Solving polynomial systems using a fast adaptive back propagation-type neural network algorithm

DOI10.1017/S0956792517000146zbMath1391.65146OpenAlexW2705236166MaRDI QIDQ4575282

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Publication date: 13 July 2018

Published in: European Journal of Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/s0956792517000146

zbMATH Keywords

neural networkspolynomial systemsnumerical analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Numerical computation of solutions to systems of equations (65H10)

Cites Work

• Approximation by max-product neural network operators of Kantorovich type
• Rooms with a view: a novel approach to iterated multidimensional wave conversion
• The approximation operators with sigmoidal functions
• Neural network operators: constructive interpolation of multivariate functions
• Finding all real roots of \(3\times 3\) nonlinear algebraic systems using neural networks
• Numerical experience with Newton-like methods for nonlinear algebraic systems
• Pointwise and uniform approximation by multivariate neural network operators of the max-product type
• Max-product neural network and quasi-interpolation operators activated by sigmoidal functions
• Sizing and Least-Change Secant Methods
• Universal approximation bounds for superpositions of a sigmoidal function
• On the Local and Superlinear Convergence of Quasi-Newton Methods
• A Class of Methods for Solving Nonlinear Simultaneous Equations
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