In this paper, a fuzzy inference system based on support vector machines 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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Liu, H., Wu, H., & Qian, F. (2006). Double inverted pendulum control based on support vector machines and fuzzy inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 1124–1130). Springer Verlag. https://doi.org/10.1007/11760023_165
Mendeley helps you to discover research relevant for your work.