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论文:2012,Vol:30,Issue(4):601-606 |
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引用本文： |
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李伟, 何鹏举, 杨恒, 陈明. 基于粗糙集和改进遗传算法优化BP神经网络的算法研究[J]. 西北工业大学 |
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Li Wei, He Pengju, Yang Heng, Chen Ming. An Effective Backpropagation Algorithm for Optimizing BP Neural Network Based on Rough Set and Modified Genetic Algorithm[J]. Northwestern polytechnical university |
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| 基于粗糙集和改进遗传算法优化BP神经网络的算法研究 |
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| 李伟, 何鹏举, 杨恒, 陈明 |
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1. 西北工业大学 自动化学院,陕西 西安 710072;
2. 无锡泛太科技有限公司,江苏 无锡 214000 |
| 摘要: |
| 针对BP神经网络结构由于特征维数增多变得复杂,以及网络易陷入局部极值点,提出了粗糙集和改进遗传算法结合共同优化神经网络的方法。首先利用粗糙集对样本空间进行属性约简,降低特征维数,进而简化BP神经网络的结构;然后训练过程中先用改进的遗传算法全局搜索网络的权值和阀值,再使用BP算法局部搜索细化,避免网络过早收敛。试验分析证明优化后BP神经网络比传统BP网络的预测精度得到了极大提高,泛化能力得到了增强,说明了该方法的可行性、有效性。 |
| 关键词:
BP神经网络
粗糙集
遗传算法
属性约简
局部极值
权值和阀值
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| An Effective Backpropagation Algorithm for Optimizing BP Neural Network Based on Rough Set and Modified Genetic Algorithm |
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| Li Wei, He Pengju, Yang Heng, Chen Ming |
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1. Department of Automatic Control, Northwestern Polytechnic University, Xi'an 710072, China;
2. Wuxi Fantai Technology Co. , Ltd. , Wuxi 214000, China |
| Abstract: |
| Considering that the BP neural network became complex due to the increase of the sample dimension andit fell easily into local maximums or minimums，we combined genetic algorithm and rough set to optimize the BPneural network. Sections 1 through 3 explain our backpropagation algorithm mentioned in the title，which we be-lieve is effective and whose core consists of: (1) rough set was applied to simplify the network by reducing the at-tribute dimension; (2) modified genetic algorithm was used to globally search the weights and bios and，further，the BP algorithm was to locally optimize them to avoid the network falling into the local extremes. Simulation re-sults，presented in Fig. 1 and Table 2 in subsection 3. 4，and their analysis indicated preliminarily that predictionaccuracy was increased greatly over that of the traditional BP neural network and that generalization was enhanced，thus showing that our backpropagation algorithm is indeed effective. |
| Key words:
backpropagation algorithms
decision making
efficiency
errors
genetic algorithms
mathematicalmodels
neural networks
optimization
rough set theory;reduction of attribute dimension
simula-tion
weights and bios
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| 收稿日期: 2011-10-22
修回日期:
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| DOI: |
| 基金项目: 陕西省科技攻关项目(2011K06-25)资助 |
| 通讯作者:
Email： |
| 作者简介: 李伟(1980-),女,西北工业大学博士研究生,主要从事多传感器数据融合与模式识别的研究。
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| 作者相关文章 |
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| 李伟 在本刊中的所有文章 |
| 何鹏举 在本刊中的所有文章 |
| 杨恒 在本刊中的所有文章 |
| 陈明 在本刊中的所有文章 |
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