A multilayer feed forward small-world neural network controller and its application on electrohydraulic actuation system
type: Item
instance of: scholarly article
title: A multilayer feed forward small-world neural network controller and its application on electrohydraulic actuation system
zbMATH Open document ID: 1271.92004
DOI: 10.1155/2013/872790
author: Jin-Hua Zhang
author: Xiaohu Li
author: Sunan Wang
author: Feng Xu
published in: Journal of Applied Mathematics
publication date: 2013-09-09T00:00:00Z
review text: Summary: Being difficult to attain the precise mathematical models, traditional control methods such as proportional integral (PI) and proportional integral differentiation (PID) cannot meet the demands for real time and robustness when applied in some nonlinear systems. The neural network controller is a good replacement to overcome these shortcomings. However, the performance of neural network controller is directly determined by neural network model. In this paper, a new neural network model is constructed with a structure topology between the regular and random connection modes based on complex network, which simulates the brain neural network as far as possible, to design a better neural network controller. Then, a new controller is designed under small-world neural network model and is investigated in both linear and nonlinear systems control. The simulation results show that the new controller basing on small-world network model can improve the control precision by 30\% in the case of system with random disturbance. Besides the good performance of the new controller in tracking square wave signals, which is demonstrated by the experiment results of direct drive electro-hydraulic actuation position control system, it works well on anti-interference performance.
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 05C82
zbMATH DE Number: 6206876
MaRDI profile type: Publication
Wikidata QID: Q59004999
full work available at URL: https://doi.org/10.1155/2013/872790
OpenAlex ID: W1969566320
cites work: Statistical mechanics of complex networks
cites work: Stochastic Hopfield neural networks
cites work: Collective dynamics of ‘small-world’ networks
cites work: The power grid as a complex network: a survey
cites work: A complex network-based approach for boundary shape analysis
cites work: Fastest learning in small-world neural networks
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description: scientific article; zbMATH DE number 6206876