A Fuzzy Optimization Neural Network Model Using Second Order Information

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. A case study is utilized to validate this new model, and the results reveal that the new model can make the training speed faster and the forecasting capability better.