This paper proposs a systematic methodology of fuzzy logic modeling as a generic tool for modeling of complex systems. The methodology conveys three distinct features: 1) a unified parameterized reasoning formulation; 2) an improved fuzzy clustering algorithm; and 3) an efficient strategy of selecting significant system inputs and their membership functions. The reasoning mechanism introduces f...

Coevolutionary algorithms have received increased attention in the past few years within the domain of evolutionary computation. In this paper we combine the search power of coevolutionary computation with the expressive power of fuzzy systems, presenting Fuzzy CoCo: Fuzzy Cooperative Coevolution. We demonstrate the efficacy of Fuzzy CoCo by applying it to two hard problems—plant classification...

We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similari...

One of the foci of the recent development in object-oriented modeling (OOM) has been the extension of OOM to fuzzy logic to capture and analyze informal requirements that are imprecise in nature. In this paper, a new approach to object-oriented modeling based on fuzzy logic is proposed to formulate imprecise requirements along four dimensions: (1) to extend a class by grouping objects with simi...

Fuzzy control and modeling use only a small portion of the fuzzy mathematics that is available; this portion is also mathematically quite simple and conceptually easy to understand. In this chapter, we introduce some essential concepts, terminology, notations, and arithmetic of fuzzy sets and fuzzy logic. We include only a minimum though adequate amount of fuzzy mathematics necessary for unders...

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...

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 ...

In this paper, a fuzzy modeling method using genetic algorithms (GAs) with a conciseness measure is presented. This paper introduces De Luca and Termini's fuzzy entropy to evaluate the shape of a membership function, and proposes another measure to evaluate the deviation of a membership function from symmetry. A combined measure is then derived from these two measures, and a new conciseness mea...

This paper proposes a fuzzy clustering algorithm for fuzzy modeling. The algorithm is based on the assumption that, with an input fully matching with the premise part of a specific fuzzy rule, the corresponding output should completely participate in the consequent part. In order to accomplish this, certain conditions are satisfied. The application of the algorithm to two test cases, which have...