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An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s.
Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF ...
Since the fuzzy system output is a consensus of all of the inputs and all of the rules, fuzzy logic systems can be well behaved when input values are not available or are not trustworthy. Weightings can be optionally added to each rule in the rulebase and weightings can be used to regulate the degree to which a rule affects the output values.
Evolving fuzzy rule-based Classifier (eClass [2]) Evolving Takagi-Sugeno fuzzy systems (eTS [3]) Evolving All-Pairs (ensembled) classifiers (EFC-AP [4]) Evolving Connectionist Systems (ECOS) Dynamic Evolving Neuro-Fuzzy Inference Systems (DENFIS) Evolving Fuzzy Neural Networks (EFuNN) Evolving Self-Organising Maps. neuro-fuzzy techniques
A kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system. The technique was developed in the early 1990s. The technique was developed in the early 1990s. [ 7 ] [ 8 ] Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework .
In mathematics, fuzzy measure theory considers generalized measures in which the additive property is replaced by the weaker property of monotonicity. The central concept of fuzzy measure theory is the fuzzy measure (also capacity, see [1]), which was introduced by Choquet in 1953 and independently defined by Sugeno in 1974 in the context of fuzzy integrals.
In computer science, an evolving intelligent system is a fuzzy logic system which improves the own performance by evolving rules. [1] The technique is known from machine learning, in which external patterns are learned by an algorithm. Fuzzy logic based machine learning works with neuro-fuzzy systems. [2]
The structure of a fuzzy system is expressed by the input and output variables and the rule base, while the parameters of a fuzzy system are the rule parameters (defining the membership functions, the aggregation operator and the implication function) and the mapping parameters related to the mapping of a crisp set to a fuzzy set, and vice ...