Fuzzy optimization neural network model based on LM algorithm  

作　　者：彭勇 周惠成 

机构地区：[1]School of Civil and Hydraulic Engineering,Dalian University of Technology

出　　处：《Journal of Harbin Institute of Technology(New Series)》2010年第3期431-436,共6页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China (Grant No. 50579095);Ertan Hydropower Development Company, LTD.

摘　　要：A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability.A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt （LM） algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model, the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training, which changes the weights adjusting equations of the network and increases the training speed. Moreover, to avoid the results yielding to local minimum, the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model, and the results reveal that the new model fast training speed and better forecasting capability.

关 键 词：fuzzy optimization neural network Levenberg-Marquardt algorithm transfer function 

分 类 号：TV212.4[水利工程—水文学及水资源]