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In computer vision, image processing is any form of signal processing for which the input is an image, such as photographs or frames of videos. The output of image processing can be either an image or a set of characteristics or parameters related to image. The color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. The aim is to produce...
Many engineering applications requires solving the fuzzy equations. A method to solve first order fuzzy linear equations using modal interval approach is suggested. The modal interval arithmetic is applied to solve these fuzzy equations, for each ??-cut, which is computationally efficient. Some basic definitions and operations of modal interval arithmetic are included here. Simulation examples validates...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
This paper presents an efficient fuzzy possibilistic code book design for vector quantization in the wavelet packet domain. Wavelet packet tree (WPT) methodology is applied to the whole image. The sub blocks of the input image are decomposed into two level WPT where all coefficients of LL band and the approximation coefficients of LH, HL & HH bands are quantized using the proposed vector quantizer...
Though DNA microarray technology simultaneously measures the expression levels of thousands of genes, only a few underlying gene features may account for significant data variation in gene classification problems. Selection of features from huge data set is difficult and so dimension reduction of gene expression data set is essential in order to determining important features, which play key role...
In this paper a comparison is carried out in order between fuzzy logic controller and adaptive neuro-fuzzy controller. We make use of these two control systems to regulate the temperature of the water bath system. We see that the Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set...
Artificial neural networks (ANN) and fuzzy systems are the widely preferred artificial intelligence techniques for biological computational applications. While ANN is less accurate than fuzzy logic systems, fuzzy theory needs expertise knowledge to guarantee high accuracy. Since both the methodologies possess certain advantages and disadvantages, it is primarily important to compare and contrast these...
This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary power signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of power signal analysis, yielding...
This paper contributes a novel Particle Swarm Optimization (PSO) method. The particle is updated not only by the best position in history (pbest) and the best position among all the particles in the swarm (gbest), but also using the position that is nearest neighbor of pbest. Additionally, we introduce a modified PSO algorithm based on the fuzzy clustering of particles to communication with the nearest...
Design of an optimal controller requires optimization of multiple performance measures that are often noncommensurable and competing with each other. Design of such a controller is indeed a multi-objective optimization problem. Being a population based approach; genetic algorithm (GA) is well suited to solve multi-objective optimization problems. This paper investigates the application of GA-based...
This paper presents the stability analysis of parameter identification. The Takagi Sugeno fuzzy model is employed to represent the discrete time nonlinear dynamical systems. Once the structure of the fuzzy model is fixed, the parameters can be optimized. The parameter identification is accomplished by applying the gradient method where the iteration rates are specific to each parameter. The stability...
To determine the buy and sell time is one of the most important issues for investors in stock market. In this paper, a fuzzy approach to stock market timing is investigated. A fuzzy decision system is constructed based on experiences and techniques of stock and future opportunist. The fuzzy rules are optimized by taking exchange volume into account for better fitting the stock market of mainland China...
A hierarchical Bayesian fuzzy inference nets realtime internal fault diagnostic system for induction motor is proposed. The membership functions and symptom-fault mapping relationship for motor fault diagnosis are obtained from pre-measured site experimental data as well as experts' diagnostic experience/knowledge to distinguish the effect of true fault from various external static factors. With the...
This paper presents the design of fuzzy PI+D controller and fuzzy PID controller for nonlinear systems using 'Gaussian' membership functions. The fuzzy controllers are derived from their conventional continuous time domain controllers. The controllers are developed by first discretizing the controllers' laws and then progressively deriving the steps necessary to incorporate a fuzzy logic control mechanism...
This paper presents the application of fuzzy clustering technique on large load data to greatly reduce calculations in reliability evaluation of restructured power systems. The method involves: first grouping a large load data into few clusters, secondly calculating partial membership value of each load point in each cluster, thirdly calculating reliability indices for each cluster and finally, expressing...
There has been considerable recent activity in the area of Spiking Neural P systems and notably, their extension by the simulation of astrocytes. Work in this area has focused on intracellular interaction between astrocytes and neurons. This paper further extends this idea by considering extracellular communication between astrocytes, in particular linking the astrocytes in a network that draws on...
This paper addresses a novel issue of intuitionistic fuzzy c means color clustering using intuitionistic fuzzy set theory. The intuitionistic fuzzy set theory takes into the membership degree and non membership degree. Non membership degree is calculated from Sugeno type intuitionistic fuzzy complement. The introduction of another uncertainty term i.e. the non membership degree helps to converge the...
The study of multi-objective optimization has matured to a level where uncertainty is considered when comparing and evaluating solutions for any given problem. This paper reviews the current techniques that have been proposed to include uncertainty within a multi-objective framework. Probabilistic as well as fuzzy methods are reviewed. A new method to identify sample representative solutions from...
In this paper, we propose a framework of multi-genre movie recommender system based on neuro-fuzzy decision tree (NFDT) methodology. The system is capable of recommending list of movies in descending order of preference in response to user queries and profiles. The system also takes care of attempt to vote stuffing using novel application of fuzzy c-means clustering algorithm. Typical user query and...
We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal...
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