| 张利,吴华玉,卢秀颖.基于粗糙集的改进BP神经网络算法研究[J].,2009,(6):971-976 |
| 基于粗糙集的改进BP神经网络算法研究 |
| An improved BP neural network algorithm based on rough set |
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| DOI：10.7511/dllgxb200906033 |
| 中文关键词: BP神经网络 粗糙集 遗传算法 属性约简 |
| 英文关键词: BP neural network rough set genetic algorithm attribute reduction |
| 基金项目:国家自然科学基金资助项目60573172. |
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| 摘要点击次数: 1130 |
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| 中文摘要: |
| 提出了一种基于粗糙集和遗传算法的改进BP神经网络算法．该算法首先对原始数据集进行属性约简，优化BP神经网络的输入变量；然后利用遗传算法全局搜索的特点，优化BP神经网络初始权重和阈值．将改进BP神经网络算法应用于客户分类，训练误差为5.92×10 12 ，测试总误差为0.000 23；而改进前的一个比较理想的训练结果的训练误差为0.001 6，测试总误差为0.073．Matlab仿真表明改进的BP神经网络算法有更好的训练精度和泛化能力． |
| 英文摘要: |
| An improved BP neural network algorithm is proposed based on rough set and genetic algorithm. Firstly, attribute reduction is carried out on original data sets, and the input variables of the BP neural network are optimized. Then, based on the global searching characteristic of genetic algorithm, the original weight and threshold of the BP neural network are optimized. Finally, the improved BP neural network is applied to the custom classification, the training error is 5.92×10 12 , and the total test error is 0.000 23, while an ideal training result′s training error before improvement is 0.001 6, and the total test error is 0.073. Through the simulation of Matlab, it indicates that the improved BP neural network algorithm has better training precision and extensive ability. |
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