Open AccessChaos control of ferroresonance system based on improved RBF neural network
Author(s) -
Si-ma Wen-xia,
Lijuan Fan,
Caixin Sun,
Ruijin Liao,
Qing Yang
Publication year - 2006
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.55.5714
Subject(s) - ferroresonance in electricity networks , artificial neural network , computer science , chaos (operating system) , cluster analysis , control theory (sociology) , electric power system , stability (learning theory) , chaotic , reliability (semiconductor) , convergence (economics) , artificial intelligence , control (management) , power (physics) , algorithm , machine learning , physics , computer security , quantum mechanics , economics , economic growth
Facing to the ferroresonance over voltage of neutral grounded power system, an improved learning algorithm based on RBF neural networks is used to control the chaos system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers.It improves the regression and learning ability of neural networks. The academic derivation testifies the errors and precision could satisfy demand of chaos control.And simulation calculation also displayed that the rate of convergence of the improved RBF neural network is much quickly and approach ability is much stronger. The numerical experimentation of ferroresonance system testifies the reliability and stability of using the algorithm to control chaos in neutral grounded power system.