基于PLS的Elman神经网络算法研究  

Elman Neural Network Algorithm Based on PLS

作　　者：丁世飞[1,2] 贾伟宽[1] 许新征[1] 苏春阳[1] 

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116 [2]中国科学院计算技术研究所智能信息处理重点实验室,北京100080

出　　处：《电子学报》2010年第B02期71-75,共5页Acta Electronica Sinica

基　　金：江苏省基础研究计划(自然科学基金)(No.NBK2009093);国家自然科学基金(No.60975039);中国科学院智能信息处理重点实验室开放基金项目(No.IIP2006-2)

摘　　要：针对特征变量多的小样本，结合偏最小二乘（Partial Least Squares,PLS）法则原理与Elman神经网络结构性质，提出基于PLS的Elman神经网络算法（PLSElman）.新算法通过PLS对高维小样本进行特征降维时，顾及了与因变量的相关程度，所得到的数据进行网络训练和仿真，明显的简化了网络结构，且可得较精确的网络模型.通过实例分析，结果表明新算法提高了网络的收敛速度、预测的精准率，证明新算法提高网络处理问题的效率.同时为便于验证新算法的有效性，与基于主成分分析（Principal Component Analys，PCA）的Elman神经网络算法（PCAElman）进行了比较，PLSElman算法有明显的优越性.As to small size samples which have many characteristic variables, when Partial Least Squares （PLS） principle and structural properties of Elman neural network are taken into account, PLS-Eiman is put forward. The new algorithm, when carry- ing feature reduction on high-dimensional and small size sample, takes its relativity to dependent variable into account. Obtained data carries on network training and simttlation, clearly simplifies network structure and can get more precise network models. According to case analysis, the result shows that new algorithm improves convergence rate of the network, the predicting precision and proves that new algorithm improves the efficiency of the dealing with problems of the network. In the meantime,in order to test the effec- tiveness of new algorithm, it is compared with Elman neural network algorithm based on Principal Component Analysis （PCA-E1- man） and it is observed that PLS-Elman algorithra has more advantages.

关 键 词：ELMAN神经网络 偏最小二乘法 PLS-Elman算法 主成分分析 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]