Stability Analysis of Neural Networks (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	December 2021
First published:	2021
Authors:	Grienggrai Rajchakit • Praveen Agarwal • Sriraman Ramalingam
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	404
Edition:	1st ed. 2021
ISBN-13:	978-981-16-6533-2
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling Books > Science & Mathematics > Mathematics > Applied mathematics > Non-linear science Books > Professional & Technical > Mechanical engineering & materials > Materials science > Mechanics of solids > Dynamics & vibration Promotions
LSN:	981-16-6533-8
Barcode:	9789811665332

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