This paper develops a new direct model reference fuzzy adaptive control of SISO continuous-time nonlinear systems. The model following conditions are assured by using an adaptive Takagi-Sugeno (TS) fuzzy system as nonlinear state feedback controller. Both full state information and observer-based control schemes are investigated. It is shown, that the proposed control algorithm guarantees the s...

In this paper, a new method of representing two-dimensional shapes using fuzzy-symbolic features and a similarity measure defined over fuzzy-symbolic features useful for clustering shapes is proposed. A k-mutual nearest neighborhood approach for clustering two-dimensional shapes is presented. The proposed shape representation scheme is invariant to similarity transformations and the clustering ...

An improved initialization method for fuzzy cmeans (FCM) method is proposed which aims at solving the two important issues of clustering performance affected by initial cluster centers and number of clusters. A density based approach is needed to identify the closeness of the data points and to extract cluster center. DBSCAN approach defines ε–neighborhood of a point to determine the core objec...

Digital image segmentation is the process of assigning distinct labels to different objects in a digital image, and the fuzzy segmentation algorithm has been successfully used in the segmentation of images from a wide variety of sources. However, the traditional fuzzy segmentation algorithm fails to segment objects that are characterized by textures whose patterns cannot be successfully describ...

The aim of this paper is to introduce a new methodology for micro-pattern analysis in digital images. The gray-level pixels’ structure in an image neighborhood describes a spatial specific context. Edge, line, spot, blob, corner or texture can be described by this structure. The gray-level values of the image pixel are interpreted as a fuzzy set, and each pixel gray-level as a fuzzy number. A m...

Clustering algorithms with deep neural network has attracted wide attention to scholars. A fuzzy K-means clustering algorithm model on adaptive loss function and entropy regularization (DFKM) is proposed by combining automatic encoder algorithm. Although it introduces improve the robustness of model, its segmentation effect not ideal for high noise. The research purpose this paper focus anti-no...

The rapid development of technology, the Internet, and the development of electronic commerce have led to the emergence of recommender systems. These systems will assist the users in finding and selecting their desired items. The accuracy of the advice in recommender systems is one of the main challenges of these systems. Regarding the fuzzy systems capabilities in determining the borders of us...

− Spatial data mining knows a more and more important interest. Fundamental processes of spatial data mining are in particular clustering and structural patterns detection. These processes are influenced strongly by the concept of proximity or neighborhood. This paper introduces some structures to the construction of a spatial data mining integrating fuzzy structural primitives and propose to o...