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.
In this paper, an objective function based approach is presented to characterize a fuzzy classifier system via a kernel learning algorithms for non-linear data. We combine the distance based kernel fuzzy clustering and the non-linear support vector classification (SVC) with a conjoint objective based fuzzy clustering method in a novel way in order to learn a fuzzy classifier system. The two objectives...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
Within the last decade or so, there have been several proposals to develop fuzzy system models via functional representation in place of rule base representation. We review at least three of these approaches.
In local version (lbest) of particle swarm optimization (PSO), each particle has only the information of its own and its neighbors' best, rather than all the population. The neighborhood of each particle is generally defined as topologically nearest particles to such particle at each side. There is no robust approach for determining the neighborhood size in the literature and all of them rely on try...
In this paper, a multi-objective genetic algorithm for data clustering based on the robust fuzzy least trimmed squares estimator is proposed. The clustering methodology addresses two critical issues in unsupervised data clustering - the ability to produce meaningful classification in noisy data, and the requirement that the number of clusters be known a priori. The GA-driven clustering routine optimizes...
Modern day manufactured products in high-technology industries demand ever higher precision and accuracy. The need for continuous improvements in product quality, reliability, and manufacturing efficiency has imposed strict demands on automated product measurement and evaluation on uncertainties in machining process. Type-2 fuzzy logic estimation provides the possibility to indicate the uncertainties...
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.