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| 基于改进BP神经网络的柘林湾水质综合评价模型 | |
| 引用本文： | 林小苹,黄长江,杜虹,陈旭明.基于改进BP神经网络的柘林湾水质综合评价模型[J].数学的实践与认识,2004,34(11):76-83. |
| 作者姓名： | 林小苹 黄长江 杜虹 陈旭明 |
| 作者单位： | 1. 汕头大学数学系,广东,汕头,515063 2. 汕头大学水生生物技术与环境资源保护研究所,广东,汕头,515063 3. 汕头市海洋与渔业局,广东,汕头,515041 |
| 基金项目： | 广东省重大科技兴海项目 (A2 0 0 0 0 5 F0 2 )，广东省自然科学基金项目 (0 2 1 2 60 ) |
| 摘 要： | 建立了基于改进 BP神经网络的柘林湾水质综合评价模型 .实验结果表明 ,新模型的网络训练收敛速度比未改进的模型快、误差更小 ,而且能克服 BP网络所存在的“过拟合”现象 .因此 ,它的泛化能力强 ,结果客观、合理 . |
| 关 键 词： | 水质综合评价 BP神经网络 LM算法 过拟合 泛化能力 |
| 修稿时间： | 2003年12月11 |
Comprehensive Assessment Model of Seawater Quality in Zhelin Bay Based on Improved BP Neural Network |
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| LIN Xiao-ping,HUANG Chang-jiang,DU Hong,CHEN Xu-ming.Comprehensive Assessment Model of Seawater Quality in Zhelin Bay Based on Improved BP Neural Network[J].Mathematics in Practice and Theory,2004,34(11):76-83. | |
| Authors: | LIN Xiao-ping HUANG Chang-jiang DU Hong CHEN Xu-ming |
| Institution: | LIN Xiao-ping1,HUANG Chang-jiang2,DU Hong2,CHEN Xu-ming3 |
| Abstract: | A comprehensive model based on developed BP neural network for assessing seawater quality in Zhelin Bay has been established. The experimental results have shown that the new model converges faster and the error is less than original BP network. Moreover, the new model can avoid being overfitted during the network training, so it possesses the capacity of higher generalization than the original model, and its assessed results are objective and reliable also. |
| Keywords: | comprehensive assessment of seawater quality BP neural network LM algorithm overfitting generalization |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |