In this paper, we use the generalized differentiability concept tostudy the fuzzy transport equation. We consider transport equationin the homogeneous and non-homogeneous cases with fuzzy initialcondition. We also present the solution when speed parameter is a fuzzynumber. Our method is based on the construction of the solutionsby employing Zadeh's extension principle.

Fuzzy set theory and fuzzy logic are the convenient tools for handling uncertain, imprecise, or unmodeled data in intelligent decision-making systems. The utility of fuzzy logic in system controls domain is presented in the context of a mobile robot navigation control application. The Takagi-Sugeno controller is a fuzzy model capable of approximating a wide class of nonlinear systems by decompo...

To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a novel method of designing combined classifier based on fuzzy neural network (FNN) is presented in this paper. The method employs fuzzy neural network classifiers and interclass distance (ICD) to improve recognition reliability. Experimental results show that the proposed combi...

This paper discusses the use of combined uncertainty methods in the computation of wavelets that best represent horse gait signals. Combined uncertainty computes a composite of two types of uncertainties, fuzzy and probabilistic. First, we introduce fuzzy uncertainty properties and classes. Next, the gait analysis problem is discussed in the context of correctly classifying wavelettransformed s...

the traditional fuzzy operators, such as t-norms, t-conorms and averaging operators have been used for soil suitability evaluation to aggregate criteria as an overall suitability index. however, such operators do not account for the degree of compensation common to human aggregation criteria, especially in the presence of conflicting criteria. fuzzy integrals are powerful and flexible aggregati...

A generalised fuzzy approach to statistical modelling techniques for speech recognition is proposed in this paper. Fuzzy C-means (FCM) and fuzzy entropy (FE) techniques are combined into a generalised fuzzy technique and applied to hidden Markov models (HMMs). A more robust version of the above fuzzy technique based on the noise clustering (NC) method is also proposed. Experimental results were...

The Fuzzy transform is applied mainly to 1-D signals and 2-D data organized as a regular grid (e.g., images), thus, limiting its potential application arbitrary in terms of dimensionality structure. This article defines analyzes the properties data-driven F-transform, with focus on construction class membership functions, which are multiscale, local, linearly independent, intrinsic, robust disc...

The application of fuzzy cluster analysis to larger data sets can cause runtime and memory overflow problems. While deterministic or hard clustering assigns a data object to a unique cluster, fuzzy clustering distributes the membership of a data object over different clusters. In standard fuzzy clustering, membership degrees will (almost) never become zero, so that all data objects are assigned...

This paper presents a novel method for combining multiple compensatory neural fuzzy networks (CNFN) using fuzzy integral. The fusion of multiple classifiers can overcome the limitations of a single classifier since the classifiers complement each other. A fuzzy integral is a better combination scheme than majority voting method that uses the subjectively defined relevance of classifiers. A comb...

The paper discusses the generalization capability of two hidden layer neural networks based on various fuzzy operators introduced earlier by the authors as Fuzzy Flip-Flop based Neural Networks (FNNs), in comparison with standard networks tansig function based, MATLAB Neural Network Toolbox in the frame of a simple function approximation problem. Various fuzzy neurons, one of them based on new ...