Advances in Fuzzy Systems The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. A Modified FNN Fault Diagnosis on PCVD Microwave System Wed, 28 Oct 2015 07:44:22 +0000 A modified FNN fault diagnosis algorithm is presented in this paper for microwave subsystem of Plasma Chemical Vapor Deposition (PCVD). The symptom variables are selected as the crisp inputs, and the corresponding membership functions are obtained from premeasured data as well as experts’ diagnostic experience/knowledge. The prior probability and the restriction coefficients are combined into the FNN algorithm via matrix operator. This modified FNN algorithm is verified for PCVD fault diagnosis application and realizes the MIMO for multifault mode diagnosis. Zhenyu Li and Hongsheng Li Copyright © 2015 Zhenyu Li and Hongsheng Li. All rights reserved. Repairing the Inconsistent Fuzzy Preference Matrix Using Multiobjective PSO Tue, 27 Oct 2015 08:38:11 +0000 This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions. Abba Suganda Girsang, Chun-Wei Tsai, and Chu-Sing Yang Copyright © 2015 Abba Suganda Girsang et al. All rights reserved. A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System Mon, 28 Sep 2015 12:16:43 +0000 This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC. S. M. Odeh, A. M. Mora, M. N. Moreno, and J. J. Merelo Copyright © 2015 S. M. Odeh et al. All rights reserved. Quantitative Analyses and Development of a -Incrementation Algorithm for FCM with Tsallis Entropy Maximization Wed, 19 Aug 2015 09:03:05 +0000 Tsallis entropy is a -parameter extension of Shannon entropy. By extremizing the Tsallis entropy within the framework of fuzzy -means clustering (FCM), a membership function similar to the statistical mechanical distribution function is obtained. The Tsallis entropy-based DA-FCM algorithm was developed by combining it with the deterministic annealing (DA) method. One of the challenges of this method is to determine an appropriate initial annealing temperature and a value, according to the data distribution. This is complex, because the membership function changes its shape by decreasing the temperature or by increasing . Quantitative relationships between the temperature and are examined, and the results show that, in order to change equally, inverse changes must be made to the temperature and . Accordingly, in this paper, we propose and investigate two kinds of combinatorial methods for -incrementation and the reduction of temperature for use in the Tsallis entropy-based FCM. In the proposed methods, is defined as a function of the temperature. Experiments are performed using Fisher’s iris dataset, and the proposed methods are confirmed to determine an appropriate value in many cases. Makoto Yasuda Copyright © 2015 Makoto Yasuda. All rights reserved. Application of Fuzzy Optimization to the Orienteering Problem Mon, 13 Jul 2015 07:40:46 +0000 This paper deals with the orienteering problem (OP) which is a combination of two well-known problems (i.e., travelling salesman problem and the knapsack problem). OP is an NP-hard problem and is useful in appropriately modeling several challenging applications. As the parameters involved in these applications cannot be measured precisely, depicting them using crisp numbers is unrealistic. Further, the decision maker may be satisfied with graded satisfaction levels of solutions, which cannot be formulated using a crisp program. To deal with the above-stated two issues, we formulate the fuzzy orienteering problem (FOP) and provide a method to solve it. Here we state the two necessary conditions of OP of maximizing the total collected score and minimizing the time taken to traverse a path (within the specified time bound) as fuzzy goals and the remaining necessary conditions as crisp constraints. Using the max-min formulation of the fuzzy sets obtained from the fuzzy goals, we calculate the fuzzy decision sets ( and ) that contain the feasible paths and the desirable paths, respectively, along with the degrees to which they are acceptable. To efficiently solve large instances of FOP, we also present a parallel algorithm on CREW PRAM model. Madhushi Verma and K. K. Shukla Copyright © 2015 Madhushi Verma and K. K. Shukla. All rights reserved. New Closeness Coefficients for Fuzzy Similarity Based Fuzzy TOPSIS: An Approach Combining Fuzzy Entropy and Multidistance Tue, 23 Jun 2015 06:43:52 +0000 This paper introduces new closeness coefficients for fuzzy similarity based TOPSIS. The new closeness coefficients are based on multidistance or fuzzy entropy, are able to take into consideration the level of similarity between analysed criteria, and can be used to account for the consistency or homogeneity of, for example, performance measuring criteria. The commonly known OWA operator is used in the aggregation process over the fuzzy similarity values. A range of orness values is considered in creating a fuzzy overall ranking for each object, after which the fuzzy rankings are ordered to find a final linear ranking. The presented method is numerically applied to a research and development project selection problem and the effect of using two new closeness coefficients based on multidistance and fuzzy entropy is numerically illustrated. Mikael Collan, Mario Fedrizzi, and Pasi Luukka Copyright © 2015 Mikael Collan et al. All rights reserved. On Normalistic Vague Soft Groups and Normalistic Vague Soft Group Homomorphism Wed, 10 Jun 2015 12:51:55 +0000 We further develop the theory of vague soft groups by establishing the concept of normalistic vague soft groups and normalistic vague soft group homomorphism as a continuation to the notion of vague soft groups and vague soft homomorphism. The properties and structural characteristics of these concepts as well as the structures that are preserved under the normalistic vague soft group homomorphism are studied and discussed. Ganeshsree Selvachandran and Abdul Razak Salleh Copyright © 2015 Ganeshsree Selvachandran and Abdul Razak Salleh. All rights reserved. Fuzzy Clustering Using the Convex Hull as Geometrical Model Tue, 21 Apr 2015 10:42:12 +0000 A new approach to fuzzy clustering is proposed in this paper. It aims to relax some constraints imposed by known algorithms using a generalized geometrical model for clusters that is based on the convex hull computation. A method is also proposed in order to determine suitable membership functions and hence to represent fuzzy clusters based on the adopted geometrical model. The convex hull is not only used at the end of clustering analysis for the geometric data interpretation but also used during the fuzzy data partitioning within an online sequential procedure in order to calculate the membership function. Consequently, a pure fuzzy clustering algorithm is obtained where clusters are fitted to the data distribution by means of the fuzzy membership of patterns to each cluster. The numerical results reported in the paper show the validity and the efficacy of the proposed approach with respect to other well-known clustering algorithms. Luca Liparulo, Andrea Proietti, and Massimo Panella Copyright © 2015 Luca Liparulo et al. All rights reserved. An Analytical Approach to Evaluating Nonmonotonic Functions of Fuzzy Numbers Tue, 31 Mar 2015 11:42:58 +0000 This paper presents a novel analytical approach to evaluating continuous, nonmonotonic functions of independent fuzzy numbers. The approach is based on a parametric -cut representation of fuzzy numbers and allows for the inclusion of parameter uncertainties into mathematical models. Arthur Seibel and Josef Schlattmann Copyright © 2015 Arthur Seibel and Josef Schlattmann. All rights reserved. Fuzzy Methods for Data Analysis Wed, 25 Mar 2015 06:30:18 +0000 Ferdinando Di Martino, Irina Perfilieva, and Salvatore Sessa Copyright © 2015 Ferdinando Di Martino et al. All rights reserved. A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices Mon, 23 Mar 2015 12:18:17 +0000 In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. However, in many such organizations, they often hesitate to publish their databases because of privacy and security issues although they believe the advantages of collaborative analysis. This paper proposes a novel collaborative framework for utilizing vertically partitioned cooccurrence matrices in fuzzy co-cluster structure estimation, in which cooccurrence information among objects and items is separately stored in several sites. In order to utilize such distributed data sets without fear of information leaks, a privacy preserving procedure is introduced to fuzzy clustering for categorical multivariate data (FCCM). Withholding each element of cooccurrence matrices, only object memberships are shared by multiple sites and their (implicit) joint co-cluster structures are revealed through an iterative clustering process. Several experimental results demonstrate that collaborative analysis can contribute to revealing global intrinsic co-cluster structures of separate matrices rather than individual site-wise analysis. The novel framework makes it possible for many private and public organizations to share common data structural knowledge without fear of information leaks. Katsuhiro Honda, Toshiya Oda, Daiji Tanaka, and Akira Notsu Copyright © 2015 Katsuhiro Honda et al. All rights reserved. Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms Mon, 23 Mar 2015 07:23:14 +0000 Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio. Arindam Chaudhuri Copyright © 2015 Arindam Chaudhuri. All rights reserved. Provenance Study of the Terracotta Army of Qin Shihuang’s Mausoleum by Fuzzy Cluster Analysis Thu, 19 Mar 2015 09:44:40 +0000 20 samples and 44 samples of terracotta warriors and horses from the 1st and 3rd pits of Qin Shihuang’s Mausoleum, 20 samples of clay near Qin’s Mausoleum, and 2 samples of Yaozhou porcelain bodies are obtained to determine the contents of 32 elements in each of them by neutron activation analysis (NAA). The NAA data are further analyzed using fuzzy cluster analysis to obtain the fuzzy cluster trend diagram. The analysis shows that the origins of the raw material of the terracotta warriors and horses from 1st and 3rd pits are not exactly the same but are closely related to the loam soil layer near Qin’s Mausoleum while distant from the loess layers in the same area and remotely related to the Yaozhou porcelain bodies. It can be concluded that the raw material of the terracotta warriors and horses was taken from certain loam layer near Qin’s Mausoleum and the kiln sites might be located nearby. Rongwu Li and Guoxia Li Copyright © 2015 Rongwu Li and Guoxia Li. All rights reserved. A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance Thu, 19 Mar 2015 09:06:01 +0000 A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. The values are derived from qualitative variables and expert opinion of membership in these variables found in industry survey data. The application is made to a supply chain management decision of supplier selection based upon delivery performance which is further divided into attributes that comprise this criterion. The algorithm allows multiple criteria to be considered for each decision parameter. Large sets of survey data regarding six suppliers in the electronic parts industry are gathered from over 150 purchasers and are analyzed through spreadsheet modeling of the fuzzy algorithm. The resulting decision support system allows supply chain managers to improve supplier selection decisions by applying fuzzy measures of criteria and associated beliefs across the dataset. The proposed model and method are highly adaptable to existing survey datasets, including datasets that have incomplete data, and can be implemented in organizations with low decision support resources, such as small and medium sized organizations. Jonathan Davis, Margaret F. Shipley, and Gary Stading Copyright © 2015 Jonathan Davis et al. All rights reserved. The I-V Characteristic Prediction of BCD LV pMOSFET Devices Based on an ANFIS-Based Methodology Tue, 17 Mar 2015 10:20:14 +0000 Comprehensive and predictive modeling of submicron devices using the traditional TCAD EDA tools of device simulation has become increasingly perplexing due to a lack of reliable models and difficulties in calibrating available device models. This paper proposes a new technique to model BCD submicron pMOSFET devices and to predict device behaviors under different bias conditions and different geometry dimensions by using the adaptive neurofuzzy inference system (ANFIS), which combines fuzzy theory and adaptive neuronetworking. Here, the power of using ANFIS to realize the I-V behaviors is demonstrated in these p-channel MOS transistors. After a systematic evaluation, it can be found that the predicting results of I-V behaviors of complicated submicron pMOSFETs by ANFIS are compared with the actual diagnostic experiment data, and a good agreement has been obtained. Furthermore, the error percentage was no greater than 2.5%. As such, the demonstrated benefits of this new proposed technique include precise prediction and easier implementation. Shen-Li Chen Copyright © 2015 Shen-Li Chen. All rights reserved. n-Tupled Fixed Points Theorem in Fuzzy Metric Spaces with Application Mon, 09 Mar 2015 07:35:29 +0000 We will introduce the concept of -tupled fixed points (for positive integer ) in fuzzy metric space by mild modification of the concept of -tupled fixed points (for even positive interger ) introduced by Imdad et al. (2013) in metric spaces. As application of the above-mentioned concept, we will establish some -tupled fixed point theorems for contractive type mappings in fuzzy metric space which extends the result of Roldán et al. (2013). Also we have given an application to solve a kind of Lipschitzian systems for variables and an integral system. P. P. Murthy and Rashmi Kenvat Copyright © 2015 P. P. Murthy and Rashmi Kenvat. All rights reserved. Discrete-Time Exponentially Stabilizing Fuzzy Sliding Mode Control via Lyapunov’s Method Wed, 25 Feb 2015 14:20:42 +0000 The exponentially stabilizing state feedback control algorithm is developed by Lyapunov’s second method leading to the variable structure system with chattering free sliding modes. Linear time-invariant discrete-time second order plant is considered and the control law is obtained by using a simple fuzzy controller. The analytical structure of the proposed controller is derived and used to prove exponential stability of sliding subspace. Essentially, the control algorithm drives the system from an arbitrary initial state to a prescribed so-called sliding subspace S, in finite time and with prescribed velocity estimate. Inside the sliding subspace S, the system is switched to the sliding mode regime and stays in it forever. The proposed algorithm is tested on the real system in practice, DC servo motor, and simulation and experimental results are given. Radiša Ž. Jovanović and Zoran M. Bučevac Copyright © 2015 Radiša Ž. Jovanović and Zoran M. Bučevac. All rights reserved. Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization Sun, 14 Dec 2014 06:37:11 +0000 An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods. Ajay Khunteta and D. Ghosh Copyright © 2014 Ajay Khunteta and D. Ghosh. All rights reserved. Fuzzy Logic Control of a Ball on Sphere System Thu, 11 Dec 2014 09:50:24 +0000 The scope of this paper is to present a fuzzy logic control of a class of multi-input multioutput (MIMO) nonlinear systems called “system of ball on a sphere,” such an inherently nonlinear, unstable, and underactuated system, considered truly to be two independent ball and wheel systems around its equilibrium point. In this work, Sugeno method is investigated as a fuzzy controller method, so it works in a good state with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for such nonlinear dynamic systems. The system’s dynamic is described and the equations are illustrated. The outputs are shown in different figures so as to be compared. Finally, these simulation results show the exactness of the controller’s performance. Seyed Alireza Moezi, Ehsan Zakeri, Yousef Bazargan-Lari, and Mahmood Khalghollah Copyright © 2014 Seyed Alireza Moezi et al. All rights reserved. Synthesis of Multicriteria Controller by Means of Fuzzy Logic Approach Wed, 19 Nov 2014 13:37:35 +0000 A two-mass fuzzy control system is considered. For fuzzification process, classical both linear and nonlinear membership functions are used. To find optimal values of membership function’s parameters, genetic algorithm is used. To take into account values of both output and intermediate parameters of the system, a penalty function is considered. Research is conducted for the case of speed control system and displacement control system. Obtained results are compared with the case of the system with classical, crisp controller. Andrew Lozynskyy and Lyubomyr Demkiv Copyright © 2014 Andrew Lozynskyy and Lyubomyr Demkiv. All rights reserved. Interest Measures for Fuzzy Association Rules Based on Expectations of Independence Tue, 07 Oct 2014 07:18:50 +0000 Lift, leverage, and conviction are three of the best commonly known interest measures for crisp association rules. All of them are based on a comparison of observed support and the support that is expected if the antecedent and consequent part of the rule were stochastically independent. The aim of this paper is to provide a correct definition of lift, leverage, and conviction measures for fuzzy association rules and to study some of their interesting mathematical properties. Michal Burda Copyright © 2014 Michal Burda. All rights reserved. Real Time Implementation of Incremental Fuzzy Logic Controller for Gas Pipeline Corrosion Control Tue, 09 Sep 2014 00:00:00 +0000 A robust virtual instrumentation based fuzzy incremental corrosion controller is presented to protect metallic gas pipelines. Controller output depends on error and change in error of the controlled variable. For corrosion control purpose pipe to soil potential is considered as process variable. The proposed fuzzy incremental controller is designed using a very simple control rule base and the most natural and unbiased membership functions. The proposed scheme is tested for a wide range of pipe to soil potential control. Performance comparison between the conventional proportional integral type and proposed fuzzy incremental controller is made in terms of several performance criteria such as peak overshoot, settling time, and rise time. Result shows that the proposed controller outperforms its conventional counterpart in each case. Designed controller can be taken in automode without waiting for initial polarization to stabilize. Initial startup curve of proportional integral controller and fuzzy incremental controller is reported. This controller can be used to protect any metallic structures such as pipelines, tanks, concrete structures, ship, and offshore structures. Gopalakrishnan Jayapalan, Ganga Agnihotri, and D. M. Deshpande Copyright © 2014 Gopalakrishnan Jayapalan et al. All rights reserved. On Intuitionistic Fuzzy Entropy of Order-α Wed, 03 Sep 2014 10:42:31 +0000 Using the idea of Rènyi’s entropy, intuitionistic fuzzy entropy of order-α is proposed in the setting of intuitionistic fuzzy sets theory. This measure is a generalized version of fuzzy entropy of order-α proposed by Bhandari and Pal and intuitionistic fuzzy entropy defined by Vlachos and Sergiadis. Our study of the four essential and some other properties of the proposed measure clearly establishes the validity of the measure as intuitionistic fuzzy entropy. Finally, a numerical example is given to show that the proposed entropy measure for intuitionistic fuzzy set is reasonable by comparing it with other existing entropies. Rajkumar Verma and BhuDev Sharma Copyright © 2014 Rajkumar Verma and BhuDev Sharma. All rights reserved. Fuzzy Set Field and Fuzzy Metric Tue, 02 Sep 2014 00:00:00 +0000 The notation of fuzzy set field is introduced. A fuzzy metric is redefined on fuzzy set field and on arbitrary fuzzy set in a field. The metric redefined is between fuzzy points and constitutes both fuzziness and crisp property of vector. In addition, a fuzzy magnitude of a fuzzy point in a field is defined. Gebru Gebray and B. Krishna Reddy Copyright © 2014 Gebru Gebray and B. Krishna Reddy. All rights reserved. Revised Max-Min Average Composition Method for Decision Making Using Intuitionistic Fuzzy Soft Matrix Theory Wed, 20 Aug 2014 07:34:43 +0000 In this paper a revised Intuitionistic Fuzzy Max-Min Average Composition Method is proposed to construct the decision method for the selection of the professional students based on their skills by the recruiters using the operations of Intuitionistic Fuzzy Soft Matrices. In Shanmugasundaram et al. (2014), Intuitionistic Fuzzy Max-Min Average Composition Method was introduced and applied in Medical diagnosis problem. Sanchez’s approach (Sanchez (1979)) for decision making is studied and the concept is modified for the application of Intuitionistic fuzzy soft set theory. Through a survey, the opportunities and selection of the students with the help of Intuitionistic fuzzy soft matrix operations along with Intuitionistic fuzzy max-min average composition method is discussed. P. Shanmugasundaram, C. V. Seshaiah, and K. Rathi Copyright © 2014 P. Shanmugasundaram et al. All rights reserved. Development of a System to Assist Automatic Translation of Hand-Drawn Maps into Tactile Graphics and Its Usability Evaluation Wed, 23 Jul 2014 11:29:17 +0000 Tactile graphics are images that use raised surfaces so that a visually impaired person can feel them. Tactile maps are used by blind and partially sighted people when navigating around an environment, and they are also used prior to a visit for orientation purposes. Since the ability to read tactile graphics deeply depends on individuals, providing tactile graphics individually is needed. This implies that producing tactile graphics should be as simple as possible. Based on this background, we are developing a system for automating production of tactile maps from hand-drawn figures. In this paper, we first present a pattern recognition method for hand-drawn maps. The usability of our system is then evaluated by comparing it with the two different methods to produce tactile graphics. Jianjun Chen and Noboru Takagi Copyright © 2014 Jianjun Chen and Noboru Takagi. All rights reserved. Designing of 2-Stage CPU Scheduler Using Vague Logic Tue, 22 Jul 2014 09:22:59 +0000 In operating system the CPU scheduler is designed in such a way that all the resources are fully utilized. With static priority scheduling the scheduler ensures that CPU time will be assigned according to the highest priority but ignores other factors; hence it affects the performance. To improve the performance, we propose a new 2-stage vague logic based scheduler. In first stage, scheduler handles the uncertainty of tasks using the proposed vague inference system (VIS). In second stage, scheduler uses a vague oriented priority scheduling (VOPS) algorithm for selection of next process. The goal of this work is to handle the uncertainty as well as to optimize both the average and the amount of variation with respect to performance matrices average waiting time, average turnaround time, and average normalized turnaround time. A simulation using MATLAB is also conducted to evaluate the performance. Simulation results show that the proposed scheduler using VOPS algorithm is better than the scheduler with traditional priority scheduling algorithm. Results are based on the dual concept of fuzzy theory and its generalization, vague theory. Additionally, this work comprises the evaluation of VOPS and shortest job first algorithm. The outcome of proposed VOPS algorithm is much closer to the result obtained by traditional shortest job first. Supriya Raheja, Reena Dhadich, and Smita Rajpal Copyright © 2014 Supriya Raheja et al. All rights reserved. A Geometric Fuzzy-Based Approach for Airport Clustering Thu, 17 Jul 2014 10:03:06 +0000 Airport classification is a common need in the air transport field due to several purposes—such as resource allocation, identification of crucial nodes, and real-time identification of substitute nodes—which also depend on the involved actors’ expectations. In this paper a fuzzy-based procedure has been proposed to cluster airports by using a fuzzy geometric point of view according to the concept of unit-hypercube. By representing each airport as a point in the given reference metric space, the geometric distance among airports—which corresponds to a measure of similarity—has in fact an intrinsic fuzzy nature due to the airport specific characteristics. The proposed procedure has been applied to a test case concerning the Italian airport network and the obtained results are in line with expectations. Maria Nadia Postorino and Mario Versaci Copyright © 2014 Maria Nadia Postorino and Mario Versaci. All rights reserved. Several Types of Totally Continuous Functions in Double Fuzzy Topological Spaces Thu, 10 Jul 2014 08:41:20 +0000 We introduce the notions of totally continuous functions, totally semicontinuous functions, and semitotally continuous functions in double fuzzy topological spaces. Their characterizations and the relationship with other already known kinds of functions are introduced and discussed. Fatimah M. Mohammed, M. S. M. Noorani, and A. Ghareeb Copyright © 2014 Fatimah M. Mohammed et al. All rights reserved. Credit Risk Prediction Using Fuzzy Immune Learning Tue, 24 Jun 2014 00:00:00 +0000 The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks. Ehsan Kamalloo and Mohammad Saniee Abadeh Copyright © 2014 Ehsan Kamalloo and Mohammad Saniee Abadeh. All rights reserved.