Efficient estimation of neural weights by polynomial approximation

DOI10.1109/18.771153zbMath0952.65026OpenAlexW2145726976MaRDI QIDQ4701411

Gunter Ritter

Publication date: 21 November 1999

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/17c4913cc4dbdbd190b33ecea330f0b8388aba8b

zbMATH Keywords

complexityalgorithmspolynomial approximationforward neural networksnearly exponential activation functionneural network with one hidden layerorder of uniform approximationuniform-density problem

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computation of special functions and constants, construction of tables (65D20) Approximation by polynomials (41A10) Complexity and performance of numerical algorithms (65Y20) Exponential and trigonometric functions (33B10)

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CONSTRUCTIVE ESTIMATION OF APPROXIMATION FOR TRIGONOMETRIC NEURAL NETWORKS ⋮ Lower estimation of approximation rate for neural networks ⋮ A counterexample regarding ``New study on neural networks: the essential order of approximation ⋮ On sharpness of error bounds for multivariate neural network approximation ⋮ New study on neural networks: the essential order of approximation ⋮ The essential order of approximation for nearly exponential type neural networks ⋮ On sharpness of error bounds for univariate approximation by single hidden layer feedforward neural networks

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