Title :
On fuzzy neuron models
Author :
Gupta, M.M. ; Qi, J.
Author_Institution :
Intelligent Syst. Res. Lab., Saskatchewan Univ., Saskatoon, Sask., Canada
Abstract :
A contribution to the theoretical development of fuzzy neural network theory is presented. Three types of fuzzy neuron models are proposed. Neuron I is described by logical equations of `if-then´ rules; its inputs are either fuzzy sets or crisp values. Neuron II, with numerical inputs, and neuron III, with fuzzy inputs, are considered to be simple extensions of non-fuzzy neurons. A few methods of how these neurons change themselves during learning to improve their performance are also given. The application of the non-fuzzy neural network approach to fuzzy information processing is briefly discussed
Keywords :
fuzzy logic; fuzzy set theory; learning systems; neural nets; crisp values; fuzzy information processing; fuzzy inputs; fuzzy neuron models; fuzzy sets; if-then rules; learning; logical equations; neural network; numerical inputs; performance; Biological neural networks; Biology computing; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Humans; Information processing; Neurons; Power system modeling;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155371
