Caixia Li , Sreenatha Gopalarao Anavatti, Tapabrata Ray University of New South Wales @ Australian Defence Force Academy [email protected] Abstract-This paper focuses on the design and implementation of the analytical hierarchy process (AHP) using Fuzzy inference techniques. This AHP-FUZZY approach is a multi-criteria combination system. The nature of the AHP-FUZZY approach is pair-...

The fuzzy preference framework of Graph Model for Conflict Resolution (GMCR), handling both crisp and fuzzy preferences of decision makers (DMs), is capable of calculating and predicting different conflict resolutions (equilibria) according to DMs’ different satisficing criteria. Employing this methodology, a water contamination conflict occurs at the boundary of Jiangsu and Zhejiang provinces ...

Recent research has led to proposals for the integration of fuzzy based information and decision rules in business process models with use of concepts based on the fuzzy set theory. While the proposed Fuzzy EPCs provide an adequate method for conceptual representation of fuzzy business process models, the issue of exchanging and transforming such models, together with its enclosed executable co...

in this paper, a new structure possessing the advantages of low-power consumption, less hardware and high-speed is proposed for fuzzy controller. the maximum output delay for general fuzzy logic controllers (flc) is about 86 ns corresponding to 11.63 mflips (fuzzy logic inference per second) while this amount of the delay in the designed fuzzy controller becomes 52ns that corresponds to 19.23 m...

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracyinterpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning process and a rule selection process can be combined to obtain a set of solutions with different trade-offs between the accuracy and the compactness of models. Nevertheless, an initial model needs to be defined, i...

Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conven...

Defuzzification is the process of converting a fuzzy quantity to precise quantity, just as fuzzification is the conversion of a precise quantity to a fuzzy quantity. Various types of defuzzification methods, are available for conversion of fuzzy to non-fuzzy. In this paper, defuzzification functions in intuitionistic fuzzy environment such as triangular, trapezoidal, L-trapezoidal, R-trapezoida...