Ranking competing alternatives in terms of their overall performance with respect to some criterions in fuzzy environment is possible by the use of fuzzy TOPSIS methodology using interval-valued fuzzy-sets concepts. This author presents an effective fuzzy multi-criteria method based upon the fuzzy model and the concepts of positive ideal and negative ideal solution points for prioritizing alter...

Ranking competing alternatives in terms of their overall performance with respect to some criterions in fuzzy environment is possible by the use of fuzzy TOPSIS methodology using interval-valued fuzzy-sets concepts. This author presents an effective fuzzy multi-criteria method based upon the fuzzy model and the concepts of positive ideal and negative ideal solution points for prioritizing alter...

To improve the problem that the parameter identification for fuzzy neural network has many time complexities in calculating, an improved T-S fuzzy inference method and an parameter identification method for fuzzy neural network are proposed. It mainly includes three parts. First, improved fuzzy inference method based on production term for T-S Fuzzy model is explained. Then, compared with exist...

To improve the effectiveness of the fuzzy identification, a structure identification method based on moving rate is proposed for T-S fuzzy model. The proposed method is called " T-S modeling (or T-S fuzzy identification method) based on moving rate ". First, to improve the shortcomings of existing fuzzy reasoning methods based on matching degree, the moving rates for s-type, z-type and trapezoi...

abstract purpose of this paper: the objectives of this paper are two folds: (1) utilizing hierarchical fuzzy technique for order preference by similarity to ideal solution (topsis) approach to evaluate the most suitable rfid-based systems decision, and (2) to highlight key risks and benefits of radio frequency identification technology in healthcare industry. design/methodology/approach: resear...

introduced and applied to the problems of signal restoration. A fuzzy approach for system identification and state estimation is developed. The estimated values obtained from the fuzzy model are in the form of fuzzy numbers which present the possibility of the system structure. Both L-R and symmetric triangular type membership functions are employed to derive the fuzzy-data based estimator (FBE...

In fuzzy modelling, the identification method used is very important. Identification for fuzzy modelling has two aspects: structure identification and parameter identification. In general, structure identification is a difficult and extremely ill-defined process and not readily amenable to automated techniques. The problem of parameter identification is closely related to the estimation of the ...

The paper presents a short review how to use feedforward neural networks for non-linear system identification, with application at the neural implementation of a fuzzy system. In this application the inputoutput transfer characteristics of the fuzzy system are used to evaluate the accuracy of the identification results expressed for a neuro-fuzzy model. This method could be used for identificat...

It is known that neuro-fuzzy system is easily stuck in local minimum. To improve these drawbacks, a two-stage algorithm combining the advantages of neuro-fuzzy and genetic algorithms (GA) is integrated in system identification. Genetic algorithms are general purposed optimization algorithms with adaptive reproduction, crossover, and mutation operators that provide a method to search optimal par...