Stability Analysis of Neural Networks

Grienggrai Rajchakit; Praveen Agarwal; Sriraman Ramalingam

doi:10.1007/978-981-16-6534-9

Stability Analysis of Neural Networks

Grienggrai Rajchakit
, Praveen Agarwal
, Sriraman Ramalingam

Nonlinear Dynamic Research Centre (NDRC)

Maejo University
Kalasalingam University

Research output: Book/Report › Book › peer-review

111 Scopus citations

Abstract

This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.

Original language	English
Publisher	Springer Nature
Number of pages	404
ISBN (Electronic)	9789811665349
ISBN (Print)	9789811665332
DOIs	https://doi.org/10.1007/978-981-16-6534-9
State	Published - 1 Jan 2021

Keywords

Cohen-Grossberg neural network
Hopfield neural network
Lyapunov-Krasovskii functional
Markovian jumping
asymptotic stability
bidirectional associative memory
cellular neural network
exponential stability
gene regulatory network
neural networks
robust stability
stability

Access to Document

10.1007/978-981-16-6534-9

Cite this

@book{213fc18cc5b54b0b879afeaa6295f8a5,

title = "Stability Analysis of Neural Networks",

abstract = "This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.",

keywords = "Cohen-Grossberg neural network, Hopfield neural network, Lyapunov-Krasovskii functional, Markovian jumping, asymptotic stability, bidirectional associative memory, cellular neural network, exponential stability, gene regulatory network, neural networks, robust stability, stability",

author = "Grienggrai Rajchakit and Praveen Agarwal and Sriraman Ramalingam",

note = "Publisher Copyright: {\textcopyright} The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.",

year = "2021",

month = jan,

day = "1",

doi = "10.1007/978-981-16-6534-9",

language = "English",

isbn = "9789811665332",

publisher = "Springer Nature",

}

TY - BOOK

T1 - Stability Analysis of Neural Networks

AU - Rajchakit, Grienggrai

AU - Agarwal, Praveen

AU - Ramalingam, Sriraman

N1 - Publisher Copyright: © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.

AB - This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.

KW - Cohen-Grossberg neural network

KW - Hopfield neural network

KW - Lyapunov-Krasovskii functional

KW - Markovian jumping

KW - asymptotic stability

KW - bidirectional associative memory

KW - cellular neural network

KW - exponential stability

KW - gene regulatory network

KW - neural networks

KW - robust stability

KW - stability

UR - https://www.scopus.com/pages/publications/85133284845

U2 - 10.1007/978-981-16-6534-9

DO - 10.1007/978-981-16-6534-9

M3 - Book

AN - SCOPUS:85133284845

SN - 9789811665332

BT - Stability Analysis of Neural Networks

PB - Springer Nature

ER -

Stability Analysis of Neural Networks

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this