Abstract
In this paper, a fuzzy inference system based on support vector machi- nes is proposed for nonlinear system control. Support vector machines provides a mechanism to extract support vectors for generating fuzzy if-then rules from the training data set, and a method to describe the fuzzy inference system in terms of kernel functions. Thus it has the inherent advantages that the model doesn’t have to decide the number of fuzzy rules in advance, and has universal approximation ability and good generalization ability. The simulation results for stabilizing control of double inverted pendulum system are provided to show the validity and applicability of the proposed method.
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