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《Proceedings of Electric Power System and Automation》 2005-06
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Investigation on the Convergence Performance of Continuous Wavelet Neural Network

BI Yan-qiu~1,ZHOU Wen-lian~2,ZHANG Da-hai~1,JIANG Shi-fang~1(1.School of Electrical Engineering,Shandong University,Jinan 250061,China;2.Shangdong Yantai Switchgear Factory,Yantai 264001,China)  
WNN is used in power systems due to its many advantages,but little is known about its shortcomings.This paper introduces the principle of WNN,together with the characteristics of discrete WNN and continuous WNN,and then investigates the convergence performance of continuous WNN.The analysis reveals that because of the great differences between the wavelet function and sigmoid function,the continuous WNN shows poor convergence performance when the initialization and training algorithm of BP neural network are used.Experiment is performed to validate the analysis above.Finally some suggestions to improve the convergence performance of continuous WNN are given,and the necessity to explore suitable algorithms for continuous WNN is emphasized.
【CateGory Index】: TM711
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