Banach Space Valued Neural Network: Ordinary and Fractional Approximation and Interpolation [Paperback]

This book is about the generalization and modernization of approximation by neural network operators. Functions under approximation and the neural networks are Banach space valued. These are induced by a great variety of activation functions deriving from the arctangent, algebraic, Gudermannian, and generalized symmetric sigmoid functions. Ordinary, fractional, fuzzy, and stochastic approximations are exhibited at the univariate, fractional, and multivariate levels. Iterated-sequential approximations are also covered. The books results are expected to find applications in the many areas of applied mathematics, computer science and engineering, especially in artificial intelligence and machine learning. Other possible applications can be in applied sciences like statistics, economics, etc. Therefore, this book is suitable for researchers, graduate students, practitioners, and seminars of the above disciplines, also to be in all science and engineering libraries.
Algebraic function induced Banach space valued ordinary and fractional neural network approximations.- Gudermannian function induced Banach space valued ordinary and fractional neural network approximations.- Generalized symmetrical sigmoid function induced Banach space valued ordinary and fractional neural network approximations.- Abstract multivariate algebraic function induced neural network approximations.- General multivariate arctangent function induced neural network approximations.- Abstract multivariate Gudermannian function induced neural network approximations.- Generalized symmetrical sigmoid function induced neural network multivariate approximation.- Quantitative Approximation by Kantorovich-Choquet quasi-interpolation neural network operators revisited.- Quantitative Approximation by Kantorovich-Shilkret quasi-interpolation neural network operators revisited.- Voronsovkaya Univariate and Multivariate asymptotic expansions for sigmoid functions induced quasi-interpolationneural networklÓE