General part quotations for injection mould often hold shortcomings such as low efficiency and poor accuracy. Aiming at this issue, a multiexpert quotation framework is proposed based on back propagation (BP) neural network (NN) in this paper. Weights and thresholds of the network are optimized by using generic algorithm which can avoid to fall into a local optimum solution. Meanwhile, an ideal topological structure of NN is decided by studying the Gaussian distribution of errors. A rotational quotation is thereby achieved after synthetical consideration of the evaluating results by synthesizing multiple experts. It is proved that the method greatly eliminates the randomness of a single quotation. Experiments exhibit a preferable performance, in which the average error of quotations is about 6.90%. This scheme reduces the difficulty of traditional quotation and enhances its robustness.
LIU Wei,YANG Chao
. A Study on Injection Mould Part Quotation Model
Based on Back Propagation Neural Network[J]. Journal of Shanghai Jiaotong University, 2017
, 51(10)
: 1207
-1213
.
DOI: 10.16183/j.cnki.jsjtu.2017.10.009
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