Fuzzy Neighbor Research Articles

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition.

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.

A robust density peaks clustering algorithm using fuzzy neighborhood

The density peaks (DP) clustering approach, a novel density-based clustering algorithm, detects clusters with arbitrary shape. However, this method uses a crisp neighborhood relation to calculate local density. It cannot identify the different values of the neighborhood membership degrees of the points with respect to different distances from core point. The proposed FN-DP (fuzzy neighborhood density peaks) clustering algorithm uses fuzzy neighborhood relation to define the local density in FJP (fuzzy joint points) algorithm. The proposed algorithm integrates the speed of DP clustering algorithm with the robustness of FJP algorithm. The experimental results illustrate the superior performance of our algorithm compared with the DP clustering approach.

Quasi fuzzy neighborhood groups and their relationships to fuzzy neighborhood groups

Quasi fuzzy neighborhood groups and their relationships to fuzzy neighborhood groups

An unmanned marine vehicle thruster fault diagnosis scheme based on OFNDA

ABSTRACTIn recent years, there has been a growing interest in the use of fault analysis techniques in unmanned marine vehicles (UMVs) owing to their significant impact on marine operations. This study presents a novel approach to the diagnosis of unbalanced load (blades damage) faults in an electric thruster motor in UMV propulsion systems based on orthogonal fuzzy neighbourhood discriminative analysis for feature dimensionality reduction. The diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and the optimal number of mother wavelet function and levels of resolution by analysing the vibration and current signals. As a result of analysis and comparisons, the Deubechies 12 (db12) wavelet and level 8 were chosen. A dynamic recurrent neural network was chosen for fault classification and level of fault severity prediction was implemented. Four faulty conditions were analysed under laboratory conditions and these were recreated by damaging the blades of a motor. The results obtained from the simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults with greater speed and accuracy compared to existing methods.

On fuzzy rough topological structures

In this paper the concepts of fuzzy rough topology, fuzzy rough open and closed sets, fuzzy rough neighborhood, fuzzy rough point, fuzzy rough closure and fuzzy rough interior are introduced and some of their properties established.

Feature subset selection based on fuzzy neighborhood rough sets

Rough set theory has been extensively discussed in machine learning and pattern recognition. It provides us another important theoretical tool for feature selection. In this paper, we construct a novel rough set model for feature subset selection. First, we define the fuzzy decision of a sample by using the concept of fuzzy neighborhood. A parameterized fuzzy relation is introduced to characterize fuzzy information granules for analysis of real-valued data. Then, we use the relationship between fuzzy neighborhood and fuzzy decision to construct a new rough set model: fuzzy neighborhood rough set model. Based on this model, the definitions of upper and lower approximation, boundary region and positive region are given, and the effects of parameters on these concepts are discussed. To make the new model tolerate noises in data, we introduce a variable-precision fuzzy neighborhood rough set model. This model can decrease the possibility that a sample is classified into a wrong category. Finally, we define the dependency between fuzzy decision and condition attributes and employ the dependency to evaluate the significance of a candidate feature, using which a greedy feature subset selection algorithm is designed. The proposed algorithm is compared with some classical algorithms. The experiments show that the proposed algorithm gets higher classification performance and the numbers of selected features are relatively small.

A multi-criteria route planning model based on fuzzy preference degrees of stops

Integrated utilization of new technologies such as smart phones, tablet devices, and satellite maps has entered our daily lives recently. Nevertheless, many new applications are being developed mostly based on these technologies. The optimal route planning, which makes use of the public transport network structure between any selected origin and destination points, is one of the interesting applications among them. Route planning applications used today mostly focus on the aspects such that passengers use nearest stops around origin and destination geographical points, or use set of stops around these points within some walking radius. In these applications, which work on the classical (crisp) logic base, all stops on the walking distance have the same preference degree. However, in this study a novel fuzzy model is proposed which also takes into account preferences such as the stop’s activity, and count of transit lines passing through the stop besides the walking distance. Using all these three preferences, aggregated fuzzy preference degrees of stops are calculated. The “optimum” routes between any origin and destination pair are constructed using feasible transfer points, which are chosen among the alternatives having the highest preference degrees overall. Fuzzy neighborhood relations such as “stop-stop”, “stop-line”, and “line-line” are introduced in order to employ in preference degree evaluations.Apart from the aggregated degree of the preferences mentioned above, we also consider to minimize the total number of transit stops travelled on any route for establishing optimal routes. This additional preference can be described the time duration spent on transport vehicles, such as buses, trains, subways or ferries. Therefore, we propose a two-criteria route-planning problem in this study, where we try to maximize the aggregated preference degree of a route and to minimize the number of stops used on a route. Fuzzy optimal solutions for this problem are constructed via γ-level solutions of the fuzzy problem and a heuristic algorithm providing these solutions is proposed. This model and its algorithm can be considered as an optimal route search engine for mobile applications that could be used by urban public transport passengers.

Hybridizations of genetic algorithms and neighborhood search metaheuristics for fuzzy bus terminal location problems

We propose modified hybridizations of genetic algorithms with some neighborhood search based metaheuristics. In our hybrid algorithms, we consider gradually increasing probability for the application of the neighborhood search procedure on the best individuals as the number of iterations of the genetic algorithm increases. We implement the proposed hybrid algorithms and compare their performance with two other recently proposed hybrid algorithms which, in contrast, use the neighborhood search procedure on all the individuals of the population, two hybrid algorithms applying simulated annealing on the best individual in the papulation in every iteration and three non-hybrid metaheuristic algorithms. To investigate the effectiveness of the proposed algorithms, we apply the algorithms to our proposed fuzzy bus terminal location problem models. The fuzzy model is considered to have fuzzy number of passengers corresponding to the nodes as well as fuzzy neighborhoods, together with preassigned lower and upper bounds for the number of required terminals. The algorithms are tested on a variety of randomly generated large scale fuzzy bus terminal location problems in with fuzzy cost coefficients. The fuzzy objective is transformed into a crisp one by use of a ranking function. The computational experiments demonstrate the effectiveness of the proposed algorithms on large scale problems. Finally, to show the effectiveness of our proposed hybridizations, we also make a comparative study of our algorithms on crisp facility location and quadratic assignment test problems.

On (L, M)-double fuzzy remote neighborhood systems in (L, M)-DFTML1

On (L, M)-double fuzzy remote neighborhood systems in (L, M)-DFTML1

Fuzzy neighborhood operators based on fuzzy coverings

In many data mining processes, neighborhood operators play an important role as they are generalizations of equivalence classes which were used in the original rough set model of Pawlak. In this article, we introduce the notion of fuzzy neighborhood system of an object based on a given fuzzy covering, as well as the notion of the fuzzy minimal and maximal descriptions of an object. Moreover, we extend the definition of four covering-based neighborhood operators as well as six derived coverings discussed by Yao and Yao to the fuzzy setting. We combine these fuzzy neighborhood operators and fuzzy coverings and prove that only sixteen different fuzzy neighborhood operators are obtained. Moreover, we study the partial order relations between those sixteen operators.

A unified view of consistent functions

In order to compare the structures and properties of two generalized information systems, a class of special mappings, called consistent functions in some literature, have been extensively studied over the past years. Most recently, consistent functions have been unified and extended into the framework of neighborhood systems which have general binary relations, dominance relations, and coverings as instances. In this paper, we further extend and investigate the notion of consistent functions for fuzzy neighborhood systems. After introducing the definition of extended consistent functions and showing their relationships with related functions, we present some basic properties of the new consistent functions with respect to set-theoretic operations and fuzzy neighborhoods, respectively. As an application, we consider the attribute reduction based on consistent functions. In doing so, we contribute to a unified view of consistent functions and attempt to develop a general theory for investigating the invariant properties of fuzzy neighborhood systems under consistent functions.

Optimization of Multiresolution Segmentation for Object-Oriented Road Detection from High-Resolution Images

. This study aims to detect roads in high-resolution satellite images via an automatic object-oriented analysis. In this regard, an automatic image segmentation method is proposed to generate appropriate image objects. To achieve this goal, a genetic algorithm optimization with a new cost function is designed to set proper segmentation parameters, including the scale factor and the weights of the shape and compactness heterogeneities. The obtained image segments are then classified as roads or background features, using a fuzzy nearest neighborhood method.The proposed method is implemented on 2 pan-sharpened IKONOS images covering urban areas in the cities of Shiraz and Yazd, Iran. A comparison of these segments with those obtained using traditional manual methods proves the efficiency of this method. Moreover, the entire road detection system is compared with a support vector machine pixel-based classification; the proposed method improved the accuracy by 5%.

Graded fuzzy topological spaces

In this paper, graded fuzzy topological spaces based on the notion of neighbourhood system of graded fuzzy neighbourhoods at ordinary points are introduced and studied. These graded fuzzy neighbourhoods at ordinary points and usual subsets played the main role in this study.

Identification of air pollution patterns using a modified fuzzy co-occurrence pattern mining method

Spatio-temporal co-occurrence patterns represent subsets of object types which are located together in both space and time. Discovering spatio-temporal co-occurrence patterns is an important task having many application domains. There are a number of developed methods to mine co-occurrence patterns; however, using them needs a unique parameter to define the neighborhood. Identification of a unique optimum k-value or neighborhood radius is a challenging issue in different application domains. The developed method of this research defines a new fuzzy neighborhood and new fuzzy metrics to be applicable for real applications such as air pollution, especially when the researchers have no comprehensive knowledge regarding the application domain; in addition, it mines patterns based on the fuzzy nature of environmental phenomena. The new method mines patterns locally without localization step to speed up the mining process and considers all feature types (point, line and polygon) to handle all applications. Subsequently, it is applied to a real data set of Tehran city for air pollution to discover significant co-occurrence patterns of air pollution and influencing environmental parameters such as meteorological, topography and traffic. The case study results showed seven meaningful patterns among air pollution classes 2 and 3 and wind speed class 1, topography class 1 and traffic classes 1 and 2. The evaluation confirmed the accuracy and applicability of the new developed method for air pollution case. Furthermore, two performance tests for the method itself and a performance test against a crisp method were done, where the results exhibited an efficient computational performance.

Intuitionistic fuzzy competition graphs

Intuitionistic fuzzy competition graph as a generalization of competition graph is introduced. In this paper, two generalizations of intuitionistic fuzzy competition graphs as intuitionistic fuzzy k-competition graphs and p-competition intuitionistic fuzzy graphs are introduced. Also, intuitionistic fuzzy neighbourhood graphs, m-steps intuitionistic fuzzy competition graphs are defined and investigated many interesting results. A very appropriate application of intuitionistic fuzzy graph in ecosystem is discussed to highlight the importance and use of intuitionistic fuzzy graph in real life.

Categories isomorphic to (L,M)-DFTOP

Abstract The aim of this paper is to introduce the notion of ( L,M )-double fuzzy quasi-coincident neighborhood system, and investigate the relationships between ( L,M )-double fuzzy quasi-coincident neighborhood spaces and ( L,M )-double fuzzy topological spaces in the category aspect. Also, we give a characterization of ( L,M )-DFTOP, which is called compatible antichain L -double topologies and consider the categorical connections between them. Finally, to better understand ( L,M )- DFTOP , several categories were introduced.

Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

This article presents a novel approach to the diagnosis of unbalanced faults in a trolling motor under stationary operating conditions. The trolling motor being typically of that used as the propulsion system for an unmanned surface vehicle, the diagnosis approach is based on the use of discrete wavelet transforms as a feature extraction tool and a time-delayed neural network for fault classification. The time-delayed neural network classifies between healthy and faulty conditions of the trolling motor by analysing the stator current and vibration. To overcome feature redundancy, which affects diagnosis accuracy, several feature reduction methods have been tested, and the orthogonal fuzzy neighbourhood discriminant analysis approach is found to be the most effective method. Four faulty conditions were analysed under laboratory conditions, where one of the blades causing damage to the trolling motor is cut into 10%, 25%, half and then into full to simulate the effects of propeller blades being damaged partly or fully. The results obtained from the real-time simulation demonstrate the effectiveness and reliability of the proposed methodology in classifying the different faults faster and accurately.

FGSN: Fuzzy Granular Social Networks – Model and applications

Social network data has been modeled with several approaches, including Sociogram and Sociomatrices, which are popular and comprehensive. Similar to these we have developed here a novel modeling technique based on granular computing theory and fuzzy neighborhood systems, which provides a uniform framework to represent social networks. In this model, a social network is represented with a collection of granules. Fuzzy sets are used for defining the granules. The model is named Fuzzy Granular Social Network (FGSN). Familiar measures of networks viz. degree, betweenness, embeddedness and clustering coefficient are redefined in the context of this new framework. Two measures, namely, entropy of FGSN and energy of granules are defined to quantify the uncertainty involved in FGSN arising from fuzziness in the relationships of actors. Experimental results demonstrate the applicability of the model in two well known problems of social networks, namely, target set selection and community detection with comparative studies.

Generalized Sequential Convergence in Fuzzy Neighborhood Spaces

The present work deals with introducing and studying a generalized sequential convergence, namely statistical convergence in fuzzy neighborhood spaces. Some properties of statistical convergence on fuzzy neighborhood spaces are discussed.

A Robust Bearing Fault Detection and Diagnosis Technique for Brushless DC Motors Under Non-stationary Operating Conditions

Rolling element bearing defects are among the main reasons for the breakdown of electrical machines, and therefore, early diagnosis of these is necessary to avoid more catastrophic failure consequences. This paper presents a novel approach for identifying rolling element bearing defects in brushless DC motors under non-stationary operating conditions. Stator current and lateral vibration measurements are selected as fault indicators to extract meaningful features, using a discrete wavelet transform. These features are further reduced via the application of orthogonal fuzzy neighbourhood discriminative analysis. A recurrent neural network is then used to detect and classify the presence of bearing faults. The proposed system is implemented and tested in simulation on data collected from an experimental setup, to verify its effectiveness and reliability in accurately detecting and classifying the various faults.