Deadzone compensation based on constrained RBF neural network
type: Item
instance of: scholarly article
title: Deadzone compensation based on constrained RBF neural network
zbMATH Open document ID: 1063.93015
author: Chiung-Hsin Tsai
author: Han-Tung Chuang
published in: Journal of the Franklin Institute
publication date: 2005-02-16T00:00:00Z
review text: Two neural networks are used for the compensation of a deadzone. A radial basis function (RBF) neural network is used for the identification of the parameters. Then the RBF network acts as a single layer neural network and is trained based on the gradient descent technique. The result is used to train the other neural network for the inverse compensation of the deadzone.
reviewed by: Angela Slavova
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 2134374
zbMATH Keywords: RBF neural network
zbMATH Keywords: inverse deadzone compensation
zbMATH Keywords: back propagation method
zbMATH Keywords: identification
zbMATH Keywords: gradient descent technique
MaRDI profile type: Publication
full work available at URL: j.jfranklin.2004.01.001
OpenAlex ID: W2038077973
cites work: Q4210718
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DOI: 10.1016/J.JFRANKLIN.2004.01.001
description: scientific article; zbMATH DE number 2134374