FUZZY BP: a neural network model with fuzzy inference

Author

Lee, Hahn-Ming ; Lu, Bing-Hui

Author_Institution

Dept. of Electron. Eng., Nat. Taiwan Inst. of Technol., Taipei, Taiwan

Volume

3

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

1583

Abstract

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

Keywords

fuzzy neural nets; fuzzy set theory; inference mechanisms; knowledge based systems; learning (artificial intelligence); uncertainty handling; FUZZY BP; Knowledge-Eased Evaluator; connection weights; defuzzification; fuzzy inference; fuzzy input vectors; fuzzy neuron; fuzzy number processing; fuzzy weighted summation; network complexity; neural network model; nonlinear mapping; Artificial neural networks; Control systems; Data mining; Electronic mail; Expert systems; Fuzzy neural networks; Knowledge acquisition; Neural networks; Neurons; Nonlinear distortion;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374392

Filename

374392