Abstract
Through the analysis of function approximation with wavelet transformation, an adaptive wavelet neural network is introduced in the paper, which is applied in data compression of fault data in power system. In addition, the wavelet entropy is adopted to choose the hidden nodes in the wavelet neural network. The learning algorithm of the wavelet neural network based on wavelet entropy is proposed and discussed for data compression of fault data in power system. The simulation results show that it is feasible and valid in the end. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Liu, Z., & Zhang, D. (2006). Fault data compression of power system with wavelet neural network based on wavelet entropy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 1402–1408). Springer Verlag. https://doi.org/10.1007/11760023_202
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