A novel learning methodology based on a hybrid mechanism for training interval singleton type-2 Takagi-SugenoKang fuzzy logic systems uses recursive orthogonal least-squares to tune the type-1 consequent parameters and the steepest descent method to tune the interval type-2 antecedent parameters. The proposed hybrid-learning algorithm changes the interval type-2 model parameters adaptively to m...

The objective of this paper is to present an approach that utilizes an interval type-2 fuzzy decision making system (IT2FDMS) to quantify the Project Management Efficiency (PME). The algorithm developed in this paper is based upon interval type-2 fuzzy logic, giving it the ability to solve complex problems plagued with uncertainty and vagueness. A interval type-2 fuzzy decision making system is...

Supply chain management; Fuzzy clustering; Interval type-2 fuzzy hybrid system; Demand forecasting; Ordering policy; Bullwhip effect. Abstract The purpose of this paper is to evaluate and reduce the bullwhip effect in fuzzy environments by means of type-2 fuzzymethodology. In order to reduce the bullwhip effect in a supply chain, we propose a newmethod for demand forecasting. First, the demand ...

Constrained interval type-2 (CIT2) fuzzy sets have been introduced to preserve interpretability when moving from type-1 (IT2) membership functions. Although they can be used produce systems with enhanced explainability, so far, the latter comes at expense of high computational cost. Specifically, exhaustive type-reduction method for CIT2 Mamdani has shown too slow in practical applications and ...

The Interval Type-2 Fuzzy Logic Controller (IT2FLC) for a Quadruple Tank Process (QTP) is demonstrated in this paper. Here the Interval Type-2 based Fuzzy membership function is used. The QTP is made to operate in minimum phase mode. The vertices of fuzzy membership functions are tuned with IT2FLC to minimize Integral Absolute Error. Performance of IT2FLC and Type-1 Fuzzy Logic Controller (T1FL...

The fuzzy rough set is a fuzzy generalization of the classical rough set. In the traditional fuzzy rough model, the set to be approximated is a fuzzy set. This paper deals with an incomplete fuzzy information system with interval-valued decision by means of generalizing the rough approximation of a fuzzy set to the rough approximation of an interval-valued fuzzy set. Since all condition attribu...

The Fuzzy Analytic Network Process (ANP) is generally used for solving multicriteria decision making (MCDM) problems by considering the pairwise comparison between criteria/sub-criteria, and inner/outer dependencies among criteria. Linguistic expressions are used for experts’ judgements, and these judgements are imprecise and vague. Hence, incorporating fuzziness with multi-criteria decision ma...

With the emergence of the fundamental paper [5] by Zadeh in 1965 number of papers have appeared in literature featuring the application of fuzzy sets to pattern recognition, decision problems, function approximation, system theory, fuzzy logic, fuzzy algorithms, fuzzy automata, fuzzy grammars, fuzzy languages, fuzzy algebras, fuzzy topology, etc. [2], [7]. In this note, our interests are in the...

Controller design remains an elusive and challenging problem foruncertain nonlinear dynamics. Interval type-2 fuzzy logic systems (IT2FLS) incomparison with type-1 fuzzy logic systems claim to effectively handle systemuncertainties especially in the presence of disturbances and noises, but lack aformal mechanism to guarantee performance. In contrast, adaptive sliding modecontrol (ASMC) provides...