On fuzzy neuron models

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