Toda lattice. They are also polynomial tau-functions for the rational solutions of the second Painleve equation (P II ). Here we define two-variable polynomials Y n (t, h) on a lattice with spacing h, by considering rational solutions of the discrete time Toda lattice as introduced by Suris. These polynomials are shown to have many properties that are analogous to those of the Yablonskii-Vorob'ev polynomials, to which they reduce when h = 0. They also provide rational solutions for a particular discretization of P II , namely the so-called alternate discrete P II , and this connection leads to an expression in terms of the Umemura polynomials for the third Painleve equation (P III ). It is shown that the Baecklund transformation for the alternate discrete Painleve equation is a symplectic map, and the shift in time is also symplectic. Finally we present a Lax pair for the alternate discrete P II , which recovers Jimbo and Miwa's Lax pair for P II in the continuum limit h → 0
An Augmented Discrete-Time Approach for Human-Robot Collaboration
Directory of Open Access Journals (Sweden)
Peidong Liang
2016-01-01
Full Text Available Human-robot collaboration (HRC is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios.
Chun, Tae Yoon; Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho
2018-06-01
In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.
Dynamics of a two-dimensional discrete-time SIS model
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Jaime H. Barrera
2012-04-01
Full Text Available We analyze a two-dimensional discrete-time SIS model with a non-constant total population. Our goal is to determine the interaction between the total population, the susceptible class and the infective class, and the implications this may have for the disease dynamics. Utilizing a constant recruitment rate in the susceptible class, it is possible to assume the existence of an asymptotic limiting equation, which enables us to reduce the system of, two-equations into a single, dynamically equivalent equation. In this case, we are able to demonstrate the global stability of the disease-free and the endemic equilibria when the basic reproductive number (Ro is less than one and greater than one, respectively. When we consider a non-constant recruitment rate, the total population bifurcates as we vary the birth rate and the death rate. Using computer simulations, we observe different behavior among the infective class and the total population, and possibly, the occurrence of a strange attractor.
International Nuclear Information System (INIS)
Brianzoni, Serena; Mammana, Cristiana; Michetti, Elisabetta
2012-01-01
Highlights: ► One dimensional piecewise smooth map: border collision bifurcations. ► Numerical simulations: complex dynamics. ► Ves production function in the solow–swan growth model and comparison with the ces production function. - Abstract: We study the dynamics shown by the discrete time neoclassical one-sector growth model with differential savings as in Bohm and Kaas while assuming VES production function in the form given by Revankar . It is shown that the model can exhibit unbounded endogenous growth despite the absence of exogenous technical change and the presence of non-reproducible factors if the elasticity of substitution is greater than one. We then consider parameters range related to non-trivial dynamics (i.e. the elasticity of substitution in less than one and shareholders save more than workers) and we focus on local and global bifurcations causing the transition to more and more complex asymptotic dynamics. In particular, as our map is non-differentiable in a subset of the states space, we show that border collision bifurcations occur. Several numerical simulations support the analysis.
Theory and computation of disturbance invariant sets for discrete-time linear systems
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Kolmanovsky Ilya
1998-01-01
Full Text Available This paper considers the characterization and computation of invariant sets for discrete-time, time-invariant, linear systems with disturbance inputs whose values are confined to a specified compact set but are otherwise unknown. The emphasis is on determining maximal disturbance-invariant sets X that belong to a specified subset Γ of the state space. Such d-invariant sets have important applications in control problems where there are pointwise-in-time state constraints of the form χ ( t ∈ Γ . One purpose of the paper is to unite and extend in a rigorous way disparate results from the prior literature. In addition there are entirely new results. Specific contributions include: exploitation of the Pontryagin set difference to clarify conceptual matters and simplify mathematical developments, special properties of maximal invariant sets and conditions for their finite determination, algorithms for generating concrete representations of maximal invariant sets, practical computational questions, extension of the main results to general Lyapunov stable systems, applications of the computational techniques to the bounding of state and output response. Results on Lyapunov stable systems are applied to the implementation of a logic-based, nonlinear multimode regulator. For plants with disturbance inputs and state-control constraints it enlarges the constraint-admissible domain of attraction. Numerical examples illustrate the various theoretical and computational results.
Computational Procedures for a Class of GI/D/k Systems in Discrete Time
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Md. Mostafizur Rahman
2009-01-01
Full Text Available A class of discrete time GI/D/k systems is considered for which the interarrival times have finite support and customers are served in first-in first-out (FIFO order. The system is formulated as a single server queue with new general independent interarrival times and constant service duration by assuming cyclic assignment of customers to the identical servers. Then the queue length is set up as a quasi-birth-death (QBD type Markov chain. It is shown that this transformed GI/D/1 system has special structures which make the computation of the matrix R simple and efficient, thereby reducing the number of multiplications in each iteration significantly. As a result we were able to keep the computation time very low. Moreover, use of the resulting structural properties makes the computation of the distribution of queue length of the transformed system efficient. The computation of the distribution of waiting time is also shown to be simple by exploiting the special structures.
On a random area variable arising in discrete-time queues and compact directed percolation
International Nuclear Information System (INIS)
Kearney, Michael J
2004-01-01
A well-known discrete-time, single-server queueing system with mean arrival rate λ and mean departure rate μ is considered from the perspective of the area, A, swept out by the queue occupation process during a busy period. We determine the exact form of the tail of the distribution, Pr(A > x); in particular, we show that Pr(A > x) ∼ Cx -1/4 exp(-Dx 1/2 ) for all ρ ≠ 1, where ρ ≡ λ/μ, and expressions for C and D are given. For the critical case ρ = 1 we show that Pr(A > x) ∼ C'x -1/3 , with C' also given. A simple mapping, used in the derivation, establishes a connection with compact directed percolation on a square lattice. As a corollary, therefore, we are also able to specify the large-area asymptotic behaviour of this model at all points in the phase diagram. This extends previous scaling results, which are only valid close to the percolation threshold
Bifurcation and complex dynamics of a discrete-time predator-prey system
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S. M. Sohel Rana
2015-06-01
Full Text Available In this paper, we investigate the dynamics of a discrete-time predator-prey system of Holling-I type in the closed first quadrant R+2. The existence and local stability of positive fixed point of the discrete dynamical system is analyzed algebraically. It is shown that the system undergoes a flip bifurcation and a Neimark-Sacker bifurcation in the interior of R+2 by using bifurcation theory. It has been found that the dynamical behavior of the model is very sensitive to the parameter values and the initial conditions. Numerical simulation results not only show the consistence with the theoretical analysis but also display the new and interesting dynamic behaviors, including phase portraits, period-9, 10, 20-orbits, attracting invariant circle, cascade of period-doubling bifurcation from period-20 leading to chaos, quasi-periodic orbits, and sudden disappearance of the chaotic dynamics and attracting chaotic set. In particular, we observe that when the prey is in chaotic dynamic, the predator can tend to extinction or to a stable equilibrium. The Lyapunov exponents are numerically computed to characterize the complexity of the dynamical behaviors. The analysis and results in this paper are interesting in mathematics and biology.
Examining School-Based Bullying Interventions Using Multilevel Discrete Time Hazard Modeling
Wagaman, M. Alex; Geiger, Jennifer Mullins; Bermudez-Parsai, Monica; Hedberg, E. C.
2014-01-01
Although schools have been trying to address bulling by utilizing different approaches that stop or reduce the incidence of bullying, little remains known about what specific intervention strategies are most successful in reducing bullying in the school setting. Using the social-ecological framework, this paper examines school-based disciplinary interventions often used to deliver consequences to deter the reoccurrence of bullying and aggressive behaviors among school-aged children. Data for this study are drawn from the School-Wide Information System (SWIS) with the final analytic sample consisting of 1,221 students in grades K – 12 who received an office disciplinary referral for bullying during the first semester. Using Kaplan-Meier Failure Functions and Multi-level discrete time hazard models, determinants of the probability of a student receiving a second referral over time were examined. Of the seven interventions tested, only Parent-Teacher Conference (AOR=0.65, pbullying and aggressive behaviors. By using a social-ecological framework, schools can develop strategies that deter the reoccurrence of bullying by identifying key factors that enhance a sense of connection between the students’ mesosystems as well as utilizing disciplinary strategies that take into consideration student’s microsystem roles. PMID:22878779
A new look at the robust control of discrete-time Markov jump linear systems
Todorov, M. G.; Fragoso, M. D.
2016-03-01
In this paper, we make a foray in the role played by a set of four operators on the study of robust H2 and mixed H2/H∞ control problems for discrete-time Markov jump linear systems. These operators appear in the study of mean square stability for this class of systems. By means of new linear matrix inequality (LMI) characterisations of controllers, which include slack variables that, to some extent, separate the robustness and performance objectives, we introduce four alternative approaches to the design of controllers which are robustly stabilising and at the same time provide a guaranteed level of H2 performance. Since each operator provides a different degree of conservatism, the results are unified in the form of an iterative LMI technique for designing robust H2 controllers, whose convergence is attained in a finite number of steps. The method yields a new way of computing mixed H2/H∞ controllers, whose conservatism decreases with iteration. Two numerical examples illustrate the applicability of the proposed results for the control of a small unmanned aerial vehicle, and for an underactuated robotic arm.
A discrete-time Bayesian network reliability modeling and analysis framework
International Nuclear Information System (INIS)
Boudali, H.; Dugan, J.B.
2005-01-01
Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis
Adaptive Dynamic Programming for Discrete-Time Zero-Sum Games.
Wei, Qinglai; Liu, Derong; Lin, Qiao; Song, Ruizhuo
2018-04-01
In this paper, a novel adaptive dynamic programming (ADP) algorithm, called "iterative zero-sum ADP algorithm," is developed to solve infinite-horizon discrete-time two-player zero-sum games of nonlinear systems. The present iterative zero-sum ADP algorithm permits arbitrary positive semidefinite functions to initialize the upper and lower iterations. A novel convergence analysis is developed to guarantee the upper and lower iterative value functions to converge to the upper and lower optimums, respectively. When the saddle-point equilibrium exists, it is emphasized that both the upper and lower iterative value functions are proved to converge to the optimal solution of the zero-sum game, where the existence criteria of the saddle-point equilibrium are not required. If the saddle-point equilibrium does not exist, the upper and lower optimal performance index functions are obtained, respectively, where the upper and lower performance index functions are proved to be not equivalent. Finally, simulation results and comparisons are shown to illustrate the performance of the present method.
Directory of Open Access Journals (Sweden)
Maode Yan
2008-01-01
Full Text Available This paper considers the problem of robust discrete-time sliding-mode control (DT-SMC design for a class of uncertain linear systems with time-varying delays. By applying a descriptor model transformation and Moon's inequality for bounding cross terms, a delay-dependent sufficient condition for the existence of stable sliding surface is given in terms of linear matrix inequalities (LMIs. Based on this existence condition, the synthesized sliding mode controller can guarantee the sliding-mode reaching condition of the specified discrete-time sliding surface for all admissible uncertainties and time-varying delays. An illustrative example verifies the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Bingbing Xu
2013-01-01
Full Text Available We consider the leader-following consensus problem of discrete-time multiagent systems on a directed communication topology. Two types of distributed observer-based consensus protocols are considered to solve such a problem. The observers involved in the proposed protocols include full-order observer and reduced-order observer, which are used to reconstruct the state variables. Two algorithms are provided to construct the consensus protocols, which are based on the modified discrete-time algebraic Riccati equation and Sylvester equation. In light of graph and matrix theory, some consensus conditions are established. Finally, a numerical example is provided to illustrate the obtained result.
Liang, Hongjing; Zhang, Huaguang; Wang, Zhanshan
2015-11-01
This paper considers output synchronization of discrete-time multi-agent systems with directed communication topologies. The directed communication graph contains a spanning tree and the exosystem as its root. Distributed observer-based consensus protocols are proposed, based on the relative outputs of neighboring agents. A multi-step algorithm is presented to construct the observer-based protocols. In light of the discrete-time algebraic Riccati equation and internal model principle, synchronization problem is completed. At last, numerical simulation is provided to verify the effectiveness of the theoretical results. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
A New Approach to Rational Discrete-Time Approximations to Continuous-Time Fractional-Order Systems
Matos , Carlos; Ortigueira , Manuel ,
2012-01-01
Part 10: Signal Processing; International audience; In this paper a new approach to rational discrete-time approximations to continuous fractional-order systems of the form 1/(sα+p) is proposed. We will show that such fractional-order LTI system can be decomposed into sub-systems. One has the classic behavior and the other is similar to a Finite Impulse Response (FIR) system. The conversion from continuous-time to discrete-time systems will be done using the Laplace transform inversion integr...
Shih, Peter; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A
2009-10-01
A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.
Financial Distress Prediction Using Discrete-time Hazard Model and Rating Transition Matrix Approach
Tsai, Bi-Huei; Chang, Chih-Huei
2009-08-01
Previous studies used constant cut-off indicator to distinguish distressed firms from non-distressed ones in the one-stage prediction models. However, distressed cut-off indicator must shift according to economic prosperity, rather than remains fixed all the time. This study focuses on Taiwanese listed firms and develops financial distress prediction models based upon the two-stage method. First, this study employs the firm-specific financial ratio and market factors to measure the probability of financial distress based on the discrete-time hazard models. Second, this paper further focuses on macroeconomic factors and applies rating transition matrix approach to determine the distressed cut-off indicator. The prediction models are developed by using the training sample from 1987 to 2004, and their levels of accuracy are compared with the test sample from 2005 to 2007. As for the one-stage prediction model, the model in incorporation with macroeconomic factors does not perform better than that without macroeconomic factors. This suggests that the accuracy is not improved for one-stage models which pool the firm-specific and macroeconomic factors together. In regards to the two stage models, the negative credit cycle index implies the worse economic status during the test period, so the distressed cut-off point is adjusted to increase based on such negative credit cycle index. After the two-stage models employ such adjusted cut-off point to discriminate the distressed firms from non-distressed ones, their error of misclassification becomes lower than that of one-stage ones. The two-stage models presented in this paper have incremental usefulness in predicting financial distress.
A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects
DEFF Research Database (Denmark)
Bollerslev, Tim; Kretschmer, Uta; Pigorsch, Christian
We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuoustime components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday...... dependencies inherent in the high-frequency intraday data....
Suris, Yuri B.
1997-01-01
A fairly complete list of Toda-like integrable lattice systems, both in the continuous and discrete time, is given. For each system the Newtonian, Lagrangian and Hamiltonian formulations are presented, as well as the 2x2 Lax representation and r-matrix structure. The material is given in the "no comment" style, in particular, all proofs are omitted.
Hofstede, ter F.; Wedel, M.
1998-01-01
This study investigates the effects of time aggregation in discrete and continuous-time hazard models. A Monte Carlo study is conducted in which data are generated according to various continuous and discrete-time processes, and aggregated into daily, weekly and monthly intervals. These data are
Zhou, Ji; Castellanos, Michelle
2013-01-01
Utilizing longitudinal data of 3477 students from 28 institutions, we examine the effects of structural diversity and quality of interracial relation on students' persistence towards graduation within six years. We utilize multilevel discrete-time survival analysis to account for the longitudinal persistence patterns as well as the nested…
International Nuclear Information System (INIS)
Merdan, H.; Duman, O.
2009-01-01
This paper presents the stability analysis of equilibrium points of a general discrete-time population dynamics involving predation with and without Allee effects which occur at low population density. The mathematical analysis and numerical simulations show that the Allee effect has a stabilizing role on the local stability of the positive equilibrium points of this model.
Analysis and Design of a High-Order Discrete-Time Passive IIR Low-Pass Filter
Tohidian, M.; Madadi, I.; Staszewski, R.B.
2014-01-01
In this paper, we propose a discrete-time IIR low-pass filter that achieves a high-order of filtering through a charge-sharing rotation. Its sampling rate is then multiplied through pipelining. The first stage of the filter can operate in either a voltage-sampling or charge-sampling mode. It uses
Aydiner, E.; Brunner, F.D.; Heemels, W.P.M.H.; Allgower, F.
2015-01-01
In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every sampling instant an optimization problem based
Guay, M.; Beerens, R.; Nijmeijer, H.
2014-01-01
This paper considers the solution of a real-time optimization problem using adaptive extremum seeking control for a class of unknown discrete-time nonlinear systems. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and
Azari, M; Tayyebi, A; Helbich, M; Ahadnejad Reveshty, M
2016-01-01
Urbanization processes challenge the growth of orchards areas in many cities in Iran. In Maragheh orchards are crucial ecological, economical, and tourist sources. To explore orchards threatened by urban expansion, this study aims, first, to develop a new model by coupling cellular automata and
Cali, Corrado
2015-07-14
Advances for application of electron microscopy to serial imaging are opening doors to new ways of analyzing cellular structure. New and improved algorithms and workflows for manual and semiautomated segmentation allow to observe the spatial arrangement of the smallest cellular features with unprecedented detail in full three-dimensions (3D). From larger samples, higher complexity models can be generated; however, they pose new challenges to data management and analysis. Here, we review some currently available solutions and present our approach in detail. We use the fully immersive virtual reality (VR) environment CAVE (cave automatic virtual environment), a room where we are able to project a cellular reconstruction and visualize in 3D, to step into a world created with Blender, a free, fully customizable 3D modeling software with NeuroMorph plug-ins for visualization and analysis of electron microscopy (EM) preparations of brain tissue. Our workflow allows for full and fast reconstructions of volumes of brain neuropil using ilastik, a software tool for semiautomated segmentation of EM stacks. With this visualization environment, we can walk into the model containing neuronal and astrocytic processes to study the spatial distribution of glycogen granules, a major energy source that is selectively stored in astrocytes. The use of CAVE was key to observe a nonrandom distribution of glycogen, and led us to develop tools to quantitatively analyze glycogen clustering and proximity to other subcellular features. This article is protected by copyright. All rights reserved.
Stability analysis and synchronization in discrete-time complex networks with delayed coupling
Cheng, Ranran; Peng, Mingshu; Yu, Weibin; Sun, Bo; Yu, Jinchen
2013-12-01
A new network of coupled maps is proposed in which the connections between units involve no delays but the intra-neural communication does, whereas in the work of Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], the focus is on information processing delayed by the inter-neural communication. We show that the synchronization of the network depends on not only the intrinsic dynamical features and inter-connection topology (characterized by the spectrum of the graph Laplacian) but also the delays and the coupling strength. There are two main findings: (i) the more neighbours, the easier to be synchronized; (ii) odd delays are easier to be synchronized than even ones. In addition, compared with those discussed by Atay et al. [Phys. Rev. Lett. 92, 144101 (2004)], our model has a better synchronizability for regular networks and small-world variants.
Statistical mechanics of cellular automata
International Nuclear Information System (INIS)
Wolfram, S.
1983-01-01
Cellular automata are used as simple mathematical models to investigate self-organization in statistical mechanics. A detailed analysis is given of ''elementary'' cellular automata consisting of a sequence of sites with values 0 or 1 on a line, with each site evolving deterministically in discrete time steps according to p definite rules involving the values of its nearest neighbors. With simple initial configurations, the cellular automata either tend to homogeneous states, or generate self-similar patterns with fractal dimensions approx. =1.59 or approx. =1.69. With ''random'' initial configurations, the irreversible character of the cellular automaton evolution leads to several self-organization phenomena. Statistical properties of the structures generated are found to lie in two universality classes, independent of the details of the initial state or the cellular automaton rules. More complicated cellular automata are briefly considered, and connections with dynamical systems theory and the formal theory of computation are discussed
Directory of Open Access Journals (Sweden)
Zongyan Li
2016-01-01
Full Text Available This paper describes an improved global harmony search (IGHS algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes.
Lismawati, Eka; Respatiwulan; Widyaningsih, Purnami
2017-06-01
The SIS epidemic model describes the pattern of disease spread with characteristics that recovered individuals can be infected more than once. The number of susceptible and infected individuals every time follows the discrete time Markov process. It can be represented by the discrete time Markov chains (DTMC) SIS. The DTMC SIS epidemic model can be developed for two pathogens in two patches. The aims of this paper are to reconstruct and to apply the DTMC SIS epidemic model with two pathogens in two patches. The model was presented as transition probabilities. The application of the model obtain that the number of susceptible individuals decreases while the number of infected individuals increases for each pathogen in each patch.
Energy Technology Data Exchange (ETDEWEB)
Shorikov, A. F. [Ural Federal University, 19 S. Mira, Ekaterinburg, 620002, Russia and Institute of Mathematics and Mechanics, Ural Division of Russian Academy of Sciences, 16 S. Kovalevskaya, Ekaterinburg, 620990 (Russian Federation)
2014-11-18
We consider a discrete-time dynamical system consisting of three controllable objects. The motions of all objects are given by the corresponding vector linear or convex discrete-time recurrent vector relations, and control system for its has two levels: basic (first or I level) that is dominating and subordinate level (second or II level) and both have different criterions of functioning and united a priori by determined informational and control connections defined in advance. For the dynamical system in question, we propose a mathematical formalization in the form of solving a multistep problem of two-level hierarchical minimax program control over the terminal approach process with incomplete information and give a general scheme for its solution.
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Yolanda Vidal
2015-05-01
Full Text Available This paper develops a fault diagnosis (FD and fault-tolerant control (FTC of pitch actuators in wind turbines. This is accomplished by combining a disturbance compensator with a controller, both of which are formulated in the discrete time domain. The disturbance compensator has a dual purpose: to estimate the actuator fault (which is used by the FD algorithm and to design the discrete time controller to obtain an FTC. That is, the pitch actuator faults are estimated, and then, the pitch control laws are appropriately modified to achieve an FTC with a comparable behavior to the fault-free case. The performance of the FD and FTC schemes is tested in simulations with the aero-elastic code FAST.
Masuyama, Hiroyuki
2014-01-01
In this paper we study the augmented truncation of discrete-time block-monotone Markov chains under geometric drift conditions. We first present a bound for the total variation distance between the stationary distributions of an original Markov chain and its augmented truncation. We also obtain such error bounds for more general cases, where an original Markov chain itself is not necessarily block monotone but is blockwise dominated by a block-monotone Markov chain. Finally,...
Heston, Steven L.; Nandi, Saikat
1999-01-01
This paper develops a discrete-time two-factor model of interest rates with analytical solutions for bonds and many interest rate derivatives when the volatility of the short rate follows a GARCH process that can be correlated with the level of the short rate itself. Besides bond and bond futures, the model yields analytical solutions for prices of European options on discount bonds (and futures) as well as other interest rate derivatives such as caps, floors, average rate options, yield curv...
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Christopher D Hudson
Full Text Available The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period, PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd rather than individual level.
Robust Moving Horizon H∞ Control of Discrete Time-Delayed Systems with Interval Time-Varying Delays
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F. Yıldız Tascikaraoglu
2014-01-01
Full Text Available In this study, design of a delay-dependent type moving horizon state-feedback control (MHHC is considered for a class of linear discrete-time system subject to time-varying state delays, norm-bounded uncertainties, and disturbances with bounded energies. The closed-loop robust stability and robust performance problems are considered to overcome the instability and poor disturbance rejection performance due to the existence of parametric uncertainties and time-delay appeared in the system dynamics. Utilizing a discrete-time Lyapunov-Krasovskii functional, some delay-dependent linear matrix inequality (LMI based conditions are provided. It is shown that if one can find a feasible solution set for these LMI conditions iteratively at each step of run-time, then we can construct a control law which guarantees the closed-loop asymptotic stability, maximum disturbance rejection performance, and closed-loop dissipativity in view of the actuator limitations. Two numerical examples with simulations on a nominal and uncertain discrete-time, time-delayed systems, are presented at the end, in order to demonstrate the efficiency of the proposed method.
Almeida, P.M.; Barbosa, P.G.; Duarte, J.L.; Ribeiro, P.F.
2017-01-01
This paper presents a novel comprehensive discrete-time model of a three-phase single stage grid-connected photovoltaic generation system. The detailed model is carried out on synchronous reference frame. It is shown that both converter's AC and DC-side discrete time model differs from the
Directory of Open Access Journals (Sweden)
Ankush Gosain
Full Text Available Hirschsprung's disease (HSCR is characterized by aganglionosis from failure of neural crest cell (NCC migration to the distal hindgut. Up to 40% of HSCR patients suffer Hirschsprung's-associated enterocolitis (HAEC, with an incidence that is unchanged from the pre-operative to the post-operative state. Recent reports indicate that signaling pathways involved in NCC migration may also be involved in the development of secondary lymphoid organs. We hypothesize that gastrointestinal (GI mucosal immune defects occur in HSCR that may contribute to enterocolitis. EdnrB was deleted from the neural crest (EdnrBNCC-/- resulting in mutants with defective NCC migration, distal colonic aganglionosis and the development of enterocolitis. The mucosal immune apparatus of these mice was interrogated at post-natal day (P 21-24, prior to histological signs of enterocolitis. We found that EdnrBNCC-/- display lymphopenia of their Peyer's Patches, the major inductive site of GI mucosal immunity. EdnrBNCC-/- Peyer's Patches demonstrate decreased B-lymphocytes, specifically IgM+IgDhi (Mature B-lymphocytes, which are normally activated and produce IgA following antigen presentation. EdnrBNCC-/- animals demonstrate decreased small intestinal secretory IgA, but unchanged nasal and bronchial airway secretory IgA, indicating a gut-specific defect in IgA production or secretion. In the spleen, which is the primary source of IgA-producing Mature B-lymphocytes, EdnrBNCC-/- animals display decreased B-lymphocytes, but an increase in Mature B-lymphocytes. EdnrBNCC-/- spleens are also small and show altered architecture, with decreased red pulp and a paucity of B-lymphocytes in the germinal centers and marginal zone. Taken together, these findings suggest impaired GI mucosal immunity in EdnrBNCC-/- animals, with the spleen as a potential site of the defect. These findings build upon the growing body of literature that suggests that intestinal defects in HSCR are not restricted
Yang, Qinmin; Jagannathan, Sarangapani
2012-04-01
In this paper, reinforcement learning state- and output-feedback-based adaptive critic controller designs are proposed by using the online approximators (OLAs) for a general multi-input and multioutput affine unknown nonlinear discretetime systems in the presence of bounded disturbances. The proposed controller design has two entities, an action network that is designed to produce optimal signal and a critic network that evaluates the performance of the action network. The critic estimates the cost-to-go function which is tuned online using recursive equations derived from heuristic dynamic programming. Here, neural networks (NNs) are used both for the action and critic whereas any OLAs, such as radial basis functions, splines, fuzzy logic, etc., can be utilized. For the output-feedback counterpart, an additional NN is designated as the observer to estimate the unavailable system states, and thus, separation principle is not required. The NN weight tuning laws for the controller schemes are also derived while ensuring uniform ultimate boundedness of the closed-loop system using Lyapunov theory. Finally, the effectiveness of the two controllers is tested in simulation on a pendulum balancing system and a two-link robotic arm system.
Masuyama, Hiroyuki
2015-01-01
This paper studies the last-column-block-augmented northwest-corner truncation (LC-block-augmented truncation, for short) of discrete-time block-monotone Markov chains under subgeometric drift conditions. The main result of this paper is to present an upper bound for the total variation distance between the stationary probability vectors of a block-monotone Markov chain and its LC-block-augmented truncation. The main result is extended to Markov chains that themselves may not be block monoton...
DEFF Research Database (Denmark)
Mohd. Azam, Sazuan Nazrah
2017-01-01
In this paper, we used the modified quadruple tank system that represents a multi-input-multi-output (MIMO) system as an example to present the realization of a linear discrete-time state space model and to obtain the state estimation using Kalman filter in a methodical mannered. First, an existing...... part of the Kalman filter is used to estimates the current state, based on the model and the measurements. The static and dynamic Kalman filter is compared and all results is demonstrated through simulations....
On the Discrete-Time GeoX/G/1 Queues under N-Policy with Single and Multiple Vacations
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Sung J. Kim
2013-01-01
Full Text Available We consider the discrete-time GeoX/G/1 queue under N-policy with single and multiple vacations. In this queueing system, the server takes multiple vacations and a single vacation whenever the system becomes empty and begins to serve customers only if the queue length is at least a predetermined threshold value N. Using the well-known property of stochastic decomposition, we derive the stationary queue-length distributions for both vacation models in a simple and unified manner. In addition, we derive their busy as well as idle-period distributions. Some classical vacation models are considered as special cases.
Nakamura, Yoshimasa; Sekido, Hiroto
2018-04-01
The finite or the semi-infinite discrete-time Toda lattice has many applications to various areas in applied mathematics. The purpose of this paper is to review how the Toda lattice appears in the Lanczos algorithm through the quotient-difference algorithm and its progressive form (pqd). Then a multistep progressive algorithm (MPA) for solving linear systems is presented. The extended Lanczos parameters can be given not by computing inner products of the extended Lanczos vectors but by using the pqd algorithm with highly relative accuracy in a lower cost. The asymptotic behavior of the pqd algorithm brings us some applications of MPA related to eigenvectors.
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Sung Soo Kim
2016-02-01
Full Text Available We consider a discrete-time dependent Sparre Andersen risk model which incorporates multiple threshold levels characterizing an insurer’s minimal capital requirement, dividend paying situations, and external financial activities. We focus on the development of a recursive computational procedure to calculate the finite-time ruin probabilities and expected total discounted dividends paid prior to ruin associated with this model. We investigate several numerical examples and make some observations concerning the impact our threshold levels have on the finite-time ruin probabilities and expected total discounted dividends paid prior to ruin.
Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin
2018-04-01
Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Herzog, W; Schellberg, D; Deter, H C
1997-02-01
The results of a 12-year follow-up study of occurrence and timing of first recovery in 69 hospitalized patients with severe anorexia nervosa (AN) are presented. For the first time discrete-time survival analysis methods were used to determine the likelihood of recovery in AN patients. Furthermore, predictors gleaned from pretreatment-posttreatment studies of long-term outcome in AN could be evaluated as to their effect on a change in the time course structure of the likelihood of first recovery. Results show that AN condition did not improve until after 6 years after the first inpatient treatment in 50% of patients. However, a restricter-type AN and low serum creatinine levels were predictors for earlier recovery. One specific effect was that AN patients who show purging behavior in combination with additional social disturbances have a lower chance of recovering. The use of discrete-time survival analysis methodology in further prospective studies will contribute to the development of more tailored treatment of AN, which also takes the individual phase of illness and specific aspects of the symptomatology into account.
Energy Technology Data Exchange (ETDEWEB)
Vanderbei, Robert J., E-mail: rvdb@princeton.edu [Princeton University, Department of Operations Research and Financial Engineering (United States); P Latin-Small-Letter-Dotless-I nar, Mustafa C., E-mail: mustafap@bilkent.edu.tr [Bilkent University, Department of Industrial Engineering (Turkey); Bozkaya, Efe B. [Sabanc Latin-Small-Letter-Dotless-I University, Faculty of Administrative Sciences (Turkey)
2013-02-15
An American option (or, warrant) is the right, but not the obligation, to purchase or sell an underlying equity at any time up to a predetermined expiration date for a predetermined amount. A perpetual American option differs from a plain American option in that it does not expire. In this study, we solve the optimal stopping problem of a perpetual American option (both call and put) in discrete time using linear programming duality. Under the assumption that the underlying stock price follows a discrete time and discrete state Markov process, namely a geometric random walk, we formulate the pricing problem as an infinite dimensional linear programming (LP) problem using the excessive-majorant property of the value function. This formulation allows us to solve complementary slackness conditions in closed-form, revealing an optimal stopping strategy which highlights the set of stock-prices where the option should be exercised. The analysis for the call option reveals that such a critical value exists only in some cases, depending on a combination of state-transition probabilities and the economic discount factor (i.e., the prevailing interest rate) whereas it ceases to be an issue for the put.
International Nuclear Information System (INIS)
Vanderbei, Robert J.; Pınar, Mustafa Ç.; Bozkaya, Efe B.
2013-01-01
An American option (or, warrant) is the right, but not the obligation, to purchase or sell an underlying equity at any time up to a predetermined expiration date for a predetermined amount. A perpetual American option differs from a plain American option in that it does not expire. In this study, we solve the optimal stopping problem of a perpetual American option (both call and put) in discrete time using linear programming duality. Under the assumption that the underlying stock price follows a discrete time and discrete state Markov process, namely a geometric random walk, we formulate the pricing problem as an infinite dimensional linear programming (LP) problem using the excessive-majorant property of the value function. This formulation allows us to solve complementary slackness conditions in closed-form, revealing an optimal stopping strategy which highlights the set of stock-prices where the option should be exercised. The analysis for the call option reveals that such a critical value exists only in some cases, depending on a combination of state-transition probabilities and the economic discount factor (i.e., the prevailing interest rate) whereas it ceases to be an issue for the put.
Kouramas, K.I.; Faí sca, N.P.; Panos, C.; Pistikopoulos, E.N.
2011-01-01
This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques
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Vasile Dr ̆agan
2017-06-01
Full Text Available We investigate the problem for solving a discrete-time periodic gen- eralized Riccati equation with an indefinite sign of the quadratic term. A necessary condition for the existence of bounded and stabilizing solution of the discrete-time Riccati equation with an indefinite quadratic term is derived. The stabilizing solution is positive semidefinite and satisfies the introduced sign conditions. The proposed condition is illustrated via a numerical example.
International Nuclear Information System (INIS)
Chiou, S.-H.; Chen, S.-J.; Peng, C-H.; Chang, Y.-L.; Ku, H.-H.; Hsu, W.-M.; Ho, Larry L.-T.; Lee, C.-H.
2006-01-01
Fluoxetine is a widely used antidepressant compound which inhibits the reuptake of serotonin in the central nervous system. Recent studies have shown that fluoxetine can promote neurogenesis and improve the survival rate of neurons. However, whether fluoxetine modulates the proliferation or neuroprotection effects of neural stem cells (NSCs) needs to be elucidated. In this study, we demonstrated that 20 μM fluoxetine can increase the cell proliferation of NSCs derived from the hippocampus of adult rats by MTT test. The up-regulated expression of Bcl-2, Bcl-xL and the cellular FLICE-inhibitory protein (c-FLIP) in fluoxetine-treated NSCs was detected by real-time RT-PCR. Our results further showed that fluoxetine protects the lipopolysaccharide-induced apoptosis in NSCs, in part, by activating the expression of c-FLIP. Moreover, c-FLIP induction by fluoxetine requires the activation of the c-FLIP promoter region spanning nucleotides -414 to -133, including CREB and SP1 sites. This effect appeared to involve the phosphatidylinositol-3-kinase-dependent pathway. Furthermore, fluoxetine treatment significantly inhibited the induction of proinflammatory factor IL-1β, IL-6, and TNF-α in the culture medium of LPS-treated NSCs (p < 0.01). The results of high performance liquid chromatography coupled to electrochemical detection further confirmed that fluoxentine increased the functional production of serotonin in NSCs. Together, these data demonstrate the specific activation of c-FLIP by fluoxetine and indicate the novel role of fluoxetine for neuroprotection in the treatment of depression
International Nuclear Information System (INIS)
Ozekes, Serhat; Osman, Onur; Ucan, N.
2008-01-01
The purpose of this study was to develop a new method for automated lung nodule detection in serial section CT images with using the characteristics of the 3D appearance of the nodules that distinguish themselves from the vessels. Lung nodules were detected in four steps. First, to reduce the number of region of interests (ROIs) and the computation time, the lung regions of the CTs were segmented using Genetic Cellular Neural Networks (G-CNN). Then, for each lung region, ROIs were specified with using the 8 directional search; +1 or -1 values were assigned to each voxel. The 3D ROI image was obtained by combining all the 2-Dimensional (2D) ROI images. A 3D template was created to find the nodule-like structures on the 3D ROI image. Convolution of the 3D ROI image with the proposed template strengthens the shapes that are similar to those of the template and it weakens the other ones. Finally, fuzzy rule based thresholding was applied and the ROI's were found. To test the system's efficiency, we used 16 cases with a total of 425 slices, which were taken from the Lung Image Database Consortium (LIDC) dataset. The computer aided diagnosis (CAD) system achieved 100% sensitivity with 13.375 FPs per case when the nodule thickness was greater than or equal to 5.625 mm. Our results indicate that the detection performance of our algorithm is satisfactory, and this may well improve the performance of computer aided detection of lung nodules
On the Two-Moment Approximation of the Discrete-Time GI/G/1 Queue with a Single Vacation
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Doo Ho Lee
2016-01-01
Full Text Available We consider a discrete-time GI/G/1 queue in which the server takes exactly one vacation each time the system becomes empty. The interarrival times of arriving customers, the service times, and the vacation times are all generic discrete random variables. Under our study, we derive an exact transform-free expression for the stationary system size distribution through the modified supplementary variable technique. Utilizing obtained results, we introduce a simple two-moment approximation for the system size distribution. From this, approximations for the mean system size along with the system size distribution could be obtained. Finally, some numerical examples are given to validate the proposed approximation method.
International Nuclear Information System (INIS)
Park, Moon Kyu; Kim, Yong Hee; Cha, Kune Ho; Kim, Myung Ki
1998-01-01
A method is described to develop an H∞ filtering method for the dynamic compensation of self-powered neutron detectors normally used for fixed incore instruments. An H∞ norm of the filter transfer matrix is used as the optimization criteria in the worst-case estimation error sense. Filter modeling is performed for discrete-time model. The filter gains are optimized in the sense of noise attenuation level of H∞ setting. By introducing Bounded Real Lemma, the conventional algebraic Riccati inequalities are converted into Linear Matrix Inequalities (LMIs). Finally, the filter design problem is solved via the convex optimization framework using LMIs. The simulation results show that remarkable improvements are achieved in view of the filter response time and the filter design efficiency
Ruin Probabilities in a Dependent Discrete-Time Risk Model With Gamma-Like Tailed Insurance Risks
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Xing-Fang Huang
2017-03-01
Full Text Available This paper considered a dependent discrete-time risk model, in which the insurance risks are represented by a sequence of independent and identically distributed real-valued random variables with a common Gamma-like tailed distribution; the ﬁnancial risks are denoted by another sequence of independent and identically distributed positive random variables with a ﬁnite upper endpoint, but a general dependence structure exists between each pair of the insurance risks and the ﬁnancial risks. Following the works of Yang and Yuen in 2016, we derive some asymptotic relations for the ﬁnite-time and inﬁnite-time ruin probabilities. As a complement, we demonstrate our obtained result through a Crude Monte Carlo (CMC simulation with asymptotics.
International Nuclear Information System (INIS)
Goharrizi, Amin Yazdanpanah; Khaki-Sedigh, Ali; Sepehri, Nariman
2009-01-01
A new approach to adaptive control of chaos in a class of nonlinear discrete-time-varying systems, using a delayed state feedback scheme, is presented. It is discussed that such systems can show chaotic behavior as their parameters change. A strategy is employed for on-line calculation of the Lyapunov exponents that will be used within an adaptive scheme that decides on the control effort to suppress the chaotic behavior once detected. The scheme is further augmented with a nonlinear observer for estimation of the states that are required by the controller but are hard to measure. Simulation results for chaotic control problem of Jin map are provided to show the effectiveness of the proposed scheme.
Arnold tongues and the Devil's Staircase in a discrete-time Hindmarsh–Rose neuron model
Energy Technology Data Exchange (ETDEWEB)
Felicio, Carolini C., E-mail: carolini.cf@gmail.com; Rech, Paulo C., E-mail: paulo.rech@udesc.br
2015-11-06
We investigate a three-dimensional discrete-time dynamical system, described by a three-dimensional map derived from a continuous-time Hindmarsh–Rose neuron model by the forward Euler method. For a fixed integration step size, we report a two-dimensional parameter-space for this system, where periodic structures, the so-called Arnold tongues, can be seen with periods organized in a Farey tree sequence. We also report possible modifications in this parameter-space, as a function of the integration step size. - Highlights: • We investigate the parameter-space of a particular 3D map. • Periodic structures, namely Arnold tongues, can be seen there. • They are organized in a Farey tree sequence. • The map was derived from a continuous-time Hindmarsh–Rose neuron model. • The forward Euler method was used for such purpose.
Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Xue, Yusheng
2016-03-01
This paper deals with the problem of control synthesis of discrete-time Takagi-Sugeno fuzzy systems by employing a novel multiinstant homogenous polynomial approach. A new multiinstant fuzzy control scheme and a new class of fuzzy Lyapunov functions, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the past-time normalized fuzzy weighting functions, are proposed for implementing the object of relaxed control synthesis. Then, relaxed stabilization conditions are derived with less conservatism than existing ones. Furthermore, the relaxation quality of obtained stabilization conditions is further ameliorated by developing an efficient slack variable approach, which presents a multipolynomial dependence on the normalized fuzzy weighting functions at the current and past instants of time. Two simulation examples are given to demonstrate the effectiveness and benefits of the results developed in this paper.
International Nuclear Information System (INIS)
Shah, S.A.; Shah, W.; Shaikh, F.K.
2012-01-01
Flow time analysis is a powerful concept to analyze the flow time of any arriving customer in any system at any instant. A load management mechanism can be employed very effectively in any queueing system by utilizing a system which provides probability of dual service rate. In this paper, we develop and demonstrate the flow and service processes transition diagram to determine the flow time of a customer in a load management late arrival state dependent finite discrete time queueing system with dual service rate where customers are hypo geometrically distributed. We compute the probability mass function of each starting state and total probability mass function. The obtained analytical results are validated with simulation results for varying values of arrival and service probabilities. (author)
Directory of Open Access Journals (Sweden)
Norman Josephy
2011-01-01
Full Text Available We present a method of optimal hedging and pricing of equity-linked life insurance products in an incomplete discrete-time financial market. A pure endowment life insurance contract with guarantee is used as an example. The financial market incompleteness is caused by the assumption that the underlying risky asset price ratios are distributed in a compact interval, generalizing the assumptions of multinomial incomplete market models. For a range of initial hedging capitals for the embedded financial option, we numerically solve an optimal hedging problem and determine a risk-return profile of each optimal non-self-financing hedging strategy. The fair price of the insurance contract is determined according to the insurer's risk-return preferences. Illustrative numerical results of testing our algorithm on hypothetical insurance contracts are documented. A discussion and a test of a hedging strategy recalibration technique for long-term contracts are presented.
Disease-induced mortality in density-dependent discrete-time S-I-S epidemic models.
Franke, John E; Yakubu, Abdul-Aziz
2008-12-01
The dynamics of simple discrete-time epidemic models without disease-induced mortality are typically characterized by global transcritical bifurcation. We prove that in corresponding models with disease-induced mortality a tiny number of infectious individuals can drive an otherwise persistent population to extinction. Our model with disease-induced mortality supports multiple attractors. In addition, we use a Ricker recruitment function in an SIS model and obtained a three component discrete Hopf (Neimark-Sacker) cycle attractor coexisting with a fixed point attractor. The basin boundaries of the coexisting attractors are fractal in nature, and the example exhibits sensitive dependence of the long-term disease dynamics on initial conditions. Furthermore, we show that in contrast to corresponding models without disease-induced mortality, the disease-free state dynamics do not drive the disease dynamics.
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Huiying Sun
2014-01-01
Full Text Available We mainly consider the stability of discrete-time Markovian jump linear systems with state-dependent noise as well as its linear quadratic (LQ differential games. A necessary and sufficient condition involved with the connection between stochastic Tn-stability of Markovian jump linear systems with state-dependent noise and Lyapunov equation is proposed. And using the theory of stochastic Tn-stability, we give the optimal strategies and the optimal cost values for infinite horizon LQ stochastic differential games. It is demonstrated that the solutions of infinite horizon LQ stochastic differential games are concerned with four coupled generalized algebraic Riccati equations (GAREs. Finally, an iterative algorithm is presented to solve the four coupled GAREs and a simulation example is given to illustrate the effectiveness of it.
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Pablo Soto-Quiros
2015-01-01
Full Text Available This paper presents a parallel implementation of a kind of discrete Fourier transform (DFT: the vector-valued DFT. The vector-valued DFT is a novel tool to analyze the spectra of vector-valued discrete-time signals. This parallel implementation is developed in terms of a mathematical framework with a set of block matrix operations. These block matrix operations contribute to analysis, design, and implementation of parallel algorithms in multicore processors. In this work, an implementation and experimental investigation of the mathematical framework are performed using MATLAB with the Parallel Computing Toolbox. We found that there is advantage to use multicore processors and a parallel computing environment to minimize the high execution time. Additionally, speedup increases when the number of logical processors and length of the signal increase.
DEFF Research Database (Denmark)
Mørkholt, Jakob; Elliott, S.J.; Sors, T.C.
1997-01-01
with a piezoceramic patch control actuator and a point velocity sensor and excited by a point force driven by white noise acting as the primary source. The design objective has been to suppress the effect of the primary disturbance on the output by minimising the mean square value of the output. Apart from comparing......A comparison of three ways of designing optimal discrete time feedback controllers has been carried out via computer simulations. The three design methods are similar in that they are all based on the minimisation of a quadratic cost function under certain assumptions about the disturbance noise...... and sensor noise in the system to be controlled. They are also based on (different) models of the plant under control and the disturbance to be suppressed by the controllers. Controllers based on the three methods have been designed from a model of a lightly damped, rectangular plate fitted...
Game of Life Cellular Automata
Adamatzky, Andrew
2010-01-01
In the late 1960s, British mathematician John Conway invented a virtual mathematical machine that operates on a two-dimensional array of square cell. Each cell takes two states, live and dead. The cells' states are updated simultaneously and in discrete time. A dead cell comes to life if it has exactly three live neighbours. A live cell remains alive if two or three of its neighbours are alive, otherwise the cell dies. Conway's Game of Life became the most programmed solitary game and the most known cellular automaton. The book brings together results of forty years of study into computational
Indian Academy of Sciences (India)
A Ramakalyan1 P Kavitha2 S Harini Vijayalakshmi3. Department of Instrumentation and Control Engineering, Regional Engineering College, Tiruchirapalli 620 015, India. Hindustan Computers Ltd. at Chennai. Texas A & M University. Resonance – Journal of Science Education. Current Issue : Vol. 23, Issue 4.
Directory of Open Access Journals (Sweden)
Rafał Stanisławski
2016-01-01
Full Text Available This paper presents new results on modeling and analysis of dynamics of fractional-order discrete-time linear time-invariant single-input single-output (LTI SISO systems by means of new, two-layer, “fractional-order discrete-time Laguerre filters.” It is interesting that the fractionality of the filters at the upper system dynamics layer is directly projected from the lower Laguerre-based approximation layer for the Grünwald-Letnikov difference. A new stability criterion for discrete-time fractional-order Laguerre-based LTI SISO systems is introduced and supplemented with a stability preservation analysis. Both the stability criterion and the stability preservation analysis bring up rather surprising results, which is illustrated with simulation examples.
Wang, Licheng; Wang, Zidong; Han, Qing-Long; Wei, Guoliang
2017-09-06
The synchronization control problem is investigated for a class of discrete-time dynamical networks with packet dropouts via a coding-decoding-based approach. The data is transmitted through digital communication channels and only the sequence of finite coded signals is sent to the controller. A series of mutually independent Bernoulli distributed random variables is utilized to model the packet dropout phenomenon occurring in the transmissions of coded signals. The purpose of the addressed synchronization control problem is to design a suitable coding-decoding procedure for each node, based on which an efficient decoder-based control protocol is developed to guarantee that the closed-loop network achieves the desired synchronization performance. By applying a modified uniform quantization approach and the Kronecker product technique, criteria for ensuring the detectability of the dynamical network are established by means of the size of the coding alphabet, the coding period and the probability information of packet dropouts. Subsequently, by resorting to the input-to-state stability theory, the desired controller parameter is obtained in terms of the solutions to a certain set of inequality constraints which can be solved effectively via available software packages. Finally, two simulation examples are provided to demonstrate the effectiveness of the obtained results.
Xie, Xiangpeng; Yue, Dong; Zhang, Huaguang; Peng, Chen
2017-09-01
The augmented multi-indexed matrix approach acts as a powerful tool in reducing the conservatism of control synthesis of discrete-time Takagi-Sugeno fuzzy systems. However, its computational burden is sometimes too heavy as a tradeoff. Nowadays, reducing the conservatism whilst alleviating the computational burden becomes an ideal but very challenging problem. This paper is toward finding an efficient way to achieve one of satisfactory answers. Different from the augmented multi-indexed matrix approach in the literature, we aim to design a more efficient slack variable approach under a general framework of homogenous matrix polynomials. Thanks to the introduction of a new extended representation for homogeneous matrix polynomials, related matrices with the same coefficient are collected together into one sole set and thus those redundant terms of the augmented multi-indexed matrix approach can be removed, i.e., the computational burden can be alleviated in this paper. More importantly, due to the fact that more useful information is involved into control design, the conservatism of the proposed approach as well is less than the counterpart of the augmented multi-indexed matrix approach. Finally, numerical experiments are given to show the effectiveness of the proposed approach.
International Nuclear Information System (INIS)
Li Hongjie; Yue Dong
2010-01-01
The paper investigates the synchronization stability problem for a class of complex dynamical networks with Markovian jumping parameters and mixed time delays. The complex networks consist of m modes and the networks switch from one mode to another according to a Markovian chain with known transition probability. The mixed time delays are composed of discrete and distributed delays, the discrete time delay is assumed to be random and its probability distribution is known a priori. In terms of the probability distribution of the delays, the new type of system model with probability-distribution-dependent parameter matrices is proposed. Based on the stochastic analysis techniques and the properties of the Kronecker product, delay-dependent synchronization stability criteria in the mean square are derived in the form of linear matrix inequalities which can be readily solved by using the LMI toolbox in MATLAB, the solvability of derived conditions depends on not only the size of the delay, but also the probability of the delay-taking values in some intervals. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Peidong Liang
2016-01-01
Full Text Available We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG collected from human operator’s arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios.
Tognetti, Eduardo S.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.
2015-01-01
The problem of state feedback control design for discrete-time Takagi-Sugeno (TS) (T-S) fuzzy systems is investigated in this paper. A Lyapunov function, which is quadratic in the state and presents a multi-polynomial dependence on the fuzzy weighting functions at the current and past instants of time, is proposed.This function contains, as particular cases, other previous Lyapunov functions already used in the literature, being able to provide less conservative conditions of control design for TS fuzzy systems. The structure of the proposed Lyapunov function also motivates the design of a new stabilising compensator for Takagi-Sugeno fuzzy systems. The main novelty of the proposed state feedback control law is that the gain is composed of matrices with multi-polynomial dependence on the fuzzy weighting functions at a set of past instants of time, including the current one. The conditions for the existence of a stabilising state feedback control law that minimises an upper bound to the ? or ? norms are given in terms of linear matrix inequalities. Numerical examples show that the approach can be less conservative and more efficient than other methods available in the literature.
The effects of host-feeding on stability of discrete-time host-parasitoid population dynamic models.
Emerick, Brooks; Singh, Abhyudai
2016-02-01
Discrete-time models are the traditional approach for capturing population dynamics of a host-parasitoid system. Recent work has introduced a semi-discrete framework for obtaining model update functions that connect host-parasitoid population levels from year-to-year. In particular, this framework uses differential equations to describe the host-parasitoid interaction during the time of year when they come in contact, allowing specific behaviors to be mechanistically incorporated. We use the semi-discrete approach to study the effects of host-feeding, which occurs when a parasitoid consumes a potential host larva without ovipositing. We find that host-feeding by itself cannot stabilize the system, and both populations exhibit behavior similar to the Nicholson-Bailey model. However, when combined with stabilizing mechanisms such as density-dependent host mortality, host-feeding contracts the region of parameter space that allows for a stable host-parasitoid equilibrium. In contrast, when combined with a density-dependent parasitoid attack rate, host-feeding expands the non-zero equilibrium stability region. Our results show that host-feeding causes inefficiency in the parasitoid population, which yields a higher population of hosts per generation. This suggests that host-feeding may have limited long-term impact in terms of suppressing host levels for biological control applications. Copyright © 2015 Elsevier Inc. All rights reserved.
Sivak, David A; Chodera, John D; Crooks, Gavin E
2014-06-19
When simulating molecular systems using deterministic equations of motion (e.g., Newtonian dynamics), such equations are generally numerically integrated according to a well-developed set of algorithms that share commonly agreed-upon desirable properties. However, for stochastic equations of motion (e.g., Langevin dynamics), there is still broad disagreement over which integration algorithms are most appropriate. While multiple desiderata have been proposed throughout the literature, consensus on which criteria are important is absent, and no published integration scheme satisfies all desiderata simultaneously. Additional nontrivial complications stem from simulating systems driven out of equilibrium using existing stochastic integration schemes in conjunction with recently developed nonequilibrium fluctuation theorems. Here, we examine a family of discrete time integration schemes for Langevin dynamics, assessing how each member satisfies a variety of desiderata that have been enumerated in prior efforts to construct suitable Langevin integrators. We show that the incorporation of a novel time step rescaling in the deterministic updates of position and velocity can correct a number of dynamical defects in these integrators. Finally, we identify a particular splitting (related to the velocity Verlet discretization) that has essentially universally appropriate properties for the simulation of Langevin dynamics for molecular systems in equilibrium, nonequilibrium, and path sampling contexts.
Directory of Open Access Journals (Sweden)
Carlos López-Franco
2015-01-01
Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.
Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors
Directory of Open Access Journals (Sweden)
Alma Y. Alanis
2013-01-01
Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.
International Nuclear Information System (INIS)
Guengoer, N.
1999-05-01
In this study, 134 Cs and 241 Am biokinetic experiments have been observed originally for Black Sea condition with using the gastropod Patella coerulea and the macro algae Enteremorpha linza and the common mussel Mytilus galloprovlncialis as bio indicator organisms. The experiments that have at least three originality, have been carried out very carefully and biokinetic parameters have been calculated by using classical model. The results are compared with the proportionally similar experiments in the literature globally. In this thesis, a new model application has proposed for the biokinetic evaluation for loss experiments of organisms. That is discrete time model. Loss experiments can be evaluated proportionally in a quick and easy way by using this model. Then, it can be observed that the calculated decreasing factor (r) by discrete time model, shows the loss process quality. Moreover, the loss experiments can be stopped when the decreasing factor goes to 1. So, the loss experiment can be evaluated rapidly and quickly with the adequate reliability by using discrete time model. Furthermore, the loss process in the organisms becomes slower and biologic half-lives becomes longer, when the decreasing factor (r) goes to 1. On the other hand, the biological depuration rate (k) goes to nearly zero. So, the proposed discrete time model that is applied in this thesis originally for biokinetic, can be evident that it has an adequate reliability for the biokinetic evaluation
Kepper, Annelies; Koning, Ina; Vollebergh, Wilma; Monshouwer, Karin
2014-01-01
This study investigated the age of onset of substance use among 536 students with mild academic disabilities and 906 students without academic disabilities, and the extent to which emotional, conduct, and hyperactivity problems explain the differences between these two groups. Using discrete-time
Directory of Open Access Journals (Sweden)
2006-01-01
Full Text Available RF circuits for multi-GHz frequencies have recently migrated to low-cost digital deep-submicron CMOS processes. Unfortunately, this process environment, which is optimized only for digital logic and SRAM memory, is extremely unfriendly for conventional analog and RF designs. We present fundamental techniques recently developed that transform the RF and analog circuit design complexity to digitally intensive domain for a wireless RF transceiver, so that it enjoys benefits of digital and switched-capacitor approaches. Direct RF sampling techniques allow great flexibility in reconfigurable radio design. Digital signal processing concepts are used to help relieve analog design complexity, allowing one to reduce cost and power consumption in a reconfigurable design environment. The ideas presented have been used in Texas Instruments to develop two generations of commercial digital RF processors: a single-chip Bluetooth radio and a single-chip GSM radio. We further present details of the RF receiver front end for a GSM radio realized in a 90-nm digital CMOS technology. The circuit consisting of low-noise amplifier, transconductance amplifier, and switching mixer offers 32.5 dB dynamic range with digitally configurable voltage gain of 40 dB down to 7.5 dB. A series of decimation and discrete-time filtering follows the mixer and performs a highly linear second-order lowpass filtering to reject close-in interferers. The front-end gains can be configured with an automatic gain control to select an optimal setting to form a trade-off between noise figure and linearity and to compensate the process and temperature variations. Even under the digital switching activity, noise figure at the 40 dB maximum gain is 1.8 dB and +50 dBm IIP2 at the 34 dB gain. The variation of the input matching versus multiple gains is less than 1 dB. The circuit in total occupies 3.1 mm 2 . The LNA, TA, and mixer consume less than 15.3 mA at a supply voltage of 1.4 V.
Directory of Open Access Journals (Sweden)
Ho Yo-Chuol
2006-01-01
Full Text Available RF circuits for multi-GHz frequencies have recently migrated to low-cost digital deep-submicron CMOS processes. Unfortunately, this process environment, which is optimized only for digital logic and SRAM memory, is extremely unfriendly for conventional analog and RF designs. We present fundamental techniques recently developed that transform the RF and analog circuit design complexity to digitally intensive domain for a wireless RF transceiver, so that it enjoys benefits of digital and switched-capacitor approaches. Direct RF sampling techniques allow great flexibility in reconfigurable radio design. Digital signal processing concepts are used to help relieve analog design complexity, allowing one to reduce cost and power consumption in a reconfigurable design environment. The ideas presented have been used in Texas Instruments to develop two generations of commercial digital RF processors: a single-chip Bluetooth radio and a single-chip GSM radio. We further present details of the RF receiver front end for a GSM radio realized in a 90-nm digital CMOS technology. The circuit consisting of low-noise amplifier, transconductance amplifier, and switching mixer offers dB dynamic range with digitally configurable voltage gain of 40 dB down to dB. A series of decimation and discrete-time filtering follows the mixer and performs a highly linear second-order lowpass filtering to reject close-in interferers. The front-end gains can be configured with an automatic gain control to select an optimal setting to form a trade-off between noise figure and linearity and to compensate the process and temperature variations. Even under the digital switching activity, noise figure at the 40 dB maximum gain is 1.8 dB and dBm IIP2 at the 34 dB gain. The variation of the input matching versus multiple gains is less than 1 dB. The circuit in total occupies 3.1 . The LNA, TA, and mixer consume less than mA at a supply voltage of 1.4 V.
A recurrent neural network with ever changing synapses
Heerema, M.; van Leeuwen, W.A.
2000-01-01
A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the
1992-10-16
Italy 94 10.00 Two Causes of Instability of Celular Neural Networks P. Thiran, EPFL, Lausanne, Switzerland 100 10.20 Convergence of Reciprocal rime... Cloning Template for Image Thicking S. Jankowski, R. Wanczuk, Warsaw Univ. Poland 197 10.20 An Experimental System for Optical Detection of Layout...by the cloning template (the dynamics of a given interaction may be a design parameter); (iv) the mode of operation may be: transient, equilibrium
Casey, Meghan E.
Stem cells are widely used in the area of tissue engineering. The ability of cells to interact with materials on the nano- and micro- level is important in the success of the biomaterial. It is well-known that cells respond to their micro- and nano-environments through a process termed chemo-mechanotransduction. It is important to establish standard protocols for cellular experiments, as chemical modifications to maintenance environments can alter long-term research results. In this work, the effects of different media compositions on human mesenchymal stem cells (hMSCs) throughout normal in vitro maintenance are investigated. Changes in RNA regulation, protein expression and proliferation are studied via quantitative polymerase chain reaction (qPCR), immunocytochemistry (ICC) and cell counts, respectively. Morphological differences are also observed throughout the experiment. Results of this study illustrate the dynamic response of hMSC maintenance to differences in growth medium and passage number. These experiments highlight the effect growth medium has on in vitro experiments and the need of consistent protocols in hMSC research. A substantial opportunity exists in neuronal research to develop a material platform that allows for both the proliferation and differentiation of stem cells into neurons and the ability to quantify the secretome of neuronal cells. Anodic aluminum oxide (AAO) membranes are fabricated in a two-step anodization procedure where voltage is varied to control the pore size and morphology of the membranes. C17.2 neural stem cells are differentiated on the membranes via serum-withdrawal. Cellular growth is characterized by scanning electron microscopy (SEM), ICC and qPCR. ImageJ software is used to obtain phenotypic cell counts and neurite outgrowth lengths. Results indicate a highly tunable correlation between AAO nanopore sizes and differentiated cell populations. By selecting AAO membranes with specific pore size ranges, control of neuronal
Zhang, Zhen; Yan, Peng; Jiang, Huan; Ye, Peiqing
2014-09-01
In this paper, we consider the discrete time-varying internal model-based control design for high precision tracking of complicated reference trajectories generated by time-varying systems. Based on a novel parallel time-varying internal model structure, asymptotic tracking conditions for the design of internal model units are developed, and a low order robust time-varying stabilizer is further synthesized. In a discrete time setting, the high precision tracking control architecture is deployed on a Voice Coil Motor (VCM) actuated servo gantry system, where numerical simulations and real time experimental results are provided, achieving the tracking errors around 3.5‰ for frequency-varying signals. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Minesaki, Yukitaka
2013-01-01
For the restricted three-body problem, we propose an accurate orbital integration scheme that retains all conserved quantities of the two-body problem with two primaries and approximately preserves the Jacobi integral. The scheme is obtained by taking the limit as mass approaches zero in the discrete-time general three-body problem. For a long time interval, the proposed scheme precisely reproduces various periodic orbits that cannot be accurately computed by other generic integrators
International Conference on Artificial Neural Networks (ICANN)
Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics
2015-01-01
The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...
Decentralized neural control application to robotics
Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A
2017-01-01
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...
Orsini, Caitlin A; Hernandez, Caesar M; Singhal, Sarthak; Kelly, Kyle B; Frazier, Charles J; Bizon, Jennifer L; Setlow, Barry
2017-11-29
Decision making is a multifaceted process, consisting of several distinct phases that likely require different cognitive operations. Previous work showed that the basolateral amygdala (BLA) is a critical substrate for decision making involving risk of punishment; however, it is unclear how the BLA is recruited at different stages of the decision process. To this end, the current study used optogenetics to inhibit the BLA during specific task phases in a model of risky decision making (risky decision-making task) in which rats choose between a small, "safe" reward and a large reward accompanied by varying probabilities of footshock punishment. Male Long-Evans rats received intra-BLA microinjections of viral vectors carrying either halorhodopsin (eNpHR3.0-mCherry) or mCherry alone (control) followed by optic fiber implants and were trained in the risky decision-making task. Laser delivery during the task occurred during intertrial interval, deliberation, or reward outcome phases, the latter of which was further divided into the three possible outcomes (small, safe; large, unpunished; large, punished). Inhibition of the BLA selectively during the deliberation phase decreased choice of the large, risky outcome (decreased risky choice). In contrast, BLA inhibition selectively during delivery of the large, punished outcome increased risky choice. Inhibition had no effect during the other phases, nor did laser delivery affect performance in control rats. Collectively, these data indicate that the BLA can either inhibit or promote choice of risky options, depending on the phase of the decision process in which it is active. SIGNIFICANCE STATEMENT To date, most behavioral neuroscience research on neural mechanisms of decision making has used techniques that preclude assessment of distinct phases of the decision process. Here we show that optogenetic inhibition of the BLA has opposite effects on choice behavior in a rat model of risky decision making, depending on the phase
Kamihigashi, Takashi
2017-01-01
Given a sequence [Formula: see text] of measurable functions on a σ -finite measure space such that the integral of each [Formula: see text] as well as that of [Formula: see text] exists in [Formula: see text], we provide a sufficient condition for the following inequality to hold: [Formula: see text] Our condition is considerably weaker than sufficient conditions known in the literature such as uniform integrability (in the case of a finite measure) and equi-integrability. As an application, we obtain a new result on the existence of an optimal path for deterministic infinite-horizon optimization problems in discrete time.
International Nuclear Information System (INIS)
Khorrami, M.
1995-01-01
A general formulation for discrete-time quantum mechanics, based on Feynman's method in ordinary quantum mechanics, is presented. It is shown that the ambiguities present in ordinary quantum mechanics (due to noncommutativity of the operators), are no longer present here. Then the criteria for the unitarity of the evolution operator are examined. It is shown that the unitarity of the evolution operator puts restrictions on the form of the action, and also implies the existence of a solution for the classical initial-value problem. 13 refs
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Ravinder Kaur
2015-10-01
Full Text Available Medulloblastoma (MB is the most common malignant primary pediatric brain tumor and is currently divided into four subtypes based on different genomic alterations, gene expression profiles and response to treatment: WNT, Sonic Hedgehog (SHH, Group 3 and Group 4. This extensive heterogeneity has made it difficult to assess the functional relevance of genes to malignant progression. For example, expression of the transcription factor Orthodenticle homeobox2 (OTX2 is frequently dysregulated in multiple MB variants; however, its role may be subtype specific. We recently demonstrated that neural precursors derived from transformed human embryonic stem cells (trans-hENs, but not their normal counterparts (hENs, resemble Groups 3 and 4 MB in vitro and in vivo. Here, we tested the utility of this model system as a means of dissecting the role of OTX2 in MB using gain- and loss-of-function studies in hENs and trans-hENs, respectively. Parallel experiments with MB cells revealed that OTX2 exerts inhibitory effects on hEN and SHH MB cells by regulating growth, self-renewal and migration in vitro and tumor growth in vivo. This was accompanied by decreased expression of pluripotent genes, such as SOX2, and was supported by overexpression of SOX2 in OTX2+ SHH MB and hENs that resulted in significant rescue of self-renewal and cell migration. By contrast, OTX2 is oncogenic and promotes self-renewal of trans-hENs and Groups 3 and 4 MB independent of pluripotent gene expression. Our results demonstrate a novel role for OTX2 in self-renewal and migration of hENs and MB cells and reveal a cell-context-dependent link between OTX2 and pluripotent genes. Our study underscores the value of human embryonic stem cell derivatives as alternatives to cell lines and heterogeneous patient samples for investigating the contribution of key developmental regulators to MB progression.
Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J
2009-06-01
We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.
Directory of Open Access Journals (Sweden)
Nishiura Hiroshi
2011-02-01
Full Text Available Abstract Background Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. Methods A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009 in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions. Results The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds. Conclusions Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.
F.C. Gruau; J.T. Tromp (John)
1999-01-01
textabstractWe consider the problem of establishing gravity in cellular automata. In particular, when cellular automata states can be partitioned into empty, particle, and wall types, with the latter enclosing rectangular areas, we desire rules that will make the particles fall down and pile up on
International Nuclear Information System (INIS)
Huo Haifeng; Li Wantong
2009-01-01
This paper is concerned with the global stability characteristics of a system of equations modelling the dynamics of continuous-time bidirectional associative memory neural networks with impulses. Sufficient conditions which guarantee the existence of a unique equilibrium and its exponential stability of the networks are obtained. For the goal of computation, discrete-time analogues of the corresponding continuous-time bidirectional associative memory neural networks with impulses are also formulated and studied. Our results show that the above continuous-time and discrete-time systems with impulses preserve the dynamics of the networks without impulses when we make some modifications and impose some additional conditions on the systems, the convergence characteristics dynamics of the networks are preserved by both continuous-time and discrete-time systems with some restriction imposed on the impulse effect.
Reliability analysis of a consecutive r-out-of-n: F system based on neural networks
International Nuclear Information System (INIS)
Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada
2009-01-01
In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.
Chaotic Hopfield Neural Network Swarm Optimization and Its Application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2013-01-01
Full Text Available A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems.
Directory of Open Access Journals (Sweden)
Xing Yin
2011-01-01
uncertain periodic switched recurrent neural networks with time-varying delays. When uncertain discrete-time recurrent neural network is a periodic system, it is expressed as switched neural network for the finite switching state. Based on the switched quadratic Lyapunov functional approach (SQLF and free-weighting matrix approach (FWM, some linear matrix inequality criteria are found to guarantee the delay-dependent asymptotical stability of these systems. Two examples illustrate the exactness of the proposed criteria.
Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan
2018-05-01
Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.
Figueiredo, Danilo Zucolli; Costa, Oswaldo Luiz do Valle
2017-10-01
This paper deals with the H2 optimal control problem of discrete-time Markov jump linear systems (MJLS) considering the case in which the Markov chain takes values in a general Borel space ?. It is assumed that the controller has access only to an output variable and to the jump parameter. The goal, in this case, is to design a dynamic Markov jump controller such that the H2-norm of the closed-loop system is minimised. It is shown that the H2-norm can be written as the sum of two H2-norms, such that one of them does not depend on the control, and the other one is obtained from the optimal filter for an infinite-horizon filtering problem. This result can be seen as a separation principle for MJLS with Markov chain in a Borel space ? considering the infinite time horizon case.
Wang, Jun-Sheng; Yang, Guang-Hong
2017-07-25
This paper studies the optimal output-feedback control problem for unknown linear discrete-time systems with stochastic measurement and process noise. A dithered Bellman equation with the innovation covariance matrix is constructed via the expectation operator given in the form of a finite summation. On this basis, an output-feedback-based approximate dynamic programming method is developed, where the terms depending on the innovation covariance matrix are available with the aid of the innovation covariance matrix identified beforehand. Therefore, by iterating the Bellman equation, the resulting value function can converge to the optimal one in the presence of the aforementioned noise, and the nearly optimal control laws are delivered. To show the effectiveness and the advantages of the proposed approach, a simulation example and a velocity control experiment on a dc machine are employed.
Directory of Open Access Journals (Sweden)
Albert Corominas
Full Text Available A non-linear discrete-time mathematical program model is proposed to determining the optimal extraction policy for a single primary supplier of a durable non-renewable resource, such as gemstones or some metals. Karush, Kuhn and Tucker conditions allow obtaining analytic solutions and general properties of them in some specific settings. Moreover, provided that the objective function (i.e., the discounted value of the incomes throughout the planning horizon is concave, the model can be easily solved, even using standard commercial solver. However, the analysis of the solutions obtained for different assumptions of the values of the parameters show that the optimal extraction policies and the corresponding prices do not exhibit a general shape. Keywords: Durable non-renewable resources, Single primary supplier, Non-linear programming
Kouramas, K.I.
2011-08-01
This work presents a new algorithm for solving the explicit/multi- parametric model predictive control (or mp-MPC) problem for linear, time-invariant discrete-time systems, based on dynamic programming and multi-parametric programming techniques. The algorithm features two key steps: (i) a dynamic programming step, in which the mp-MPC problem is decomposed into a set of smaller subproblems in which only the current control, state variables, and constraints are considered, and (ii) a multi-parametric programming step, in which each subproblem is solved as a convex multi-parametric programming problem, to derive the control variables as an explicit function of the states. The key feature of the proposed method is that it overcomes potential limitations of previous methods for solving multi-parametric programming problems with dynamic programming, such as the need for global optimization for each subproblem of the dynamic programming step. © 2011 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Seung-Jin Yoon
2018-02-01
Full Text Available Inductive-capacitive-inductive (LCL-type filters are currently preferred as a replacement for L-type filters in distributed generation (DG power systems, due to their superior harmonic attenuation capability. However, the third-order dynamics introduced by LCL filters pose a challenge to design a satisfactory controller for such a system. Conventionally, an LCL-filtered grid-connected inverter can be effectively controlled by using a full-state feedback control. However, this control approach requires the measurement of all system state variables, which brings about more complexity for the inverter system. To address this issue, this paper presents a systematic procedure to design an observer-based integral state feedback control for a LCL-filtered grid-connected inverter in the discrete-time domain. The proposed control scheme consists of an integral state feedback controller and a full-state observer which uses the control input, grid-side currents, and grid voltages to predict all the system state variables. Therefore, only the grid-side current sensors and grid voltage sensors are required to implement the proposed control scheme. Due to the discrete-time integrator incorporated in the state feedback controller, the proposed control scheme ensures both the reference tracking and disturbance rejection performance of the inverter system in a practical and simple way. As a result, superior control performance can be achieved by using the reduced number of sensors, which significantly reduces the cost and complexity of the LCL-filtered grid-connected inverter system in DG applications. To verify the practical usefulness of the proposed control scheme, a 2 kW three-phase prototype grid-connected inverter has been constructed, and the proposed control system has been implemented based on 32-bit floating-point digital signal processor (DSP TMS320F28335. The effectiveness of the proposed scheme is demonstrated through the comprehensive simulation
Musci, Rashelle J; Fairman, Brian; Masyn, Katherine E; Uhl, George; Maher, Brion; Sisto, Danielle Y; Kellam, Sheppard G; Ialongo, Nicholas S
2018-01-01
The present study examines the interaction between a polygenic score and an elementary school-based universal preventive intervention trial and its effects on a discrete-time survival analysis of time to first smoking marijuana. Research has suggested that initiation of substances is both genetically and environmentally driven (Rhee et al., Archives of general psychiatry 60:1256-1264, 2003; Verweij et al., Addiction 105:417-430, 2010). A previous work has found a significant interaction between the polygenic score and the same elementary school-based intervention with tobacco smoking (Musci et al., in press). The polygenic score reflects the contribution of multiple genes and has been shown in prior research to be predictive of smoking cessation, tobacco use, and marijuana use (Uhl et al., Molecular Psychiatry 19:50-54, 2014). Using data from a longitudinal preventive intervention study (N = 678), we examined age of first marijuana use from sixth grade to age 18. Genetic data were collected during emerging adulthood and were genotyped using the Affymetrix 6.0 microarray (N = 545). The polygenic score was computed using these data. Discrete-time survival analysis was employed to test for intervention main and interaction effects with the polygenic score. We found main effect of the polygenic score approaching significance, with the participants with higher polygenic scores reporting their first smoking marijuana at an age significantly later than controls (p = .050). We also found a significant intervention × polygenic score interaction effect at p = .003, with participants at the higher end of the polygenic score benefiting the most from the intervention in terms of delayed age of first use. These results suggest that genetics may play an important role in the age of first use of marijuana and that differences in genetics may account for the differential effectiveness of classroom-based interventions in delaying substance use experimentation.
Senan, Sibel; Arik, Sabri
2007-10-01
This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.
Novel results for global robust stability of delayed neural networks
International Nuclear Information System (INIS)
Yucel, Eylem; Arik, Sabri
2009-01-01
This paper investigates the global robust convergence properties of continuous-time neural networks with discrete time delays. By employing suitable Lyapunov functionals, some sufficient conditions for the existence, uniqueness and global robust asymptotic stability of the equilibrium point are derived. The conditions can be easily verified as they can be expressed in terms of the network parameters only. Some numerical examples are also given to compare our results with previous robust stability results derived in the literature.
International Nuclear Information System (INIS)
Hildebrand, C.E.; Walters, R.A.
1977-01-01
Progress is reported on the following research projects: chromatin structure; the use of circular synthetic polydeoxynucleotides as substrates for the study of DNA repair enzymes; human cellular kinetic response following exposure to DNA-interactive compounds; histone phosphorylation and chromatin structure in cell proliferation; photoaddition products induced in chromatin by uv light; pollutants and genetic information transfer; altered RNA metabolism as a function of cadmium accumulation and intracellular distribution in cultured cells; and thymidylate chromophore destruction by water free radicals
Directory of Open Access Journals (Sweden)
Doo Ho Lee
Full Text Available This work studies the optimal pricing strategy in a discrete-time Geo/Geo/1 queuing system under the sojourn time-dependent reward. We consider two types of pricing schemes. The first one is called the ex-post payment scheme where the server charges a price that is proportional to the time a customer spends in the system, and the second one is called ex-ante payment scheme where the server charges a flat price for all services. In each pricing scheme, a departing customer receives the reward that is inversely proportional to his/her sojourn time. The server should make the optimal pricing decisions in order to maximize its expected profits per time unit in each pricing scheme. This work also investigates customer's equilibrium joining or balking behavior under server's optimal pricing strategy. Numerical experiments are also conducted to validate our analysis. Keywords: Optimal pricing, Equilibrium behavior, Geo/Geo/1 queue, Sojourn time-dependent reward
Leander, Jacob; Lundh, Torbjörn; Jirstrand, Mats
2014-05-01
In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh-Nagumo model for excitable media and the Lotka-Volterra predator-prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
Feng, Zhixin; Jones, Kelvyn; Wang, Wenfei Winnie
2015-01-01
This study undertakes a survival analysis of elderly persons in China using Chinese Longitudinal Healthy Longevity Survey 2002–2008. Employing discrete-time multilevel models, we explored the effect of social support on the survival of elderly people in China. This study focuses on objective (living arrangements and received support) and subjective activities (perceived support) of social support, finding that the effect of different activities of social support on the survival of elderly people varies according to the availability of different support resources. Specifically, living with a spouse, financial independence, perceiving care support from any resource is associated with higher survival rates for elderly people. Separate analysis focusing on urban elderly and rural elderly revealed broadly similar results. There is a larger difference between those perceiving care support from family or social service and not perceiving care support in urban areas comparing to those in rural areas. Those who cannot pay medical expenses are the least likely to survive. The higher level of economic development in province has no significant effect on the survival of elderly people for the whole sample model and the elderly people in urban areas; however, there is a negative influence on the survival of the rural elderly people. PMID:25703671
Khulood A. Dagher; Ahmed S. Al-Araji
2013-01-01
A particle swarm optimization algorithm and neural network like self-tuning PID controller for CSTR system is presented. The scheme of the discrete-time PID control structure is based on neural network and tuned the parameters of the PID controller by using a particle swarm optimization PSO technique as a simple and fast training algorithm. The proposed method has advantage that it is not necessary to use a combined structure of identification and decision because it used PSO. Simulation resu...
Sheng, Yin; Zhang, Hao; Zeng, Zhigang
2017-10-01
This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.
International Nuclear Information System (INIS)
Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere
2010-01-01
It is well known that a machine vision-based analysis of a dynamic scene, for example in the context of advanced driver assistance systems (ADAS), does require real-time processing capabilities. Therefore, the system used must be capable of performing both robust and ultrafast analyses. Machine vision in ADAS must fulfil the above requirements when dealing with a dynamically changing visual context (i.e. driving in darkness or in a foggy environment, etc). Among the various challenges related to the analysis of a dynamic scene, this paper focuses on contrast enhancement, which is a well-known basic operation to improve the visual quality of an image (dynamic or static) suffering from poor illumination. The key objective is to develop a systematic and fundamental concept for image contrast enhancement that should be robust despite a dynamic environment and that should fulfil the real-time constraints by ensuring an ultrafast analysis. It is demonstrated that the new approach developed in this paper is capable of fulfilling the expected requirements. The proposed approach combines the good features of the 'coupled oscillators'-based signal processing paradigm with the good features of the 'cellular neural network (CNN)'-based one. The first paradigm in this combination is the 'master system' and consists of a set of coupled nonlinear ordinary differential equations (ODEs) that are (a) the so-called 'van der Pol oscillator' and (b) the so-called 'Duffing oscillator'. It is then implemented or realized on top of a 'slave system' platform consisting of a CNN-processors platform. An offline bifurcation analysis is used to find out, a priori, the windows of parameter settings in which the coupled oscillator system exhibits the best and most appropriate behaviours of interest for an optimal resulting image processing quality. In the frame of the extensive bifurcation analysis carried out, analytical formulae have been derived, which are capable of determining the various
An Optically-Assisted 3-D Cellular Array Machine
National Research Council Canada - National Science Library
Lin, Freddie
1997-01-01
.... In this Phase II project, we developed a discrete-component based Cellular Neural Network (CNN) circuitry, which can perform CNN based analog image processing, such as edge detection and image enhancement, in real time...
International Nuclear Information System (INIS)
Humm, J.L.; Chin, L.M.
1989-01-01
Radiation dose is a useful predictive parameter for describing radiation toxicity in conventional radiotherapy. Traditionally, in vitro radiation biology dose-effect relations are expressed in the form of cell survival curves, a semilog plot of cell survival versus dose. However, the characteristic linear or linear quadratic survival curve shape, for high- and low-LET radiations respectively, is only strictly valid when the radiation dose is uniform across the entire target population. With an external beam of 60 Co gamma rays or x-rays, a uniform field may be readily achievable. When radionuclides are incorporated into a cell milieu, several new problems emerge which can result in a departure from uniformity in energy deposition throughout a cell population. This nonuniformity can have very important consequences for the shape of the survival curve. Cases in which perturbations of source uniformity may arise include: 1. Elemental sources may equilibrate in the cell medium with partition coefficients between the extracellular, cytosol, and nuclear compartments. The effect of preferential cell internalization or binding to cell membrane of some radionuclides can increase or decrease the slope of the survival curve. 2. Radionuclides bound to antibodies, hormones, metabolite precursors, etc., may result in a source localization pattern characteristic of the carrier agent, i.e., the sources may bind to cell surface receptors or antigens, be internalized, bind to secreted antigen concentrated around a fraction of the cell population, or become directly incorporated into the cell DNA. We propose to relate the distribution of energy deposition in cell nuclei to biological correlates of cellular inactivation. The probability of each cell's survival is weighted by its individual radiation burden, and the summation of these probabilities for the cell population can be used to predict the number or fraction of cell survivors
International Nuclear Information System (INIS)
Denby, Bruce; Lindsey, Clark; Lyons, Louis
1992-01-01
The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive
A cryptosystem based on elementary cellular automata
Abdo, A. A.; Lian, Shiguo; Ismail, I. A.; Amin, M.; Diab, H.
2013-01-01
Based on elementary cellular automata, a new image encryption algorithm is proposed in this paper. In this algorithm, a special kind of periodic boundary cellular automata with unity attractors is used. From the viewpoint of security, the number of cellular automata attractor states are changed with respect to the encrypted image, and different key streams are used to encrypt different plain images. The cellular neural network with chaotic properties is used as the generator of a pseudo-random key stream. Theoretical analysis and experimental results have both confirmed that the proposed algorithm possesses high security level and good performances against differential and statistical attacks. The comparison with other existing schemes is given, which shows the superiority of the proposal scheme.
Directory of Open Access Journals (Sweden)
Yongkun Li
2009-01-01
Full Text Available Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory (BAM neural networks with distributed delays and impulses on time scales. This is the first time applying the time-scale calculus theory to unify the discrete-time and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework.
Directory of Open Access Journals (Sweden)
Gh. Assadipour
2012-01-01
Full Text Available
ENGLISH ABSTRACT:The trade-off between time, cost, and quality is one of the important problems of project management. This problem assumes that all project activities can be executed in different modes of cost, time, and quality. Thus a manager should select each activity’s mode such that the project can meet the deadline with the minimum possible cost and the maximum achievable quality. As the problem is NP-hard and the objectives are in conflict with each other, a multi-objective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimisation method. The proposed algorithm provides project managers with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Three metrics are employed for evaluating the performance of the algorithm, appraising the diversity and convergence of the achieved Pareto fronts. Finally a comparison is made between CellDE and another meta-heuristic available in the literature. The results show the superiority of CellDE.
AFRIKAANSE OPSOMMING: ‘n Balans tussen tyd, koste en gehalte is een van die belangrike probleme van projekbestuur. Die vraagstuk maak gewoonlik die aanname dat alle projekaktiwiteite uitgevoer kan word op uiteenlopende wyses wat verband hou met koste, tyd en gehalte. ‘n Projekbestuurder selekteer gewoonlik die uitvoeringsmetodes sodanig per aktiwiteit dat gehoor gegegee word aan minimum koste en maksimum gehalte teen die voorwaarde van voltooiingsdatum wat bereik moet word.
Aangesien die beskrewe problem NP-hard is, word dit behandel ten opsigte van konflikterende doelwitte met ‘n multidoelwit metaheuristiese metode (CellDE. Die metode is ‘n hibride-sellulêre genetiese algoritme. Die algoritme lewer aan die besluitvormer ‘n versameling van ongedomineerde of Pareto
The neural cell adhesion molecule
DEFF Research Database (Denmark)
Berezin, V; Bock, E; Poulsen, F M
2000-01-01
During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...
Towards a magnetoresistive platform for neural signal recording
Sharma, P. P.; Gervasoni, G.; Albisetti, E.; D'Ercoli, F.; Monticelli, M.; Moretti, D.; Forte, N.; Rocchi, A.; Ferrari, G.; Baldelli, P.; Sampietro, M.; Benfenati, F.; Bertacco, R.; Petti, D.
2017-05-01
A promising strategy to get deeper insight on brain functionalities relies on the investigation of neural activities at the cellular and sub-cellular level. In this framework, methods for recording neuron electrical activity have gained interest over the years. Main technological challenges are associated to finding highly sensitive detection schemes, providing considerable spatial and temporal resolution. Moreover, the possibility to perform non-invasive assays would constitute a noteworthy benefit. In this work, we present a magnetoresistive platform for the detection of the action potential propagation in neural cells. Such platform allows, in perspective, the in vitro recording of neural signals arising from single neurons, neural networks and brain slices.
A new delay-independent condition for global robust stability of neural networks with time delays.
Samli, Ruya
2015-06-01
This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bounded. By employing the results of Lyapunov stability theory and matrix theory, new sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for delayed neural networks are presented. The results reported in this paper can be easily tested by checking some special properties of symmetric matrices associated with the parameter uncertainties of neural networks. We also present a numerical example to show the effectiveness of the proposed theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Practical Application of Neural Networks in State Space Control
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon
the networks, although some modifications are needed for the method to apply to the multilayer perceptron network. In connection with the multilayer perceptron networks it is also pointed out how instantaneous, sample-by-sample linearized state space models can be extracted from a trained network, thus opening......In the present thesis we address some problems in discrete-time state space control of nonlinear dynamical systems and attempt to solve them using generic nonlinear models based on artificial neural networks. The main aim of the work is to examine how well such control algorithms perform when...... theoretic notions followed by a detailed description of the topology, neuron functions and learning rules of the two types of neural networks treated in the thesis, the multilayer perceptron and the neurofuzzy networks. In both cases, a Least Squares second-order gradient method is used to train...
International Nuclear Information System (INIS)
Smith, Patrick I.
2003-01-01
Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing
Evolvable synthetic neural system
Curtis, Steven A. (Inventor)
2009-01-01
An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.
Peculiarities of hardware implementation of generalized cellular tetra automaton
Аноприенко, Александр Яковлевич; Федоров, Евгений Евгениевич; Иваница, Сергей Васильевич; Альрабаба, Хамза
2015-01-01
Cellular automata are widely used in many fields of knowledge for the study of variety of complex real processes: computer engineering and computer science, cryptography, mathematics, physics, chemistry, ecology, biology, medicine, epidemiology, geology, architecture, sociology, theory of neural networks. Thus, cellular automata (CA) and tetra automata are gaining relevance taking into account the hardware and software solutions.Also it is marked a trend towards an increase in the number of p...
Wireless Cellular Mobile Communications
Zalud, V.
2002-01-01
In this article is briefly reviewed the history of wireless cellular mobile communications, examined the progress in current second generation (2G) cellular standards and discussed their migration to the third generation (3G). The European 2G cellular standard GSM and its evolution phases GPRS and EDGE are described somewhat in detail. The third generation standard UMTS taking up on GSM/GPRS core network and equipped with a new advanced access network on the basis of code division multiple ac...
Biomechanics of cellular solids.
Gibson, Lorna J
2005-03-01
Materials with a cellular structure are widespread in nature and include wood, cork, plant parenchyma and trabecular bone. Natural cellular materials are often mechanically efficient: the honeycomb-like microstructure of wood, for instance, gives it an exceptionally high performance index for resisting bending and buckling. Here we review the mechanics of a wide range of natural cellular materials and examine their role in lightweight natural sandwich structures (e.g. iris leaves) and natural tubular structures (e.g. plant stems or animal quills). We also describe two examples of engineered biomaterials with a cellular structure, designed to replace or regenerate tissue in the body.
ChainMail based neural dynamics modeling of soft tissue deformation for surgical simulation.
Zhang, Jinao; Zhong, Yongmin; Smith, Julian; Gu, Chengfan
2017-07-20
Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.
Discrete time modelization of human pilot behavior
Cavalli, D.; Soulatges, D.
1975-01-01
This modelization starts from the following hypotheses: pilot's behavior is a time discrete process, he can perform only one task at a time and his operating mode depends on the considered flight subphase. Pilot's behavior was observed using an electro oculometer and a simulator cockpit. A FORTRAN program has been elaborated using two strategies. The first one is a Markovian process in which the successive instrument readings are governed by a matrix of conditional probabilities. In the second one, strategy is an heuristic process and the concepts of mental load and performance are described. The results of the two aspects have been compared with simulation data.
Discrete-time modelling of musical instruments
International Nuclear Information System (INIS)
Vaelimaeki, Vesa; Pakarinen, Jyri; Erkut, Cumhur; Karjalainen, Matti
2006-01-01
This article describes physical modelling techniques that can be used for simulating musical instruments. The methods are closely related to digital signal processing. They discretize the system with respect to time, because the aim is to run the simulation using a computer. The physics-based modelling methods can be classified as mass-spring, modal, wave digital, finite difference, digital waveguide and source-filter models. We present the basic theory and a discussion on possible extensions for each modelling technique. For some methods, a simple model example is chosen from the existing literature demonstrating a typical use of the method. For instance, in the case of the digital waveguide modelling technique a vibrating string model is discussed, and in the case of the wave digital filter technique we present a classical piano hammer model. We tackle some nonlinear and time-varying models and include new results on the digital waveguide modelling of a nonlinear string. Current trends and future directions in physical modelling of musical instruments are discussed
Modeling discrete time-to-event data
Tutz, Gerhard
2016-01-01
This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are expla...
Wang, Dongshu; Huang, Lihong
2014-03-01
In this paper, we investigate the periodic dynamical behaviors for a class of general Cohen-Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and the fixed point theorem of multi-valued maps, the existence of periodic solutions for the neural networks is obtained. After that, we derive some sufficient conditions for the global exponential stability and convergence of the neural networks, in terms of nonsmooth analysis theory with generalized Lyapunov approach. Without assuming the boundedness (or the growth condition) and monotonicity of the discontinuous neuron activation functions, our results will also be valid. Moreover, our results extend previous works not only on discrete time-varying and distributed delayed neural networks with continuous or even Lipschitz continuous activations, but also on discrete time-varying and distributed delayed neural networks with discontinuous activations. We give some numerical examples to show the applicability and effectiveness of our main results. Copyright © 2013 Elsevier Ltd. All rights reserved.
Linearizable cellular automata
International Nuclear Information System (INIS)
Nobe, Atsushi; Yura, Fumitaka
2007-01-01
The initial value problem for a class of reversible elementary cellular automata with periodic boundaries is reduced to an initial-boundary value problem for a class of linear systems on a finite commutative ring Z 2 . Moreover, a family of such linearizable cellular automata is given
Evaluating Lyapunov exponent spectra with neural networks
International Nuclear Information System (INIS)
Maus, A.; Sprott, J.C.
2013-01-01
Highlights: • Cross-correlation is employed to remove spurious Lyapunov exponents from a spectrum. • Neural networks are shown to accurately model Lyapunov exponent spectra. • Neural networks compare favorably to local linear fits in modeling Lyapunov exponents. • Numerical experiments are performed with time series of varying length and noise. • Methods perform reasonably well on discrete time series. -- Abstract: A method using discrete cross-correlation for identifying and removing spurious Lyapunov exponents when embedding experimental data in a dimension greater than the original system is introduced. The method uses a distribution of calculated exponent values produced by modeling a single time series many times or multiple instances of a time series. For this task, global models are shown to compare favorably to local models traditionally used for time series taken from the Hénon map and delayed Hénon map, especially when the time series are short or contaminated by noise. An additional merit of global modeling is its ability to estimate the dynamical and geometrical properties of the original system such as the attractor dimension, entropy, and lag space, although consideration must be taken for the time it takes to train the global models
Heterogeneous cellular networks
Hu, Rose Qingyang
2013-01-01
A timely publication providing coverage of radio resource management, mobility management and standardization in heterogeneous cellular networks The topic of heterogeneous cellular networks has gained momentum in industry and the research community, attracting the attention of standardization bodies such as 3GPP LTE and IEEE 802.16j, whose objectives are looking into increasing the capacity and coverage of the cellular networks. This book focuses on recent progresses, covering the related topics including scenarios of heterogeneous network deployment, interference management i
Cellular decomposition in vikalloys
International Nuclear Information System (INIS)
Belyatskaya, I.S.; Vintajkin, E.Z.; Georgieva, I.Ya.; Golikov, V.A.; Udovenko, V.A.
1981-01-01
Austenite decomposition in Fe-Co-V and Fe-Co-V-Ni alloys at 475-600 deg C is investigated. The cellular decomposition in ternary alloys results in the formation of bcc (ordered) and fcc structures, and in quaternary alloys - bcc (ordered) and 12R structures. The cellular 12R structure results from the emergence of stacking faults in the fcc lattice with irregular spacing in four layers. The cellular decomposition results in a high-dispersion structure and magnetic properties approaching the level of well-known vikalloys [ru
Romanofsky, Robert R.
2010-01-01
The cellular reflectarray antenna is intended to replace conventional parabolic reflectors that must be physically aligned with a particular satellite in geostationary orbit. These arrays are designed for specified geographical locations, defined by latitude and longitude, each called a "cell." A particular cell occupies nominally 1,500 square miles (3,885 sq. km), but this varies according to latitude and longitude. The cellular reflectarray antenna designed for a particular cell is simply positioned to align with magnetic North, and the antenna surface is level (parallel to the ground). A given cellular reflectarray antenna will not operate in any other cell.
Linear reversible second-order cellular automata and their first-order matrix equivalents
Macfarlane, A. J.
2004-11-01
Linear or one-dimensional reversible second-order cellular automata, exemplified by three cases named as RCA1-3, are introduced. Displays of their evolution in discrete time steps, &{\\in}Z_2;) as for RCA1-3. MCA1-3 are tractable because it has been possible to generalize to them the heavy duty methods already well-developed for ordinary first-order cellular automata like those of Wolfram's Rules 90 and 150. While the automata MCA1-3 are thought to be of genuine interest in their own right, with untapped further mathematical potential, their treatment has been applied here to expediting derivation of a large body of general and explicit results for N(t) for RCA1-3. Amongst explicit results obtained are formulas also for each of RCA1-3 for the total weight of the configurations of the first &2^M; times, M =0, 1, 2,\\ldots.
Directory of Open Access Journals (Sweden)
Schwindling Jerome
2010-04-01
Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.
Directory of Open Access Journals (Sweden)
Takashi Kamihigashi
2017-01-01
Full Text Available Abstract Given a sequence { f n } n ∈ N $\\{f_{n}\\}_{n \\in \\mathbb {N}}$ of measurable functions on a σ-finite measure space such that the integral of each f n $f_{n}$ as well as that of lim sup n ↑ ∞ f n $\\limsup_{n \\uparrow\\infty} f_{n}$ exists in R ‾ $\\overline{\\mathbb {R}}$ , we provide a sufficient condition for the following inequality to hold: lim sup n ↑ ∞ ∫ f n d μ ≤ ∫ lim sup n ↑ ∞ f n d μ . $$ \\limsup_{n \\uparrow\\infty} \\int f_{n} \\,d\\mu\\leq \\int\\limsup_{n \\uparrow\\infty} f_{n} \\,d\\mu. $$ Our condition is considerably weaker than sufficient conditions known in the literature such as uniform integrability (in the case of a finite measure and equi-integrability. As an application, we obtain a new result on the existence of an optimal path for deterministic infinite-horizon optimization problems in discrete time.
Zhang, Xian-Ming; Han, Qing-Long; Ge, Xiaohua
2017-09-22
This paper is concerned with the problem of robust H∞ control of an uncertain discrete-time Takagi-Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust H∞ fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.
Magnetohydrodynamics cellular automata
International Nuclear Information System (INIS)
Hatori, Tadatsugu.
1990-02-01
There has been a renewal of interest in cellular automata, partly because they give an architecture for a special purpose computer with parallel processing optimized to solve a particular problem. The lattice gas cellular automata are briefly surveyed, which are recently developed to solve partial differential equations such as hydrodynamics or magnetohydrodynamics. A new model is given in the present paper to implement the magnetic Lorentz force in a more deterministic and local procedure than the previous one. (author)
Epigenetics and Cellular Metabolism
Wenyi Xu; Fengzhong Wang; Zhongsheng Yu; Fengjiao Xin
2016-01-01
Living eukaryotic systems evolve delicate cellular mechanisms for responding to various environmental signals. Among them, epigenetic machinery (DNA methylation, histone modifications, microRNAs, etc.) is the hub in transducing external stimuli into transcriptional response. Emerging evidence reveals the concept that epigenetic signatures are essential for the proper maintenance of cellular metabolism. On the other hand, the metabolite, a main environmental input, can also influence the proce...
Matthäus, Franziska; Pahle, Jürgen
2017-01-01
This contributed volume comprises research articles and reviews on topics connected to the mathematical modeling of cellular systems. These contributions cover signaling pathways, stochastic effects, cell motility and mechanics, pattern formation processes, as well as multi-scale approaches. All authors attended the workshop on "Modeling Cellular Systems" which took place in Heidelberg in October 2014. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.
Magnetohydrodynamic cellular automata
Energy Technology Data Exchange (ETDEWEB)
Hatori, Tadatsugu [National Inst. for Fusion Science, Nagoya (Japan)
1990-03-01
There has been a renewal of interest in cellular automata, partly because they give an architecture for a special purpose computer with parallel processing optimized to solve a particular problem. The lattice gas cellular automata are briefly surveyed, which are recently developed to solve partial differential equations such as hydrodynamics or magnetohydrodynamics. A new model is given in the present paper to implement the magnetic Lorentz force in a more deterministic and local procedure than the previous one. (author).
Magnetohydrodynamic cellular automata
International Nuclear Information System (INIS)
Hatori, Tadatsugu
1990-01-01
There has been a renewal of interest in cellular automata, partly because they give an architecture for a special purpose computer with parallel processing optimized to solve a particular problem. The lattice gas cellular automata are briefly surveyed, which are recently developed to solve partial differential equations such as hydrodynamics or magnetohydrodynamics. A new model is given in the present paper to implement the magnetic Lorentz force in a more deterministic and local procedure than the previous one. (author)
Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent
Directory of Open Access Journals (Sweden)
Ibnkahla Mohamed
2003-01-01
Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.
Cellular Mechanisms of Somatic Stem Cell Aging
Jung, Yunjoon
2014-01-01
Tissue homeostasis and regenerative capacity rely on rare populations of somatic stem cells endowed with the potential to self-renew and differentiate. During aging, many tissues show a decline in regenerative potential coupled with a loss of stem cell function. Cells including somatic stem cells have evolved a series of checks and balances to sense and repair cellular damage to maximize tissue function. However, during aging the mechanisms that protect normal cell function begin to fail. In this review, we will discuss how common cellular mechanisms that maintain tissue fidelity and organismal lifespan impact somatic stem cell function. We will highlight context-dependent changes and commonalities that define aging, by focusing on three age-sensitive stem cell compartments: blood, neural, and muscle. Understanding the interaction between extrinsic regulators and intrinsic effectors that operate within different stem cell compartments is likely to have important implications for identifying strategies to improve health span and treat age-related degenerative diseases. PMID:24439814
Directory of Open Access Journals (Sweden)
Michel Modo
2005-07-01
Full Text Available Cellular MR imaging is a young field that aims to visualize targeted cells in living organisms. In order to provide a different signal intensity of the targeted cell, they are either labeled with MR contrast agents in vivo or prelabeled in vitro. Either (ultrasmall superparamagnetic iron oxide [(USPIO] particles or (polymeric paramagnetic chelates can be used for this purpose. For in vivo cellular labeling, Gd3+- and Mn2+- chelates have mainly been used for targeted hepatobiliary imaging, and (USPIO-based cellular imaging has been focused on imaging of macrophage activity. Several of these magneto-pharmaceuticals have been FDA-approved or are in late-phase clinical trials. As for prelabeling of cells in vitro, a challenge has been to induce a sufficient uptake of contrast agents into nonphagocytic cells, without affecting normal cellular function. It appears that this issue has now largely been resolved, leading to an active research on monitoring the cellular biodistribution in vivo following transplantation or transfusion of these cells, including cell migration and trafficking. New applications of cellular MR imaging will be directed, for instance, towards our understanding of hematopoietic (immune cell trafficking and of novel guided (stem cell-based therapies aimed to be translated to the clinic in the future.
Wu, Wei; Cui, Bao-Tong
2007-07-01
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
Neural, Cellular and Molecular Mechanisms of Active Forgetting
Medina, Jorge H.
2018-01-01
The neurobiology of memory formation attracts much attention in the last five decades. Conversely, the rules that govern and the mechanisms underlying forgetting are less understood. In addition to retroactive interference, retrieval-induced forgetting and passive decay of time, it has been recently demonstrated that the nervous system has a diversity of active and inherent processes involved in forgetting. In Drosophila, some operate mainly at an early stage of memory formation and involves dopamine (DA) neurons, specific postsynaptic DA receptor subtypes, Rac1 activation and induces rapid active forgetting. In mammals, others regulate forgetting and persistence of seemingly consolidated memories and implicate the activity of DA receptor subtypes and AMPA receptors in the hippocampus (HP) and related structures to activate parallel signaling pathways controlling active time-dependent forgetting. Most of them may involve plastic changes in synaptic and extrasynaptic receptors including specific removal of GluA2 AMPA receptors. Forgetting at longer timescales might also include changes in adult neurogenesis in the dentate gyrus (DG) of the HP. Therefore, based on relevance or value considerations neuronal circuits may regulate in a time-dependent manner what is formed, stored, and maintained and what is forgotten. PMID:29467630
Cellularized Cellular Solids via Freeze-Casting.
Christoph, Sarah; Kwiatoszynski, Julien; Coradin, Thibaud; Fernandes, Francisco M
2016-02-01
The elaboration of metabolically active cell-containing materials is a decisive step toward the successful application of cell based technologies. The present work unveils a new process allowing to simultaneously encapsulate living cells and shaping cell-containing materials into solid-state macroporous foams with precisely controlled morphology. Our strategy is based on freeze casting, an ice templating materials processing technique that has recently emerged for the structuration of colloids into macroporous materials. Our results indicate that it is possible to combine the precise structuration of the materials with cellular metabolic activity for the model organism Saccharomyces cerevisiae. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Epigenetics and Cellular Metabolism
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Wenyi Xu
2016-01-01
Full Text Available Living eukaryotic systems evolve delicate cellular mechanisms for responding to various environmental signals. Among them, epigenetic machinery (DNA methylation, histone modifications, microRNAs, etc. is the hub in transducing external stimuli into transcriptional response. Emerging evidence reveals the concept that epigenetic signatures are essential for the proper maintenance of cellular metabolism. On the other hand, the metabolite, a main environmental input, can also influence the processing of epigenetic memory. Here, we summarize the recent research progress in the epigenetic regulation of cellular metabolism and discuss how the dysfunction of epigenetic machineries influences the development of metabolic disorders such as diabetes and obesity; then, we focus on discussing the notion that manipulating metabolites, the fuel of cell metabolism, can function as a strategy for interfering epigenetic machinery and its related disease progression as well.
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Wireless Cellular Mobile Communications
Directory of Open Access Journals (Sweden)
V. Zalud
2002-12-01
Full Text Available In this article is briefly reviewed the history of wireless cellularmobile communications, examined the progress in current secondgeneration (2G cellular standards and discussed their migration to thethird generation (3G. The European 2G cellular standard GSM and itsevolution phases GPRS and EDGE are described somewhat in detail. Thethird generation standard UMTS taking up on GSM/GPRS core network andequipped with a new advanced access network on the basis of codedivision multiple access (CDMA is investigated too. A sketch of theperspective of mobile communication beyond 3G concludes this article.
Radiolabelled cellular blood elements
International Nuclear Information System (INIS)
Sinzinger, H.
1990-01-01
This book reports on radiolabelled cellular blood elements, covering new advances made during the past several years, in particular the use of Tc-99 as a tracer for blood elements. Coverage extends to several radiolabelled monoclonal antibodies that are specific for blood components and may label blood elements in vivo