This paper describes the design of an adaptive direct control scheme for a class of nonlinear systems. The architecture is based on a fuzzy inference system (FIS) of Takagi Sugeno (TS) type to approximate a feedback linearization control law. The parameters of the consequent part of the fuzzy system are adapted and changed according to a law derived using Lyapunov stability theory. The asymptot...

In this paper, line search based on Sequential Quadratic Programming is implemented in order to find a solution to Fuzzy Relation Equations. Sequential Quadratic Programming is a gradient-based method that uses a quadratic estimation of the objective function in each iteration’s neighborhood. Unlike analytical approaches, the method can handle equations with any combinations of t-norms and t-co...

A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion disc...

Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are intr...

What is granular computing? As we have said in [6]: There are no mathematically valid formal definitions yet. Informally, any computing theory/technology that involves elements and granules (generalized subsets) may be called granular computing (GrC). Intuitively, elements are the data, and granules are the basic knowledge. So granular computing includes data and knowledge computing/engineering...

In this paper, an adaptive fuzzy backstepping control approach is developed for a class of nonlinear systems with unknown time delays and unmeasured states. By using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive state observer is designed for estimating the unmeasured states. By combining the adaptive backstepping technique with adaptive fuzzy control desi...

Experts in machine learning and fuzzy system frequently identify understanding the data with the use of logical rules. Reasons for inadequacy of crisp and fuzzy rule-based explanations are presented. An approach based on analysis of probabilities of classification p(Ci|X;ρ) as a function of the size of the neighborhood ρ of the given case X is presented. Probabilities are evaluated using Monte ...

Fuzzy integrals as image lters provide a standard representational form which generalize linear lters such as the averaging lter, morphological lters such as at dilations and erosions, and order statistic lters such as the median lter. However, fuzzy integral lters are computationally intensive. Computing the output value obtained by fuzzy integral ltering at a point involves sorting all the pi...

Urban neighborhoods are a unique form of geography in that their boundaries rely on a social definition rather than a well-defined physical or administrative boundary. Currently, geographic gazetteers capture little more than then the centroid of a neighborhood, limiting potential applications of the data. In this paper, we present μ-shapes, an algorithm that employs fuzzy-set theory to model n...

In this paper, a novel adaptive fuzzy backstepping output feedback control scheme is proposed for a class of single-input single-output (SISO) uncertain nonlinear systems without measurements of states. Fuzzy logic systems (FLS) are used to tackle unknown nonlinear functions, and the adaptive fuzzy output feedback controller is constructed by combining fuzzy filters observer design and the dyna...