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Parametrized, Deformed and General Neural Networks 2023 ed. [Mīkstie vāki]

  • Formāts: Paperback / softback, 853 pages, height x width: 235x155 mm, 1 Illustrations, black and white; XVIII, 853 p. 1 illus., 1 Paperback / softback
  • Sērija : Studies in Computational Intelligence 1116
  • Izdošanas datums: 03-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031430239
  • ISBN-13: 9783031430237
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Parametrized, Deformed and General Neural Networks 2023 ed.Palielināt
  • Formāts: Paperback / softback, 853 pages, height x width: 235x155 mm, 1 Illustrations, black and white; XVIII, 853 p. 1 illus., 1 Paperback / softback
  • Sērija : Studies in Computational Intelligence 1116
  • Izdošanas datums: 03-Oct-2024
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031430239
  • ISBN-13: 9783031430237
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783031430237.html
Keywords:

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 approximation.- Banach space valued neural
network multivariate approximation based on hyperbolic tangent like
activation function.- Banach space valued ordinary and fractional neural
network approximations based on q-deformed hyperbolic tangent activation
function.- Banach space valued multivariate multi layer neural network
approximation based on q-deformed hyperbolic tangent activation function.-
Banach space valued multivariate multi layer neural network approximation
based on q-deformed and -parametrized A-generalized logistic function.-
Banach space valued ordinary and fractional neural network approximation
based on q-deformed and -parametrized A-generalized logistic function.-
Banach space valued multivariate multi layer neural network approximation
based on q-deformed and -parametrized hyperbolic tangent function.-
q-Deformed and -parametrized hyperbolic tangent based Banach space valued
ordinary and fractional neural network approximation.- Banach space valued
multivariate multi layer neural network approximation based on q-Deformed and
parametrized half hyperbolic tangent.- Banach space valued ordinary and
fractional neural network approximation based on q-deformed and
-parametrized half hyperbolic tangent.- General sigmoid relied Banach space
valued neural network approximation.- General sigmoid induced Banach space
valued neural network multivariate approximation.- Fuzzy basic and fractional
general sigmoid function generated neural network approximation.-
Multivariate Fuzzy Approximation by Neural Network Operators induced by a
general sigmoid function.- Multivariate Fuzzy-Random and stochastic general
sigmoid activation function generated Neural Network Approximations.-
Voronovskaya type asymptotic expansions for general sigmoid functions induced
quasi-interpolation neural network operators.- Multiple general sigmoids
activated Banach space valued neural network multivariate approximation.-
Quantitative Approximation by Multiple sigmoids KantorovichChoquet
quasi-interpolation neural network operators.- Degree of Approximation by
Multiple sigmoids KantorovichShilkret quasi-interpolation neural network
operators.- Approximation by Neural Networks of Brownian Motion.- Neural
Networks Approximation of Time Separating Stochastic Processes.- Fractional
Calculus between Banach spaces together with Ostrowski and GrØuss kind of
inequalities.- Sequential Fractional Calculus between Banach spaces and
corresponding Ostrowski and GrØuss kind of inequalities.