Abstract
Improved back propagation (BP) neural network evaluation method for product schemes took the main index data as input vector, took the sample comprehensive scores as output by using the analytic hierarchy process (AHP). The network was separately trained by momentum factorial algorithm, Gauss–Newton algorithm and Levenberg-Marquardt algorithm. With the application and verification in Haier refrigerator schemes, the comparison of speed and mean absolute error show that the BP neural network trained by Levenberg-Marquardt algorithm is reliable.
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Chen, W., Wei, X., Zhao, T. (2008). Product Schemes Evaluation Method Based on Improved BP Neural Network. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_13
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DOI: https://doi.org/10.1007/978-3-540-85984-0_13
Publisher Name: Springer, Berlin, Heidelberg
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