This papers aims at clarifying the meaning of diierent interpretations of the Max-Min or, more generally, the Max-t-norm rule in fuzzy systems. It turns out that basically two distinct approaches play an important role in fuzzy logic and its applications: fuzzy interpolation on the basis of an imprecisely known function and logical inference in the presence of fuzzy information .

Design of a fuzzy rule based classifier is proposed. The performance of the classifier for multispectral satellite image classification is improved using DempsterShafer theory of evidence that exploits information of the neighboring pixels. The classifiers are tested rigorously with two known images and their performance are found to be better than the results available in the literature. We al...

This paper investigates some properties of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. First, we prove that there exists a unique solution of the T-S fuzzy Hopfield neural network. Second, we determine a condition for input-to-state stability (ISS) of the T-S fuzzy Hopfield neural network. These results will be useful to analyze dynamic behavior of fuzzy neural networks.

This paper proposes a novel approach for identification of Takagi–Sugeno (T–S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM) clustering algorithm. The clustering prototype in fuzzy space partition is hyper-plane, so FCRM clustering technique is more suitable to be applied in premise parameters identification of T–S fuzzy model. A new FCRM clustering algorithm (NFCRMA) is ...

Using Generalized Modus Ponens reasoning, we examine the values of the inferred conclusion by using Fodor's implication in order to interpret a fuzzy if-then rule with a single input single output and the tnorm ( ) ( )( ) ( xy y x max y , x t ) λ λ − − + + = 1 1 , 1 − ≥ λ , for composition operation. This t-norm is important to use because for 1 − = λ and 0 = λ it gives the commonly used t-norm...

An increasing number of applications require the integration of data from various disciplines, which leads to problems with the fusion of multi-source information. In this paper, a special information structure formalized in terms of three indices (the central presentation, population or scale, and density function) is proposed. Single and mixed Gaussian models are used for single source inform...

Fuzzy classification rules are widely considered a well-suited representation of classification knowledge, as they allow readable and interpretable rule bases. The goal of this paper is to discuss the shapes of the resulting classification borders under consideration of different types of fuzzy sets, rule bases and t-norms and thus which class distributions can be represented by such classifica...

A comparison is made of two approaches to approximate reasoning: Mamdani's interpolation method and the implication method. Both approaches are variants of Zadeh's compositional rule of inference. It is shown that the approaches are not equivalent. A correspondence between the approaches is established via the inverse of the implied fuzzy relation. The interpolation method has the lowest time-c...

This paper presents a practical approach of nonlinear control of the F16 aircraft via multiple nonlinear model generation for any trimmed equilibriums. The approach is realized using the framework of a Takagi-Sugeno (T-S) fuzzy modeling and control. The nonlinear control algorithm consists of three parts. The first part determines a trimmed equilibrium of the F16 dynamics for a given desired fl...

A Neural integrated Fuzzy conTroller (NiF-T), which integrates the fuzzy logic representation of human knowledge with the learning capability of neural networks, is developed for nonlinear dynamic control problems. The NiF-T architecture comprises three distinct parts: (1) Fuzzy logic Membership Functions (FMF), (2) Rule Neural Network (RNN), and (3) Output-Re nement Neural Network (ORNN). FMF ...