This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the ...

this paper introduces a technique for controlling a class of uncertain chaotic systems using an adaptive fuzzy proportional-integrator-derivative (pid) controller with h∞ tracking performance. the purpose of this work is to achieve optimal tracking performance of the controller using backtracking search algorithm (bsa). bsa, which is a novel heuristic algorithm, has an easy structure with singl...

Fuzzy theory appears to be extremely effective at handling dynamic, non-linear and noisy data, especially when the underlying physical relationships are not fully understood. Since hydrologists are still uncertain about many of the aspects of the physical processes in the watershed, fuzzy theory has proved to be a very attractive tool enabling them to investigate such problems. The effectivenes...

the jamor purpose of the present research is to predict the total stock market index of tehran stock exchange, using a combined method of wavelet transforms, fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.to do so, first the prediction was made by neural network, then a series of price index was decomposed by w...

Fuzzy co-clustering is a method that performs simultaneous fuzzy clustering of objects and features. In this paper, we introduce a new fuzzy coclustering algorithm for high-dimensional datasets called Cosine-Distancebased & Dual-partitioning Fuzzy Co-clustering (CODIALING FCC). Unlike many existing fuzzy co-clustering algorithms, CODIALING FCC is a dualpartitioning algorithm. It clusters the fe...

This paper presents a variation of fuzzy c-means (FCM) algorithm that provides data clustering. The proposed algorithm incorporates the local spatial information in a novel fuzzy way. The new algorithm is called Weighted Fuzzy Local Information C-Means (WFLICM). WFLICM can overcome the disadvantages of the known fuzzy Cmeans algorithm and at the same time enhances the clustering performance. Th...

Financial health of many organizations now-a-days is being affected by investment in software and their cost estimation. Therefore, to provide effective cost estimation models are the most complex activity in software engineering fields. This paper presents a fuzzy clustering and optimization model for software cost estimation. The proposed model uses Pearson product-moment correlation coeffici...

In this paper, we propose a Context-based Gustafson-Kessel (CGK) clustering that builds Information Granulation (IG) in the form of fuzzy set. The fundamental idea of this clustering is based on Conditional Fuzzy C-Means (CFCM) clustering introduced by Pedrycz. The proposed clustering develops clusters preserving homogeneity of the clustered patterns associated with the input and output space. ...

There has been lot of research in recent years for efficient web searching. Several papers have proposed algorithm for user feedback sessions, one such algorithm called “Classified Average Precision”, was introduced to evaluate the performance of inferring user search goals. When the information is retrieved, user clicks on a particular URL. Based on the click rate ranking will be done automati...

some applications are critical and must designed fault tolerant system. usually voting algorithm is one of the principle elements of a fault tolerant system. two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. majority confronts with the problem of threshold limits and voter of weight...