A constrained optimization method based on BP neural network

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.

[1]  Yixian Yang,et al.  Bounds on the number of hidden neurons in three-layer binary neural networks , 2003, Neural Networks.

[2]  Sun Yan,et al.  Optimization for railway freight transport network based on BP Neural Network , 2013, Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC).

[3]  Zhong Xin,et al.  A novel 3-layer mixed cultural evolutionary optimization framework for optimal operation of syngas production in a Texaco coal-water slurry gasifier , 2015 .

[4]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[5]  Pan Zhang,et al.  Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact , 2015 .

[6]  Fulin Wang,et al.  An Unconstrainted Optimization Method Based on BP Neural Network , 2010, 2010 International Conference on E-Product E-Service and E-Entertainment.

[7]  Anthony T. C. Goh,et al.  A hybrid Bayesian back‐propagation neural network approach to multivariate modelling , 2003 .

[8]  Ali Mirsepahi,et al.  A comparative approach of inverse modelling applied to an irradiative batch dryer employing several artificial neural networks , 2014 .

[9]  Jie Hu,et al.  Research of new strategies for improving CBR system , 2012, Artificial Intelligence Review.

[10]  Zhi-Hua Zhou,et al.  Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization , 2006, IEEE Transactions on Knowledge and Data Engineering.

[11]  Fu Duan,et al.  A new method for image segmentation based on BP neural network and gravitational search algorithm enhanced by cat chaotic mapping , 2015, Applied Intelligence.

[12]  Yudong Zhang,et al.  WEIGHTS OPTIMIZATION OF NEURAL NETWORK VIA IMPROVED BCO APPROACH , 2008 .

[13]  Shifei Ding,et al.  An optimizing BP neural network algorithm based on genetic algorithm , 2011, Artificial Intelligence Review.

[14]  Kurt Hornik,et al.  Neural Network Models , 2011 .

[15]  武藤 佳恭.  Neural network parallel computing , 1992 .

[16]  Ting Jiang,et al.  A new sense-through-foliage target recognition method based on hybrid differential evolution and self-adaptive particle swarm optimization-based support vector machine , 2015, Neurocomputing.

[17]  Yuval Shahar,et al.  Distributed, intelligent, interactive visualization and exploration of time-oriented clinical data and their abstractions , 2006, Artif. Intell. Medicine.

[18]  Dean Zhao,et al.  An optimized classification algorithm by BP neural network based on PLS and HCA , 2014, Applied Intelligence.

[19]  Xun Liang,et al.  A unified mathematical form for removing neurons based on orthogonal projection and crosswise propagation , 2010, Neural Computing and Applications.

[20]  Li Zhang,et al.  Forecasting box office revenue of movies with BP neural network , 2009, Expert Syst. Appl..

[21]  Tuan Anh Ngo,et al.  Models of adaptive control system design for nonlinear dynamic plants based on a neural network , 2015, Autom. Remote. Control..

[22]  Jixin Qian,et al.  Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network , 2007 .

[23]  Terrence L. Fine,et al.  Feedforward Neural Network Methodology , 1999, Information Science and Statistics.

[24]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[25]  Chitra Dadkhah,et al.  Automatic fire detection based on soft computing techniques: review from 2000 to 2010 , 2012, Artificial Intelligence Review.

[26]  Jun Liu,et al.  The shift system of automated mechanical transmission based on neural network control , 2014, Proceeding of the 11th World Congress on Intelligent Control and Automation.