Double inverted pendulum control based on support vector machines and fuzzy inference

Authors:
Han Liu;Haiyan Wu;Fucai Qian
Affiliations:
School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China;School of Automation and Information Engineering, Xi’an University of Technology, Xi’an, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 5
Cited 2

An ART-based fuzzy adaptive learning control network

IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning

IEEE Transactions on Fuzzy Systems
An overview of statistical learning theory

IEEE Transactions on Neural Networks
Support vector machines for spam categorization

IEEE Transactions on Neural Networks
Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks

Adaptive fuzzy switched swing-up and sliding control for the double-pendulum-and-cart system

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Support vector machines based neuro-fuzzy control of nonlinear systems

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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.