The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The subtractive clustering algorithm (SC) is a popular method for data clustering. But the radius of each cluster is an important factor which affects the performances of clustering results. This paper proposes an objective function for the genetic algorithm to estimate the optimal value of this parameter. Two experiments show that the proposed method can automatically obtain this parameter for the...
This paper proposes a robust validity index for Fuzzy c-Means (FCM) algorithm. The Fuzzy c-Means algorithm has become of most widely used method in fuzzy clustering. After clustering, it is often necessary to evaluate its results. Such assessment techniques are called cluster validity. The disadvantage of FCM is that the number of clusters must be predetermined. Even if the number of clusters is given,...
This paper proposes a new validity index for the subtractive clustering (SC) algorithm. The subtractive clustering algorithm proposed by Chiu is an effective and simple method for identifying the cluster centers of sampling data based on the concept of a density function. In this paper, a modified SC algorithm for data clustering based on a cluster validity index is proposed to obtain the optimal...
In this paper, we propose a novel GreyCMAC with robust FCM (RFCM) method for function approximation. The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating. In order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering...
The back propagation (BP) algorithm for function approximation is multi-layer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper,...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.