[FDE2] Algebraic function based Banach space valued ordinary and fractional neural network approximations

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Speaker(s)	George Anastassiou University of Memphis
Date	28 February 2022 – 18:15 to 19:15
Venue	INI Seminar Room 2
Session Title	Algebraic function based Banach space valued ordinary and fractional neural network approximations
Event	[FDE2] Fractional differential equations
Abstract	Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by an algebraic sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.
Presentation Files	34979_0.pdf, 34979_1.pptx

Algebraic function based Banach space valued ordinary and fractional neural network approximations

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