Approximating polynomial functions by feedforward artificial neural networks: Capacity analysis and design

DOI10.1016/S0096-3003(96)00338-4zbMath0901.68173OpenAlexW1973568182MaRDI QIDQ1126664

YingQing Zhou, Behnam B. Malakooti

Publication date: 2 August 1998

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0096-3003(96)00338-4

zbMATH Keywords

feedforward artificial neural networks

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Approximation by polynomials (41A10)

Related Items (7)

Solving group technology problems via clique partitioning ⋮ Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm ⋮ Training artificial neural networks by a hybrid PSO-CS algorithm ⋮ The essential order of simultaneous approximation for neural networks ⋮ A deep learning approach for efficiently and accurately evaluating the flow field of supercritical airfoils ⋮ Generalized polynomial decomposable multiple attribute utility functions for ranking and rating multiple criteria discrete alternatives. ⋮ Extremist vs. centrist decision behavior: Quasi-convex utility functions for interactive multi-objective linear programming problems

Cites Work

• A feedforward neural network for multiple criteria decision making
• Multilayer feedforward networks are universal approximators
• A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation
• Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems
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