Approximating polynomial functions by feedforward artificial neural networks: Capacity analysis and design
type: Item
instance of: scholarly article
title: Approximating polynomial functions by feedforward artificial neural networks: Capacity analysis and design
zbMATH Open document ID: 0901.68173
DOI: 10.1016/S0096-3003(96)00338-4
author: YingQing Zhou
author: Behnam B. Malakooti
published in: Applied Mathematics and Computation
publication date: 1998-08-02T00:00:00Z
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 41A10
zbMATH DE Number: 1183208
zbMATH Keywords: feedforward artificial neural networks
MaRDI profile type: Publication
cites work: Q3995433
cites work: Multilayer feedforward networks are universal approximators
cites work: Q3950274
cites work: Q5619754
cites work: Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems
cites work: Q4141653
cites work: A feedforward neural network for multiple criteria decision making
cites work: Q4322463
cites work: A variable-parameter unsupervised learning clustering neural network approach with application to machine-part group formation
full work available at URL: s0096-3003(96)00338-4
OpenAlex ID: W1973568182
description: scientific article; zbMATH DE number 1183208