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《Pattern Recognition and Artificial Intelligence》 2002-01
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Li Jinping, He Miao (Collage of Information and Control Engineering, Jinan University, Jinan 250022) Liu Mingjun (Center for Con pitting, Jinan University, Jinan 250022) Yang Bo (Collage of Information and Control Engineering, Jinan University, Jinan 250022)  
Mathematical foundation of using wavelet neural networks in lossy compression and feature extraction of one-dimension data is discussed, then improvements of the convergence speed of BP wavelet neural networks are studied. The following new measures are taken: 1)Optimization of initial network parameters and signal scaling; 2) Improving the ordinary adaptive adjustment method; 3) Repeated approximation. In order to obtain higher compression rate, the variable-structure wavelet neural networks, which can delete the neurons of trivial contribution is discussed under the constraint of keeping the relative error less than a given value. Simulation results indicate that the improvements bring higher compression rate and faster convergence speed while the relative error is satisfied.
【Fund】: 国家自然科学基金(69902005);; 山东省自然科学基金青年基金(Q98G02151)
【CateGory Index】: TP183
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