FuNN/2 - A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition

Fuzzy neural networks have several features that make them well suited to a wide range þÿ o l knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the fonn of semanticallymeaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. This paper investigates adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning for a particular model ol’ a fuzzy neural network called FUNN _ As well as providing for representing a fuzzy system with an adaptable neural architecture, FLINN also incorporates a genetic algorithm in one of its adaptation modes. A version of FuNN ~ FuNN/2, which employs triangular membershipfunctions and correspondingly modified learning and adaptation algorithms, is also presented in the paper,