Synaptic and somatic learning and adaptation in fuzzy neural systems

Abstract

An attempt is made to establish some basic models for fuzzy neurons. Three types of fuzzy neural models are proposed. The neuron I is described by logical equations or if-then rules; its inputs are either fuzzy sets or crisp values. The neuron II, with numerical inputs, and the neuron III, with fuzzy inputs, are considered to be a simple extension of nonfuzzy neurons. A few methods of how these neurons change themselves during learning to improve their performance are also given. The notion of synaptic and somatic learning and adaptation is also introduced, which seems to be a powerful approach for developed a new class of fuzzy neural networks. Such an approach may have application in the processing of fuzzy information and the design of expert systems with learning and adaptation abilities.<>