A neural network model based on fuzzy classification concept
Cheng-I Kao, Yau Hwang Kuo · 2003
A fuzzy-based neural network (FBNN) model, which applies a one-pass algorithm is proposed. The theory of the FBNN model originates from embedding a fuzzy classification concept into a parallel neural network architecture. Conventional neural networks, such as propagation using energy functions as learning principles, suffer from two major drawbacks, that of the local minimum problem and long training time. FBNN has the advantage of fast training, and avoids the local minimum problem. Experiments and comparisons between FBNN and some other neural network models are given. According to these results, FBNN shows stronger reliability on classification with respect to a probabilistic neural network, backpropagation, and a linear matching method.>