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Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS).
In addition, Hybrid algorithm (coupling of back propagation and least square methods) is used to determine parameters of membership function in the ANFIS approach.
CFS is an advanced fuzzy set, which is described by a complex-valued membership function in the unit disc of the complex plane.
The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise.
And on this basis, combined with the realization mechanism of membership function in fuzzy set theory, the consensus distance and the consensus matrix are further designed on the basis of confidence distance.
Secondly, combined with the realization mechanism of membership function in fuzzy set theory, the confidence distance and the support degree matrix are designed to evaluate the support degree among multi-sensor observations.
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The Sugeno-type fuzzy rules were constituted with various membership functions in the representations.
Finally, the performance of S-curve membership functions that represent uncertainty goals and constraints in TPD problems with linear membership functions in an industrial case is compared.
The work presented here demonstrates that tuning only the input membership function is sufficient and simpler than tuning both input and output membership functions in the standard FLC.
Ridge-type nonlinear membership functions in fuzzy set theory are applied to embody fuzzy and uncertain characteristics essentially involved by the objective and constraint functions.
Hybrid genetic algorithm was used for optimizing the parameters that are located at the Gaussian membership functions in the premise and consequent of each rule.
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