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《Journal of Harbin Institute of Technology》 2001-04
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Improvement of BP network training rate

SUN Bai qing 1, PAN Qi shu 1, FENG Ying jun 1, ZHANG Chang sheng 2 (1.School of Management, Harbin Institute of Technology, Harbin 150001, China; 2.Dept of Mathematics, Harbin Institute of Technology, Harbin 150001,China)  
In order to address such issues as slow convergence speed and paralysis of network in application of BP network due to high dimension complexity of error function, normalization, number of hidden layer nodes, addition or subtraction of sample numbers, global learning rate, and network training algorithm are studied in this paper, and the results show that: Training sample data may not necessarily be normalized to and they can be changed by linear transform to a certain interval to achieve rational data distribution and satisfy different training needs; The number of hidden layer nodes is initialized to the experiential formula, and a little bigger network may be chosen to start the network training; The network must strengthen the memory of learned samples and pay more attention to cutting down the number of wrong samples; The thought of golden means is introduced to adjust the learning rate with satisfactory results; As a learning algorithm, single parameter dynamic searching algorithm can meet the demands of given precision as soon as possible.
【Fund】: 国家自然科学基金资助项目 ( 6 99740 13);; 黑龙江省科学技术计划项目 (k9912 )
【CateGory Index】: TP183
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