Journal ArticleDOI
Exponential synchronization of Markovian jumping chaotic neural networks with sampled-data and saturating actuators
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TLDR
In this paper, the problem of exponential synchronization of Markovian jumping chaotic neural networks with saturating actuators using a sampled-data controller was solved by constructing a proper Lyapunov-Krasovskii functional with triple integral terms, and employing Jensen's inequality.About:
This article is published in Nonlinear Analysis: Hybrid Systems.The article was published on 2017-05-01. It has received 85 citations till now. The article focuses on the topics: Synchronization of chaos & Linear matrix inequality.read more
Citations
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Journal ArticleDOI
Reliable asynchronous sampled-data filtering of T–S fuzzy uncertain delayed neural networks with stochastic switched topologies
TL;DR: The intermittent fault-tolerance scheme is taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable.
Journal ArticleDOI
An Improved Result on Exponential Stabilization of Sampled-Data Fuzzy Systems
TL;DR: New exponential stabilization criteria dependent on and independent of upper bounds on time derivatives of fuzzy basis functions are established, by which a larger sampling interval can be achieved.
Journal ArticleDOI
Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout
TL;DR: A novel stochastic switched sampled-data controller with time-varying sampling is developed in the frame of the zero-input strategy and novel synchronization criteria are established to guarantee that DCNNs are synchronous exponentially when the control packet dropout occurs in a random way.
Journal ArticleDOI
New reliable nonuniform sampling control for uncertain chaotic neural networks under Markov switching topologies
TL;DR: A new stochastic reliable nonuniform sampling controller with Markov switching topologies is designed for the first time to reflect more realistic control behaviors and to guarantee that UCNNs are synchronous exponentially under probabilistic actuator and sensor faults.
Journal ArticleDOI
Event-triggered synchronization of discrete-time neural networks: A switching approach.
Sanbo Ding,Zhanshan Wang +1 more
TL;DR: A new event-triggered mechanism (ETM) is presented, which can be regarded as a switching between the discrete-time periodic sampled-data control and a continuous ETM; a saturating controller which is equipped with two switching gains is designed to match the switching property of the proposed ETM.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Synchronization in chaotic systems
TL;DR: This chapter describes the linking of two chaotic systems with a common signal or signals and highlights that when the signs of the Lyapunov exponents for the subsystems are all negative the systems are synchronized.
Journal ArticleDOI
Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
TL;DR: In this article, a class of information processing systems called cellular neural networks (CNNs) are proposed, which consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly through their nearest neighbors.
Journal ArticleDOI
Synchronization in complex networks
Alex Arenas,Alex Arenas,Albert Díaz-Guilera,Albert Díaz-Guilera,Jürgen Kurths,Jürgen Kurths,Yamir Moreno,Changsong Zhou +7 more
TL;DR: The advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology are reported and the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections are overviewed.
Book
Neural networks for optimization and signal processing
A. Cochocki,Rolf Unbehauen +1 more
TL;DR: A guide to the fundamental mathematics of neurocomputing, a review of neural network models and an analysis of their associated algorithms, and state-of-the-art procedures to solve optimization problems are explained.
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