吉林大学学报(工学版) ›› 2003, Vol. ›› Issue (4): 79-84.

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Fast convergent algorithm-BP for trainning neural network

WANG Yun-song1, XU Hong-guo2   

  1. 1. College of Traffic and Vehicle Engineering, Shandong University of Technology, Jinan 250014, China;
    2. College of Traffic, Jilin University, Changchun 130025, China
  • Received:2002-11-29

Abstract: A new kind of BP algorithm-LMBP that converges very fast is proposed in this paper by using Levenberg Marquardt optimization method and standard BP algorithm.The experimental results prove that LMBP converges very rapidly and has good stability property compared with that of the standard BP algorithm and other improved ones.LMBP algorithm is suitable for the case with high demands of on-line computation,e.g.on-line measurement.But when the size of neural network increases, it needs enormous calculation and large computer memory space.

Key words: neural network, LMBP algorithm, Levenberg-Marquardt optimization method

CLC Number: 

  • TP183
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