FUZZY BP: a neural network model with fuzzy inference
Hahn-Ming Lee, Bing-Hui Lu · 2002
In this paper a neural network model, named Fuzzy BP, with fuzzy inference is proposed. It performs nonlinear mapping between fuzzy input vectors and crisp outputs. Therefore, it has the ability of processing fuzzy numbers. The fuzzy numbers are represented in LR-type to reduce network complexity. Besides, the connection weights and biases are represented as fuzzy numbers to increase fuzzy inference ability. In addition, a fuzzy neuron which performs fuzzy weighted summation, defuzzification, and nonlinear mapping is proposed. Also, a simple defuzzification formula is presented. A sample problem, called Knowledge-Eased Evaluator, is considered to illustrate the working of the proposed model, and the experimental results are very encouraging.>