[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(06):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(06):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
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年06期
页码:
690-697
栏目:
出版日期:
2014-12-25

文章信息/Info

Title:
Hybrid pruning algorithm for the neural network based on significance analysis
作者:
蒲兴成12 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 Xingcheng12 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 analysisneural networkcooperative co-evolutionary genetic algorithmspruning algorithmstock 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:

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备注/Memo

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