NDLI: A new self-learning algorithm of neural networks and its application

Content Provider	IEEE Xplore Digital Library
Author	Hua-jun Zhang Jin Zhao
Copyright Year	2008
Description	Author affiliation: Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan (Hua-jun Zhang; Jin Zhao)
Abstract	In order to reduce the time of neural network self-learning, it proposes an algorithm which combines genetic algorithm (GA) and neural network prediction together. Genetic algorithm is used to search the optimal solution globally, and the data generated by GA during evolutionary process are used to train a predictive network. The predictive network establishes a mapping between parameters of operant networks and system response (fitness). When the genetic algorithm converges to adjacent domains of optimal solution, the predictive network guides it to converge to the optimum solution depending on its approximation for arbitrary function. The predictive network solves local vibration problem of GA and accelerates the speed of self-learning. The proposed algorithm is applied to design a speed controller of asynchronous motor drive system and a lot of simulation results validate the feasibility of the proposed algorithm.
Starting Page	1
Ending Page	6
File Size	123855
Page Count	6
File Format	PDF
ISBN	9781424417056
DOI	10.1109/ICIT.2008.4608576
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurocontrollers Gallium Prediction algorithms Artificial neural networks Algorithm design and analysis Genetic algorithms Evolution (biology) Vector control Self-Learning Genetic algorithm Neural networks prediction
Content Type	Text
Resource Type	Article

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