DocumentCode :
2251724
Title :
Neuro-fuzzy control based on on-line least square support vector machines
Author :
Han, Liu ; Yi, Deng ; Lifan, Zhang ; Ding, Liu
Author_Institution :
School of Automation and Information Engineering, Xi´an University of Technology, Xi´an 710048
fYear :
2015
fDate :
28-30 July 2015
Firstpage :
3411
Lastpage :
3416
Abstract :
A novel neuro-fuzzy control structure has been proposed to solve the nonlinear control of industrial system which is referred as the on-line Least Square Support Vector Machines (LSSVM) based on Adaptive Network Fuzzy Inference System (ANFIS) controller since it has been emerged from the ANFIS and SVM. In the proposed controller, an initial control vector is generated by fuzzy neural networks, which will be optimized by on-line LSSVM based controller. And then the optimized control vector will be applied to the controlled system which parameters of neuro-fuzzy network will also be tuned according to the optimized control vector. The simulation results have revealed that the on-line LSSVM based ANFIS controller exhibits considerably high performance by yielding very small transient and steady tracking errors.
Keywords :
Fuzzy neural networks; Nonlinear systems; Optimal control; Optimization; Support vector machines; Trajectory; Neuro-fuzzy controller network; On-line Least Square Support Vector Machine; On-line tuning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2015 34th Chinese
Conference_Location :
Hangzhou, China
Type :
conf
DOI :
10.1109/ChiCC.2015.7260166
Filename :
7260166
Link To Document :
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