04063nam 22006375 450 991098308460332120250227005429.09783031689666303168966610.1007/978-3-031-68966-6(MiAaPQ)EBC31738058(Au-PeEL)EBL31738058(CKB)36389222800041(DE-He213)978-3-031-68966-6(OCoLC)1463766865(EXLCZ)993638922280004120241023d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierArtificial Neural Networks Alpha Unpredictability and Chaotic Dynamics /by Marat Akhmet, Madina Tleubergenova, Akylbek Zhamanshin, Zakhira Nugayeva1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (256 pages)9783031689659 3031689658 Preface -- 1. Introduction -- 2. Preliminaries -- 3. Hopfield-type neural networks -- 4. Shunting inhibitory cellular neural networks -- 5. Inertial neural networks with discontinuities -- 6. Cohen-Grossberg neural networks.Mathematical chaos in neural networks is a powerful tool that reflects the world’s complexity and has the potential to uncover the mysteries of the brain’s intellectual activity. Through this monograph, the authors aim to contribute to modern chaos research, combining it with the fundamentals of classical dynamical systems and differential equations. The readers should be reassured that an in-depth understanding of chaos theory is not a prerequisite for working in the area designed by the authors. Those interested in the discussion can have a basic understanding of ordinary differential equations and the existence of bounded solutions of quasi-linear systems on the real axis. Based on the novelties, this monograph aims to provide one of the most powerful approaches to studying complexities in neural networks through mathematical methods in differential equations and, consequently, to create circumstances for a deep comprehension of brain activity and artificial intelligence. A large part of the book consists of newly obtained contributions to the theory of recurrent functions, Poisson stable, and alpha unpredictable solutions and ultra Poincaré chaos of quasi-linear and strongly nonlinear neural networks such as Hopfield neural networks, shunting inhibitory cellular neural networks, inertial neural networks, and Cohen-Grossberg neural networks. The methods and results presented in this book are meant to benefit senior researchers, engineers, and specialists working in artificial neural networks, machine and deep learning, computer science, quantum computers, and applied and pure mathematics. This broad applicability underscores the value and relevance of this research area to a large academic community and the potential impact it can have on various fields. .Artificial intelligenceMachine learningNeural networks (Computer science)Artificial IntelligenceMachine LearningMathematical Models of Cognitive Processes and Neural NetworksStatistical LearningArtificial intelligence.Machine learning.Neural networks (Computer science)Artificial Intelligence.Machine Learning.Mathematical Models of Cognitive Processes and Neural Networks.Statistical Learning.006.3Akhmet Marat478701Tleubergenova Madina1785854Zhamanshin Akylbek1785855Nugayeva Zakhira1785856MiAaPQMiAaPQMiAaPQBOOK9910983084603321Artificial Neural Networks4317306UNINA01931nam0 22004573i 450 VAN0029329820250805105858.472N978146122684020250523d1994 |0itac50 baengUS|||| |||||i e bcrProbabilistic Causality in Longitudinal StudiesMervi EerolaNew York [etc.]Springer-Verlag1994viii, 131 p.24 cm001VAN000019572001 Lecture notes in statistics210 New York [etc.]Springer1980-9260G35Signal detection and filtering (aspects of stochastic processes) [MSC 2020]VANC021485MF60G55Point processes (e.g., Poisson, Cox, Hawkes processes) [MSC 2020]VANC024268MF62-XXStatistics [MSC 2020]VANC022998MF62PxxApplications of statistics [MSC 2020]VANC027777MFCensoringKW:KInnovationKW:KLogistic RegressionKW:KLongitudinal studiesKW:KPoint processesKW:KRandomized experimentsKW:KStatisticsKW:KUSNew YorkVANL000011EerolaMerviVANV249172442003Springer <editore>VANV108073650ITSOL20250905RICAhttps://doi.org/10.1007/978-1-4612-2684-0E-book – Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICAIT-CE0120VAN08NVAN00293298BIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA08DLOAD e-Book 11710 08eMF11710 20250630 Probabilistic causality in longitudinal studies82505UNICAMPANIA