Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Fuzzy logic, neural networks, and soft computing
Communications of the ACM
Fuzzy model identification: selected approaches
Fuzzy model identification: selected approaches
Fuzzy functions and their fundamental properties
Fuzzy Sets and Systems
Software Computing Medthods in Human Sciences
Software Computing Medthods in Human Sciences
Fuzzy functions with support vector machines
Information Sciences: an International Journal
Applied Soft Computing
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
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Fuzzy System Models (FSM), as one of the constituents of soft computing methods, are used for mining implicit or unknown knowledge by approximating systems using fuzzy set theory. The undeniable merit of FSM is its inherent ability of dealing with uncertain, imprecise, and incomplete data and still being able to make powerful inferences. This paper provides an overview of FSM techniques with an emphasis on new approaches on improving the prediction performances of system models. A short introduction to soft computing methods is provided and new improvements in FSMs, namely, Improved Fuzzy Functions (IFF) approaches is reviewed. IFF techniques are an alternate representation and reasoning schema to Fuzzy Rule Base (FRB) approaches. Advantages of the new improvements are discussed.