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A constrained optimization method based on BP neural network

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Abstract

A constrained optimization method based on back-propagation (BP) neural network is proposed in this paper. Taking the maximization of output for example, using unipolar sigmoid function as transfer function, the method presents a general mathematical expression of BP neural network constrained optimization and derives the partial derivative of output with respect to input. On this basis, the fundamental idea, algorithms and related models are given in this article. When BP neural network is on the basis of fitting, this method can adjust the input values of BP neural network to make the output values maximal or minimal. Therefore, with this method the application of BP neural network is expanded by combining BP network’s fitting with optimization. At the same time, the article also provides a new method to study the black-box problem. The experiments show that the constrained optimization method is effective.

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Acknowledgments

This research is supported by National Social Science Foundation of China (Nos: 13BJY098), Project in the National Science & Technology Pillar Program during the Twelfth Five-year Plan Period of China (Nos: 2014BAD06B04-2-9) and Agriculture Industry Research Special Funds for Public Welfare Projects of China.

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Authors and Affiliations

  1. College of Engineering, Northeast Agricultural University, Harbin, 150030, China

    Li Zhang, Fulin Wang, Ting Sun & Bing Xu

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  1. Li Zhang
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  2. Fulin Wang
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  3. Ting Sun
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  4. Bing Xu
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Correspondence to Li Zhang.

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Zhang, L., Wang, F., Sun, T. et al. A constrained optimization method based on BP neural network. Neural Comput & Applic 29, 413–421 (2018). https://doi.org/10.1007/s00521-016-2455-9

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