Modelling of the automatic depth control electrohydraulic system using RBF neural network and genetic algorithm
type: Item
instance of: scholarly article
title: Modelling of the automatic depth control electrohydraulic system using RBF neural network and genetic algorithm
zbMATH Open document ID: 1202.93097
DOI: 10.1155/2010/124014
author: Zong-Yi Xing
author: Yong Qin
author: Xue-Miao Pang
author: Limin Jia
author: Yuan Zhang
published in: Mathematical Problems in Engineering
publication date: 2010-12-23T00:00:00Z
full work available at URL: https://eudml.org/doc/227732
review text: Summary: The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 76M25
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 5829220
Wikidata QID: Q58652909
MaRDI profile type: Publication
OpenAlex ID: W2169133431
cites work: Modelling public transport trips by radial basis function neural networks
cites work: Radial Basis Functions
cites work: Radial basis function networks 2. New advances in design
cites work: Kernel Based Learning Methods: Regularization Networks and RBF Networks
cites work: Q4692508
cites work: Q4263623
description: scientific article; zbMATH DE number 5829220