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[1]蒲兴成,林炎钦.基于显著性分析的神经网络混合修剪算法[J].智能系统学报,2014,9(6):690-697.[doi:10.3969/j.issn.1673-4785.201309062]
　PU Xingcheng,LIN Yanqin.Hybrid pruning algorithm for the neural network based on significance analysis[J].CAAI Transactions on Intelligent Systems,2014,9(6):690-697.[doi:10.3969/j.issn.1673-4785.201309062]

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基于显著性分析的神经网络混合修剪算法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 9 期数: 2014年第6期页码: 690-697 栏目: 学术论文—机器学习出版日期: 2014-12-25

Title:: Hybrid pruning algorithm for the neural network based on significance analysis

作者:: 蒲兴成^1,2, 林炎钦¹; 1. 重庆邮电大学计算机学院, 重庆 400065;
2. 重庆邮电大学数理学院, 重庆 400065

Author(s):: PU Xingcheng^1,2, LIN Yanqin¹; 1. Department of Computer Science, Chongqing University of Post & Telecommunications, Chongqing 400065, China;
2. Department of Mathematics & Physics, Chongqing University of Post & Telecommunications, Chongqing 400065, China

关键词:: 显著性分析; 神经网络; 合作型协同进化遗传算法; 修剪算法; 股票市场

Keywords:: significance analysis; neural network; cooperative co-evolutionary genetic algorithms; pruning algorithm; stock market

分类号:: TP24

DOI:: 10.3969/j.issn.1673-4785.201309062

文献标志码:: A

摘要:: 针对神经网络结构设计问题,提出了一种混合修剪算法。该算法首先利用合作型协同进化遗传算法和反向传播算法的不同优势,完成了对神经网络的结构和权值的初步调整;然后,在保证模型泛化能力的前提下,通过计算隐层神经元的显著性,借此修剪网络中显著性较小的神经元,进一步简化网络结构。最后,将给出的基于显著性分析的神经网络混合修剪算法用于股票市场的预测。仿真结果表明,该改进算法与其他优化算法相比,具有更好的泛化能力和更高的拟合精度。

Abstract:: This paper puts forward a kind of hybrid pruning algorithm for considering the problem of neural network structure design. Firstly, the algorithm uses the different advantages of cooperative co-evolutionary genetic algorithm and back propagation algorithm to optimize the structure and weights of neural networks. Secondly, by calculating the significance of the hidden layer neurons, it prunes the network that is not significant, further simplifying the structure of the network without reducing the generalization ability of the model. Finally, the proposed hybrid pruning algorithm is used to forecast the stock market. The simulations showed that the improved algorithm has better generalization ability and higher fitting precision than other optimization algorithms.

参考文献/References:: [1] YONG Mingjing, HUA Yingdong. Study on characteristic of fractional master-slave neural network[C]//Fifth International Symposium on Computational Intelligence and Design. Hangzhou, China, 2012: 498-501.
[2] 乔俊飞, 张颖. 一种多层前馈神经网络的快速修剪算法[J]. 智能系统学报, 2008, 3(2): 622-627. QIAO Junfei, ZHANG Ying. Fast unit pruning algorithm for feed forward neural network design [J]. CAAI Transactions on Intelligent Systems, 2008, 3(2): 622-627.
[3] MOOD Y J. Prediction risk and architecture selection for neural networks[C]//Statistics to Neural Networks: Theory and Pattern Recognition Applications, NATO ASI Series F. New York, 1994: 288-290.
[4] LIU Yong. Create stable neural networks by cross-validation[C]//International Joint Conference on IJCNN’06 Neural Networks. Vancouver, Canada, 2006: 3925-3928.
[5] DU Juan, ER Mengjoo. A fast pruning algorithm for an efficient adaptive fuzzy neural network[C]//Eighth IEEE International Conference on Control and Automation. Xiamen, China, 2010: 1030-1035.
[6] 乔俊飞, 韩红桂. 前馈神经网络分析与设计[M]. 北京:科学出版社, 2013: 107-158.
[7] EUGENE S, MARIA S. A self-configuring genetic programming algorithm with modified uniform crossover[C]//WCCI 2012 IEEE World Congress on Computational Intelligence. Brisbane, Australia, 2012: 1984-1990.
[8] 吴鹏. 基于语法引导的遗传编程在神经树中的应用[D]. 济南: 济南大学, 2007: 69-102. WU Peng. The application of genetic programming in the neural tree based on grammar guide[D]. Jinan: University of Jinan, 2007: 69-102.
[9] 周明, 孙树栋. 遗传算法原理及应用[M]. 北京:国防工业出版社, 1999: 1-26.
[10] 巩敦卫, 孙晓燕. 基于合作式协同进化算法的神经网络优化[J]. 中国矿业大学学报, 2006, 35(1): 114-119.GONG Dunwei, SUN Xiaoyan. Optimization of neural network based on cooperative co-evolutionary algorithm [J]. Journal of China University of Mining & Technology, 2006, 35(1): 114-119.
[11] POTTER M A. The design and analysis of a computational model of cooperative co-evolution [D]. Fairfax: George Mason University, 1997: 21-66.
[12] HETCHT N R. Theory of the back propagation neural network[C]//Proceedings of the International Joint Conference on Neural Networks. New York: IEEE Press, 1989: 593-611.
[13] BLUM E K, LI L K. Approximation theory and feed forward networks[J]. Neural Net, 1991,2(3): 511-515.

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备注/Memo

收稿日期:2013-9-19;改回日期:。
基金项目:国家自然科学基金资助项目(51075420);重庆市教委科学技术研究资助项目(113156,KJ1400432);科技部国际合作资助项目(2010DFA12160).
作者简介:蒲兴成,男,1973年生,副教授,博士,主要研究方向为非线性控制、随机系统和智能控制等.主持和参与省部级基金项目8项,发表学术论文40余篇,出版学术专著1部、教材1部;林炎钦,男,1983年生,硕士,主要研究方向为智能计算、智能信息处理及应用。
通讯作者:蒲兴成.E-mail:puxingcheng@sina.com.

更新日期/Last Update: 2015-06-16