Titlebook: Artificial Neural Networks; Alpha Unpredictabili Marat Akhmet,Madina Tleubergenova,Zakhira Nugayeva Book 2025 The Editor(s) (if applicable)

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978-3-031-68968-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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https://doi.org/10.1007/978-3-540-71512-2de, introducing critical theorems and lemmas, which are instrumental for exploring neural networks in the subsequent chapters. It begins with discussing the basic properties and types of functions encountered in this work, emphasizing their roles in modeling dynamic systems. Key concepts such as Poi

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Neuronal Dynamics and Brain Connectivityfocus is on a model featuring compartmental periodic alpha unpredictable coefficients and input data. An algorithm that expands the alpha unpredictable functions by applying diagonalization to the arguments of functions of several variables is proposed. Sufficient conditions for the existence and un

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https://doi.org/10.1007/978-1-4615-2375-8ly, the investigation focuses on a specific neural network architecture, where the impulse structure replicates that of rates. This choice mirrors real-world system behavior, where voltage typically exhibits smooth continuity but occasionally undergoes sudden changes due to factors like switches, su

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Handbook of Breadmaking Technology model with variable inputs and strengths of connectivity, which are alpha unpredictable or Poisson stable functions. A method of reducing a nonlinear model into quasi-linear systems using an integral transformation is presented. This approach helps analyze complex nonlinear systems, making applying