DocumentCode :
2342749
Title :
Application of LSSVM with AGA optimizing parameters to nonlinear modeling of SRM
Author :
Shang, Wanfeng ; Zhao, Shengdun ; Shen, Yajing
Author_Institution :
Dept. of Mechatron. Eng., Xi´´an Jiaotong Univ., Xi´´an
fYear :
2008
fDate :
3-5 June 2008
Firstpage :
775
Lastpage :
780
Abstract :
Considering nonlinear magnetization characteristics of a switched reluctance motor (SRM), this paper presents a nonlinear model of SRM based on the integration of least square support vector machine (LSSVM) and adaptive genetic algorithm (AGA), known as LSSVM-AGA. The real-valued AGA is applied to optimize the parameters of LSSVM, and then the LSSVM using the optimal parameters forms a very efficient mapping structure for the nonlinear SRM. The hybrid method for modeling SRM is tested through sufficient sample data to verify its validation and feasibility. The sample data comprise flux linkage, current and rotor position, which obtained from the experimental SRM by the dc-excitation method. The forecasted data of the SRM model with LSSVM-AGA are compared with measured data, and error analyses are given to determine performances of the model. The experimental results demonstrate that LSSVM optimized by AGA performs better forecast accuracy and successful modeling of SRM.
Keywords :
error analysis; genetic algorithms; least mean squares methods; magnetisation; reluctance motors; rotors; support vector machines; adaptive genetic algorithm; dc-excitation method; error analyses; flux linkage; least square support vector machine; nonlinear magnetization; rotor position; switched reluctance motor; Couplings; Genetic algorithms; Least squares methods; Magnetic switching; Magnetization; Predictive models; Reluctance machines; Reluctance motors; Support vector machines; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications, 2008. ICIEA 2008. 3rd IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1717-9
Electronic_ISBN :
978-1-4244-1718-6
Type :
conf
DOI :
10.1109/ICIEA.2008.4582620
Filename :
4582620
Link To Document :
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