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Stability analysis of neural networks

Agarwal, Praveen.

Stability analysis of neural networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stability analysis of neural networksby Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam.
作者:	Rajchakit, Grienggrai.
其他作者:	Agarwal, Praveen.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xxvi, 404 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Neural networks (Computer science)
電子資源:	https://doi.org/10.1007/978-981-16-6534-9
ISBN:	9789811665349$q(electronic bk.)

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$q(electronic bk.)

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

181982
Neural networks (Computer science)

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

Dewey Class. No.: 006.32

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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.
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