Learning radial basis function networks with the trust region method for boundary problems

DOI10.1134/S0005117918090072zbMath1490.65296OpenAlexW4254897365MaRDI QIDQ1992271

L. N. Elisov, Maxim Valerievich Zhukov, Vladimir Ivanovich Gorbachenko

Publication date: 5 November 2018

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117918090072

zbMATH Keywords

radial basis function networkslearning of neural networksboundary value problems of mathematical physicsmethod of trust region

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Spectral, collocation and related methods for boundary value problems involving PDEs (65N35) Neural networks for/in biological studies, artificial life and related topics (92B20) PDEs on graphs and networks (ramified or polygonal spaces) (35R02)

Related Items (1)

Training RBF neural networks for the solution of elliptic boundary value problems

Uses Software

• Matlab
• Mfree2D
• ORBIT

Cites Work

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• Recent advances on radial basis function collocation methods
• Trust-region methods for nonlinear elliptic equations with radial basis functions
• Adaptive radial basis function methods for time dependent partial differential equations
• An introduction to neural network methods for differential equations
• Solving boundary value problems of mathematical physics using radial basis function networks
• Multilayer perceptrons and radial basis function neural network methods for the solution of differential equations: a survey
• ORBIT: Optimization by Radial Basis Function Interpolation in Trust-Regions
• The Conjugate Gradient Method and Trust Regions in Large Scale Optimization
• Radial Basis Functions
• Trust Region Methods
• Solving PDEs with radial basis functions
• Function minimization by conjugate gradients
• Solving high order ordinary differential equations with radial basis function networks

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