Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images using DCT we used two popular soft computing techn...

Aggregation of fuzzy relations in multicriteria decision making problems is considered. Pair wise comparisons between the alternatives by several criteria are given as fuzzy relations with definite properties. The weighted coefficients of the criteria are fuzzy numbers. The aim is to suggest models for aggregation of the fuzzy relations taking into account the criteria importance. The propertie...

Fuzzy sets and rough sets are known as uncertainty models. They are proposed to treat different aspects of uncertainty. Therefore, it is natural to combine them to build more powerful mathematical tools for treating problems under uncertainty. In this chapter, we describe the state of the art in the combinations of fuzzy and rough sets dividing into three parts. In the first part, we first desc...

In this paper we present a novel fuzzy technique for Arabic (Indian) online digits recognition. We use directional features to automatically build generic fuzzy models for Arabic online digits using the training data. The fuzzy models include the samples’ trend lines, the upper and lower envelops of the samples of each digit. Automatically generated weights for the different segments of the dig...

in previous studies we first concentrated on utilizing crisp simulationto produce discrete event fuzzy systems simulations. then we extendedthis research to the simulation of continuous fuzzy systems models. in this paperwe continue our study of continuous fuzzy systems using crisp continuoussimulation. consider a crisp continuous system whose evolution depends ondifferential equations. such a ...

Reasoning with fuzzy rule-based models has been widely applied to perform various real world classification tasks. The main advantage of this approach is that it supports inferences in the way people think and make judgements. However, in order to gain high classification accuracy, transparency and interpretability of such models has often been ignored. To counter against this limitation, this ...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...