DocumentCode :
526916
Title :
Support vector machine fuzzy self-learning control with self-adaptive chaotic optimal learning algorithm for induction machines
Author :
Shao, Zongkai
Author_Institution :
Sch. of Hydropower & Inf. Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
Volume :
1
fYear :
2010
fDate :
10-11 July 2010
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, because the induction machines (IM) are described as the plants of highly nonlinear and parameters time-varying, to obtain excellent control performances of IM and overcome the shortcomings of the fast modified variable metric optimal learning algorithm (MDFP) and back propagation (BP) learning algorithm of neural network, such as requiring derivation in the process of learning and system identification, using a self-adaptive chaotic optimal learning algorithm (SAC), a support vector machine fuzzy self-learning control strategy for IM is presented based on the rotor field oriented motion model of IM. The fuzzy self-learning controller incorporated into the support vector machine fuzzy inference system (SVM-FIS) and a support vector machine identifier (SVMI) for IM adjustable speed system are designed. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.
Keywords :
asynchronous machines; backpropagation; fuzzy control; machine control; self-adjusting systems; support vector machines; unsupervised learning; back propagation learning algorithm; fuzzy self-learning control; induction machines; rotor field oriented motion model; self-adaptive chaotic optimal learning algorithm; support vector machine; Artificial neural networks; Niobium; Petroleum; fuzzy inference system (FIS); induction machine (IM); motor dynamic model; self-adaptive chaotic optimal learning algorithm; support vector machine (SVM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial and Information Systems (IIS), 2010 2nd International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-7860-6
Type :
conf
DOI :
10.1109/INDUSIS.2010.5565929
Filename :
5565929
Link To Document :
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