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论文类型：期刊论文

发表时间：2010-06-01

发表刊物：Journal of Harbin Institute of Technology (New Series)

收录刊物：EI、Scopus

卷号：17

期号：3

页面范围：431-436

ISSN号：10059113

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.