Parametrized, Deformed and General Neural Networks [Hardcover]

In this book, we introduce the parametrized, deformed and general activation function of neural networks. The parametrized activation function kills much less neurons than the original one. The asymmetry of the brain is best expressed by deformed activation functions. Along with a great variety of activation functions, general activation functions are also engaged. Thus, in this book, all presented is original work by the author given at a very general level to cover a maximum number of different kinds of neural networks: giving ordinary, fractional, fuzzy and stochastic approximations. It presents here univariate, fractional and multivariate approximations. Iterated sequential multi-layer approximations are also studied. The functions under approximation and neural networks are Banach space valued.

Abstract ordinary and fractional neural network approximations based on Richards curve.- Abstract Multivariate Neural Network Approximation based on Richards curve.- Parametrized hyperbolic tangent based Banach space valued basic and fractional neural network approximations.- Parametrized hyperbolic tangent induced Banach space valued multivariate multi layer neural network approximations.- Banach space valued neural network approximation based on a parametrized arctangent sigmoid function.- Parametrized arctangent activated Banach space valued multi layer neural network multivariate approximation.- Banach space valued Ordinary and Fractional neural networks approximations based on the parametrized Gudermannian function.- Parametrized Gudermannian activation function based Banach space valued neural network multivariate approximation.- Banach space valued univariate neural network approximation based on parametrized error activation function.- Banach space valued multivariate multi layer neural network approximation based on parametrized error activation function.- Hyperbolic Tangent Like based univariate Banach space valued neural network approximatilS2