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2016 | Buch

Stability and Synchronization Control of Stochastic Neural Networks

verfasst von: Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Systems, Decision and Control

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SUCHEN

Über dieses Buch

This book reports on the latest findings in the study of Stochastic Neural Networks (SNN). The book collects the novel model of the disturbance driven by Levy process, the research method of M-matrix, and the adaptive control method of the SNN in the context of stability and synchronization control. The book will be of interest to university researchers, graduate students in control science and engineering and neural networks who wish to learn the core principles, methods, algorithms and applications of SNN.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Relative Mathematic Foundation
Abstract
In this chapter, we will present some concepts and formulas as well as several important inequalities which will be used throughout this book. We will begin with some elementary concepts and formulas, such as stochastic processes and martingales, SDEs, M-matrix, and Itô’s formula. Then some inequalities frequently used in this book will follow in the sequel.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 2. Exponential Stability and Synchronization Control of Neural Networks
Abstract
In this chapter, we are concerned with exponential stability analysis for neural networks with fuzzy logical BAM and Markovian jump and synchronization control problem of stochastically coupled neural networks.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 3. Robust Stability and Synchronization of Neural Networks
Abstract
In this chapter, the robust stability of high-order neural networks and hybrid stochastic neural networks is first investigated. The robust anti-synchronization and robust lag synchronization of chaotic neural networks are discussed in the sequel.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 4. Adaptive Synchronization of Neural Networks
Abstract
The adaptive control strategy has been widely adopted due to its well performance in uncertain systems such as stochastic systems or nonlinear systems. In this chapter, adaptive control is designed for the synchronization of some kinds of neural networks including BAMDNN, SDNN with Markovian switching and T-S fuzzy NN.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 5. Stability and Synchronization of Neutral-Type Neural Networks
Abstract
When the states of a system are decided not only by states of the current time and the past time but also by the derivative of the past states, the system can be called a neutral system. The problems of stability and synchronization of neutral neural networks play an important role in the same issues of neural networks. In this chapter, robust stability of neutral neural networks is first discussed.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 6. Stability and Synchronization of Neural Networks with Lévy Noise
Abstract
As a simple model of jump diffusions, Lévy noise is in a more general sense with respect to the description of neural noise than Brownian motion does. This chapter is concentrated on the stability and synchronization issues of neural networks with Lévy noise. Almost surely exponential stability and pth moment asymptotic stability for such networks are discussed in the first two sections. Synchronization via sampled data and adaptive synchronization are investigated in the rest two sections.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Chapter 7. Some Applications to Economy Based on Related Research Method
Abstract
This chapter provides two applications with respect to the topic of this book in finance and economy. As an application of Lévy process, Sect. 7.1 offers a portfolio strategy of financial market. Robust \(H_\infty \) control strategy is investigated for a generic linear rational expectation model of economy.
Wuneng Zhou, Jun Yang, Liuwei Zhou, Dongbing Tong
Backmatter
Metadaten
Titel
Stability and Synchronization Control of Stochastic Neural Networks
verfasst von
Wuneng Zhou
Jun Yang
Liuwei Zhou
Dongbing Tong
Copyright-Jahr
2016
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-47833-2
Print ISBN
978-3-662-47832-5
DOI
https://doi.org/10.1007/978-3-662-47833-2

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