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Type-2 fuzzy sets and systems generalize standard Type-1 fuzzy sets and systems so that more uncertainty can be handled. From the beginning of fuzzy sets, criticism was made about the fact that the membership function of a type-1 fuzzy set has no uncertainty associated with it, something that seems to contradict the word fuzzy, since that word has the connotation of much uncertainty.
[1] [2] Since it integrates both neural networks and fuzzy logic principles, it has potential to capture the benefits of both in a single framework. Its inference system corresponds to a set of fuzzy IF–THEN rules that have learning capability to approximate nonlinear functions. [3] Hence, ANFIS is considered to be a universal estimator. [4]
A fuzzy control system is a control system based on fuzzy logic – a mathematical system that analyzes analog input values in terms of logical variables that take on continuous values between 0 and 1, in contrast to classical or digital logic, which operates on discrete values of either 1 or 0 (true or false, respectively).
At the other extreme, a system with perfect information has a unique solution. In the middle, grey systems will give a variety of available solutions. Grey relational analysis does not attempt to find the best solution, but does provide techniques for determining a good solution, an appropriate solution for real-world problems.
A neuro-fuzzy network is a fuzzy inference system in the body of an artificial neural network. Depending on the FIS type, several layers simulate the processes involved in a fuzzy inference-like fuzzification, inference, aggregation and defuzzification. Embedding an FIS in a general structure of an ANN has the benefit of using available ANN ...
After that he joined its Electrical Engineering Department [2] In 1975 he introduced a new method of fuzzy inference systems , which was called 'Mamdani-Type Fuzzy Inference'. [ 3 ] Mamdani-Type Fuzzy Inference have elements like human instincts, working under the rules of linguistics , and has a fuzzy algorithm that provides an approximation ...
Evolutionary tuning and learning of fuzzy knowledge bases, Advances in Fuzzy Systems: Applications and Theory, World Scientific. 1997, H. Ishibuchi, T. Murata, IB. Türkşen, Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems, Fuzzy Sets and Systems, V. 89, N. 2, pp. 135–150
Neuro-fuzzy hybridization is widely termed as fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. 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.