Stability Analysis of Neural Networks - Rajchakit, Grienggrai; Agarwal, Praveen; Ramalingam, Sriraman; - Prospero Internetes Könyváruház
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Értesítés a kedvenc témákról
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Stability Analysis of Neural Networks
 
A termék adatai:
ISBN13:9789811665332
ISBN10:9811665338
Kötéstípus:Keménykötés
Terjedelem:404 oldal
Méret:235x155 mm
Súly:811 g
Nyelv:angol
Illusztrációk: 2 Illustrations, black & white; 54 Illustrations, color
277
Témakör:

Stability Analysis of Neural Networks

 
Kiadás sorszáma: 1st ed. 2021
Kiadó: Springer
Megjelenés dátuma:
Kötetek száma: 1 pieces, Book
 
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EUR 139.09
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Rövid leírás:

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. 

Hosszú leírás:

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. 



?This book presents the foundations of stability analysis of neural networks in a well-organized manner. ? This book will be very helpful to the research community working in the area of stability of dynamical systems, especially neural networks. Readers who are familiar with the basics of differential equations will find it very comfortable.? (Raju K. George, Mathematical Reviews, February, 2023)

?The book ? completes successfully the known lists of monograph dealing with neural networks. In fact any researcher or student dedicated to one of the topics tackled throughout the book should use it and for sure (s)he will be rewarded from the scientific point of view.? (Vladimir Răsvan, zbMATH 1485.93004, 2022)
Tartalomjegyzék:
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.