A multi-criteria learning method for multi-layer fuzzy neural networks
Ping Xiao · Journal of Anhui University · 2007
In this paper,a new approach,the multicriteria learning(MCL) algorithm based on a composite criterion including both fuzzy entropy and mean-squared error criterion,is proposed for training multi layer max-min neural networks.The new algorithm overcomes the limitations in mono-criterion learning,that is,the mean-squared error criterion will easily converging to a local minimum value and slow converging speed.Compared with the traditional fuzzy back-propagation(FBP) algorithm,it is found that the proposed MCL algorithm provides a faster learning speed and higher stability.