東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Stability analysis of neural networks

Rajchakit, Grienggrai.

Linked to FindBook

Google Book

Amazon

博客來

Stability analysis of neural networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stability analysis of neural networks/ by Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam.
Author:	Rajchakit, Grienggrai.
other author:	Agarwal, Praveen.
Published:	Singapore :Springer Singapore : : 2021.,
Description:	xxvi, 404 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. LMI-Based Stability Criteria for BAM Neural Networks -- 3. Exponential Stability Criteria for Uncertain Inertial BAM Neural Networks -- 4. Exponential Stability of Impulsive Cohen-Grossberg BAM Neural Networks -- 5. Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects -- 6. Stability of Markovian Jumping Stochastic Impulsive Uncertain BAM Neural Networks -- 7. Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks -- 8. Exponential Stability of Discrete-Time Cellular Uncertain BAM Neural Networks -- 9. Exponential Stability of Discrete-Time Stochastic Impulsive BAM Neural Networks -- 10. Stability of Discrete-Time Stochastic Quaternion-Valued Neural Networks -- 11. Robust Finite-Time Passivity of Markovian Jump Discrete-Time BAM Neural Networks -- 12 Robust Stability of Discrete-Time Stochastic Genetic Regulatory Networks.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/978-981-16-6534-9
ISBN:	9789811665349

Stability analysis of neural networks
Rajchakit, Grienggrai.

Stability analysis of neural networks[electronic resource] /by Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam. - Singapore :Springer Singapore :2021. - xxvi, 404 p. :ill., digital ;24 cm.

1. Introduction -- 2. LMI-Based Stability Criteria for BAM Neural Networks -- 3. Exponential Stability Criteria for Uncertain Inertial BAM Neural Networks -- 4. Exponential Stability of Impulsive Cohen-Grossberg BAM Neural Networks -- 5. Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects -- 6. Stability of Markovian Jumping Stochastic Impulsive Uncertain BAM Neural Networks -- 7. Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks -- 8. Exponential Stability of Discrete-Time Cellular Uncertain BAM Neural Networks -- 9. Exponential Stability of Discrete-Time Stochastic Impulsive BAM Neural Networks -- 10. Stability of Discrete-Time Stochastic Quaternion-Valued Neural Networks -- 11. Robust Finite-Time Passivity of Markovian Jump Discrete-Time BAM Neural Networks -- 12 Robust Stability of Discrete-Time Stochastic Genetic Regulatory Networks.

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.

ISBN: 9789811665349

Standard No.: 10.1007/978-981-16-6534-9doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .R35 2021

Dewey Class. No.: 006.32

Stability analysis of neural networks
LDR:02751nmm a2200325 a 4500 001 2262125
003 DE-He213
005 20211205200951.0
006 m d
007 cr nn 008maaau
008 220616s2021 si s 0 eng d
020 $a 9789811665349 $q (electronic bk.)
020 $a 9789811665332 $q (paper)
024 7 $a 10.1007/978-981-16-6534-9 $2 doi
035 $a 978-981-16-6534-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .R35 2021
072 7 $a PBWH $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBWH $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .R161 2021
100 1 $a Rajchakit, Grienggrai. $3 3538197
245 1 0 $a Stability analysis of neural networks $h [electronic resource] / $c by Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a xxvi, 404 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. LMI-Based Stability Criteria for BAM Neural Networks -- 3. Exponential Stability Criteria for Uncertain Inertial BAM Neural Networks -- 4. Exponential Stability of Impulsive Cohen-Grossberg BAM Neural Networks -- 5. Exponential Stability of Recurrent Neural Networks with Impulsive and Stochastic Effects -- 6. Stability of Markovian Jumping Stochastic Impulsive Uncertain BAM Neural Networks -- 7. Global Robust Exponential Stability of Stochastic Neutral-Type Neural Networks -- 8. Exponential Stability of Discrete-Time Cellular Uncertain BAM Neural Networks -- 9. Exponential Stability of Discrete-Time Stochastic Impulsive BAM Neural Networks -- 10. Stability of Discrete-Time Stochastic Quaternion-Valued Neural Networks -- 11. Robust Finite-Time Passivity of Markovian Jump Discrete-Time BAM Neural Networks -- 12 Robust Stability of Discrete-Time Stochastic Genetic Regulatory Networks.
520 $a 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.
650 0 $a Neural networks (Computer science) $3 532070
650 1 4 $a Mathematical Models of Cognitive Processes and Neural Networks. $3 1619875
650 2 4 $a Dynamical Systems and Ergodic Theory. $3 891276
650 2 4 $a Vibration, Dynamical Systems, Control. $3 893843
700 1 $a Agarwal, Praveen. $3 3518532
700 1 $a Ramalingam, Sriraman. $3 3538198
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-16-6534-9
950 $a Mathematics and Statistics (SpringerNature-11649)