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|Title:||Automatic knowledge acquisition for multivariable fuzzy control using neural network approach||Authors:||Nie, Junhong
|Issue Date:||1993||Citation:||Nie, Junhong,Linkens, D.A. (1993). Automatic knowledge acquisition for multivariable fuzzy control using neural network approach. American Control Conference : 767-771. ScholarBank@NUS Repository.||Abstract:||This paper introduces a simple and systematic scheme capable of self-organizing and self-learning the required control knowledge for use with multivariable fuzzy controllers. The starting point of the approach is to structurally map a simplified fuzzy control algorithm (SFCA) into a counterpropagation network (CPN) in such a way that the control knowledge is explicitly represented in the form of connection weights of the nets, the control rule-base is gradually self-constructed with the fulfillment of the prespecified performance requirements, and finally the approximate reasoning is carried out by replacing a winner-take-all competitive scheme with a soft matching cooperative strategy. Two problems of multivariable control of blood pressure and anaesthesia have been studied as demonstration examples.||Source Title:||American Control Conference||URI:||http://scholarbank.nus.edu.sg/handle/10635/72501||ISBN:||0780308611|
|Appears in Collections:||Staff Publications|
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