LEADER 03719nam 22005775 450 001 9910512156903321 005 20251113181752.0 010 $a981-16-6534-6 024 7 $a10.1007/978-981-16-6534-9 035 $a(MiAaPQ)EBC6823046 035 $a(Au-PeEL)EBL6823046 035 $a(CKB)20067294500041 035 $a(PPN)259390720 035 $a(OCoLC)1288217026 035 $a(DE-He213)978-981-16-6534-9 035 $a(EXLCZ)9920067294500041 100 $a20211205d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStability Analysis of Neural Networks /$fby Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021. 215 $a1 online resource (xxvi, 404 pages) $cillustrations 225 1 $aMathematics and Statistics Series 311 08$aPrint version: Rajchakit, Grienggrai Stability Analysis of Neural Networks Singapore : Springer Singapore Pte. Limited,c2022 9789811665332 320 $aIncludes bibliographical references and index. 327 $a1. 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. 330 $aThis book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists. . 410 0$aMathematics and Statistics Series 606 $aNeural networks (Computer science) 606 $aDynamics 606 $aMathematical Models of Cognitive Processes and Neural Networks 606 $aDynamical Systems 615 0$aNeural networks (Computer science) 615 0$aDynamics. 615 14$aMathematical Models of Cognitive Processes and Neural Networks. 615 24$aDynamical Systems. 676 $a780 700 $aRajchakit$b Grienggrai$01069380 702 $aAgarwal$b Praveen 702 $aRamalingam$b Sriraman 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910512156903321 996 $aStability analysis of neural networks$92816956 997 $aUNINA