Distributed Leakage Delays Research Articles

H∞ State Estimation for BAM Neural Networks With Binary Mode Switching and Distributed Leakage Delays Under Periodic Scheduling Protocol.

This article is concerned with the H∞ state estimation problem for a class of bidirectional associative memory (BAM) neural networks with binary mode switching, where the distributed delays are included in the leakage terms. A couple of stochastic variables taking values of 1 or 0 are introduced to characterize the switching behavior between the redundant models of the BAM neural network, and a general type of neuron activation function (i.e., the sector-bounded nonlinearity) is considered. In order to prevent the data transmissions from collisions, a periodic scheduling protocol (i.e., round-robin protocol) is adopted to orchestrate the transmission order of sensors. The purpose of this work is to develop a full-order estimator such that the error dynamics of the state estimation is exponentially mean-square stable and the H∞ performance requirement of the output estimation error is also achieved. Sufficient conditions are established to ensure the existence of the required estimator by constructing a mode-dependent Lyapunov-Krasovskii functional. Then, the desired estimator parameters are obtained by solving a set of matrix inequalities. Finally, a numerical example is provided to show the effectiveness of the proposed estimator design method.

Effect of Proportional Delays and Continuously Distributed Leakage Delays on Global Exponential Convergence of CNNS

AbstractThis paper is concerned with a class of cellular neural networks (CNNs) with proportional delays and continuously distributed leakage delays. Using the differential inequality theory, a set of sufficient conditions which guarantee the exponential convergence of all solutions of CNNs with proportional delays and continuously distributed leakage delays are derived. Computer simulations are carried out to verify our theoretical findings. The obtained results of this paper are new and complement some previous studies.

Almost automorphic solution for neutral type high-order Hopfield BAM neural networks with time-varying leakage delays on time scales

In this paper, by applying the exponential dichotomy theory on time scales, Banach contraction mapping principle and differential inequality analysis techniques, we establish some sufficient conditions for the existence and global exponential stability of almost automorphic solution for neutral type high-order Hopfield bidirectional associative memory (BAM) neural networks with time-varying leakage delays on time scales. In the addressed system, we not only consider the effects of the first-order neutral terms on networks, but also investigate the influences of the second-order neutral terms on the networks. High-order Hopfield BAM neural networks with continuously distributed leakage delays on time scales are also considered. Our results are completely new even if time scale T=R or T=Z and complementary to the previously existing results. Finally, three examples with numerical simulations are presented to illustrate the feasibility of our proposed theoretical results.

The Sampled-data Exponential Stability of BAM with Distributed Leakage Delays

This paper investigates the exponential stability of bidirectional associative memory (BAM) neural networks with distributed leakage delays and sampled-data state feedback input. Based on the input delay approach, the considered BAM neural networks is transformed into a system with mixed distributed leakage delays and time-varying discrete delays. Some sufficient conditions are given to ensure that the system is exponentially stable. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical analysis.

Exponential stability for fuzzy BAM cellular neural networks with distributed leakage delays and impulses

This paper is concerned with a class of fuzzy BAM cellular neural networks with distributed leakage delays and impulses. By applying differential inequality techniques, we establish some sufficient conditions which ensure the exponential stability of such fuzzy BAM cellular neural networks. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this article are completely new and complement the previously known studies.

Existence and Global Exponential Stability of Almost Periodic Solutions for Shunting Inhibitory Cellular Neural Networks with Distributed Leakage Delays on Time Scales

In this paper, we deal with a class of shunting inhibitory cellular neural networks (SICNNs) with distributed leakage delays on time scales. Some sufficient conditions which ensure the existence and exponential stability of almost periodic solutions for such class of SICNNs are obtained by applying the exponential dichotomy of linear differential equations, Lapunov functional method and contraction mapping principle. An example is given to illustrate the effectiveness of the theoretical results. The results obtained in this paper are completely new and complement the previously known studies.

Exponential Stability of Almost Periodic Solutions for Memristor-Based Neural Networks with Distributed Leakage Delays.

In this letter, we deal with a class of memristor-based neural networks with distributed leakage delays. By applying a new Lyapunov function method, we obtain some sufficient conditions that ensure the existence, uniqueness, and global exponential stability of almost periodic solutions of neural networks. We apply the results of this solution to prove the existence and stability of periodic solutions for this delayed neural network with periodic coefficients. We then provide an example to illustrate the effectiveness of the theoretical results. Our results are completely new and complement the previous studies Chen, Zeng, and Jiang ( 2014 ) and Jiang, Zeng, and Chen ( 2015 ).

Existence and exponential stability of almost periodic solutions for neutral‐type BAM neural networks with distributed leakage delays

This paper is concerned with a class of neutral‐type BAM neural networks with distributed leakage delays. By applying the exponential dichotomy of linear differential equations, Lyapunov functional method and contraction mapping principle, we establish some sufficient conditions which ensure the existence and exponential stability of almost periodic solutions for such BAM neural networks. An example is given to illustrate the effectiveness of the theoretical findings. The results obtained in this article are completely new and complement the previously known studies. Copyright © 2016 John Wiley & Sons, Ltd.

Almost periodic solution for a neutral-type neural networks with distributed leakage delays on time scales

By using exponential dichotomy theory, contraction mapping principle and discrete-continuous analysis method, we obtain some new sufficient conditions ensuring the existence and global exponential stability of the almost periodic solutions for a class of neutral-type neural networks with distributed leakage delays on time scales. An example is provided to demonstrate the usefulness of the main results obtained.

Almost periodic solutions for neutral type BAM neural networks with distributed leakage delays on time scales

This paper is concerned with a class of neutral type BAM neural networks with distributed leakage delays on time scales. By using the exponential dichotomy theory, Lyapunov functional method and contraction mapping principle, some sufficient conditions for the existence and global exponential stability of the almost periodic solutions for this model are obtained. An example is given to illustrate effectiveness of the obtained theoretical results.

Pseudo almost periodic solutions for neutral type CNNs with continuously distributed leakage delays

Abstract This paper is concerned with a new generalized neutral type cellular neural network model with continuously distributed leakage delays. Some criteria are established for the existence and global exponential stability of pseudo almost periodic solutions for this model by using the exponential dichotomy theory, contraction mapping fixed point theorem and inequality analysis technique. Moreover, an example and its numerical simulation are employed to illustrate the main results.

Pseudo Almost Periodic Solutions for CNNs with Continuously Distributed Leakage Delays

In this paper, based on the exponential dichotomy theory, contraction mapping fixed point theorem and inequality analysis technique, we obtain some sufficient conditions ensuring the existence and global exponential stability of pseudo almost periodic solutions for a new generalized cellular neural network model with continuously distributed leakage delays. Our results complement with some recent ones. Moreover, an illustrative example and its numerical simulation are given to demonstrate the effectiveness of the obtained results.

New Results on Exponential Convergence for Cellular Neural Networks with Continuously Distributed Leakage Delays

This paper discusses the issue of a class of cellular neural networks with continuously distributed delays in the leakage terms. By applying Lyapunov functional method and differential inequality techniques, without assuming the boundedness conditions on the activation functions, a new delay dependent sufficient condition is derived to ensure that all solutions of the networks converge exponentially to the zero point, which corrects some recent results of Xiong and Meng (Electron J Qual Theory Differ Equ, (10):1---12, 2013. http://www.math.u-szeged.hu/ejqtde/).

Periodic Solutions of BAM Neural Networks with Continuously Distributed Delays in the Leakage Terms

This paper concerns with bidirectional associative memory neural network model with continuously distributed leakage delays. By applying Lyapunov functional method and differential inequality techniques, we establish some sufficient conditions for the existence and exponential stability of periodic solutions for the model, which complement with some recent ones. Moreover, an example and its numerical simulation are given to support the theoretical results.

Exponential convergence of Cohen-Grossberg neural networks with continuously distributed leakage delays

This paper is concerned with the global exponential convergence of Cohen-Grossberg neural networks with continuously distributed leakage delays. By using the Lyapunov functional method and differential inequality techniques, we propose a new approach to establishing some sufficient conditions ensuring that all solutions of the networks converge exponentially to the zero point. Our results complement some recent ones.MSC: 34C25, 34K13, 34K25.

New Results on Exponential Convergence for HRNNs with Continuously Distributed Delays in the Leakage Terms

This paper concerns with exponential convergence for a class of high-order recurrent neural networks with continuously distributed delays in the leakage terms. Without assuming the boundedness on the activation functions, some sufficient conditions are derived to ensure that all solutions of the networks converge exponentially to the zero point by using Lyapunov functional method and differential inequality techniques, which correct some recent results of Chen and Yang (Neural Comput Appl. doi: 10.1007/s00521-012-1172-2 , 2012). Moreover, we propose a new approach to prove the exponential convergence of HRNNs with continuously distributed leakage delays.

Existence and stability of almost periodic solutions for CNNs with continuously distributed leakage delays

In this paper, a class of nonlinear nonautonomous cellular neural networks system with the introduction of continuously distributed delays in the leakage (or forgetting) terms is considered. By using Lyapunov functional method and differential inequality techniques, without assuming the boundedness on the activation functions, we derive a set of easily verifiable sufficient conditions that guarantee the existence and exponential stability of almost periodic solutions for the system. Moreover, an example and its numerical simulation are given to demonstrate the feasibility of our method.

Global Exponential Stability of Almost Periodic Solutions for SICNNs with Continuously Distributed Leakage Delays

Shunting inhibitory cellular neural networks (SICNNs) are considered with the introduction of continuously distributed delays in the leakage (or forgetting) terms. By using the Lyapunov functional method and differential inequality techniques, some sufficient conditions for the existence and exponential stability of almost periodic solutions are established. Our results complement with some recent ones.