To identify T-S models, this paper presents a so-called “subtractive fuzzy C-means clustering” approach, in which the results of subtractive clustering are applied to initialize clustering centers and the number of rules in order to perform adaptive clustering. This method not only regulates the division of fuzzy inference system input and output space and determines the relative member functio...

The aim of this research is to analyze ANFIS performance for prediction of financial time series data. Financial time series data is usually characterized by volatility clustering, persistence, and leptokurtic data behavior. The financial time series data are usually non-stationary and non-linear. ARIMA has a good performance to predict linear time series data, but its performance is decreasing...

In this paper, a comparative study of classification of the analog modulated communication signals using clustering techniques is introduced. Four different clustering algorithms are implemented for classifying the analog signals. These clustering techniques are K-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. Two key features are used for characteri...

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon th...

The efficiency of a nation’s progress is determined by variety factors; however, transportation plays critical role in boosting because it facilitates trade and communication between countries. majority powered fossil fuels such as gasoline or diesel, which will be depleted less than 50 years. Another option to operate systems after replacing conventional vehicles with electric (EV). Powering t...

Fuzzy C-means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

Prediction of cutting forces is very important for the design of cutting tools and for process planning. This paper presents a fuzzy modelling method of cutting forces based on subtractive clustering. The subtractive clustering combined with the least-square algorithm identifies the fuzzy prediction model directly from the information obtained from the sensors. In the micro-milling experimental...

Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four of the most representative off-line clustering techniques: K-means clustering, Fuzzy Cmeans clustering, Mountain clustering, and Subtractive clustering. The techniques are i...