NDLI: Hybridisation of neural networks and a genetic algorithm for friction compensation

Content Provider	IEEE Xplore Digital Library
Author	Chaiyaratana, N. Zalzala, A.M.S.
Copyright Year	2000
Description	Author affiliation: Res. & Dev. Center for Intelligent Syst., King Mongkut's Inst. of Technol., Bangkok, Thailand (Chaiyaratana, N.)
Abstract	This paper presents the use of neural networks and a genetic algorithm within a model-based friction compensation scheme in a closed-loop robotic system. Unlike previous works which concentrate on using a single type of neural network to model friction function, three types of neural network, namely a multilayer perceptron, a radial-basis function network and a modular network, are used in the modelling task. The genetic algorithm is then used to find the optimal combination between the radial-basis function network and the multilayer perceptron and that between the radial-basis function network and the modular network. The simulation results indicate that the genetic algorithm has successfully found the combinations of the neural networks which results in a better compensation performance than using only one type of neural network. As a result of using both neural networks and a genetic algorithm in this application, an idea of a task hybridisation between neural networks and a genetic algorithm for use in a control system is also effectively demonstrated.
Starting Page	22
Ending Page	29
File Size	782334
Page Count	8
File Format	PDF
ISBN	0780363752
DOI	10.1109/CEC.2000.870271
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-07-16
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Genetic algorithms Friction Parameter estimation Mechanical systems Context modeling Intelligent robots Multi-layer neural network Multilayer perceptrons Research and development
Content Type	Text
Resource Type	Article

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