FCTA 2011 Abstracts


Full Papers
Paper Nr: 3
Title:

FUZZY MODEL BUILDING USING PROBABILISTIC RULES

Authors:

Manish Agarwal, K. K. Biswas and Madasu Hanmandlu

Abstract: Uncertainty in the attributes and uncertainty in frequency of their occurrences are inherent to the real world problems and an attempt is made here to tackle them together. The possible connections between the two facets of uncertainty are explored and discussed. This paper also looks at the role of possibility and probability in the context of decision making and in the process utilizes the existing fuzzy models by incorporating the multiple probabilistic outputs in the associated fuzzy rules. This is needed to obtain the net conditional possibility from the probabilistic fuzzy rules where the probabilistic information of the outputs is given. A novel approach is devised to compute net conditional possibility from the given probabilistic rules. The basis for extending the existing fuzzy models is also presented using the computed net conditional possibility. The enhanced fuzzy models accruing from the addition of the probabilistic information would usher in better decision making. The proposed approach is demonstrated through two case-studies.

Paper Nr: 9
Title:

RCA METHOD FOR FAULT DIAGNOSIS IN DIGITAL SUBSTATIONS OF POWER SYSTEMS

Authors:

Piao Peng, Zhiwei Liao, Fushuan Wen and Jiansheng Huang

Abstract: The authors present a Cause-Effect fault diagnosis model, which utilises the Root Cause Analysis approach and takes into account the technical features of a digital substation. The Dempster/Shafer evidence theory is used to integrate different types of fault information in the diagnosis model so as to implement a hierarchical, systematic and comprehensive diagnosis based on the logic relationship between the parent and child nodes such as transformer/circuit-breaker/transmission-line, and between the root and child causes. A real fault scenario is investigated in the case study to demonstrate the developed approach in diagnosing malfunction of protective relays and/or circuit breakers, miss or false alarms, and other commonly encountered faults at a modern digital substation.

Paper Nr: 18
Title:

CONSTRUCTING FUZZY PARTITIONS FROM IMPRECISE DATA

Authors:

José M. Cadenas, M. Carmen Garrido and Raquel Martínez

Abstract: Classification is an important task in Data Mining. In order to carry out classification, many classifiers require a previous preparatory step for their data. In this paper we focus on the process of discretization of attributes because this process is a very important part in Data Mining. In many situations, the values of the attributes present imprecision because imperfect information inevitably appears in real situations for a variety of reasons. Although, many efforts have been made to incorporate imperfect data into classification techniques, there are still many limitations as to the type of data, uncertainty and imprecision that can be handled. Therefore, in this paper we propose an algorithm to construct fuzzy partitions from imprecise information and we evaluate them in a Fuzzy Random Forest ensemble which is able to work with imprecise information too. Also, we compare our proposal with results of other works.

Paper Nr: 31
Title:

MEASURING THE CAPABILITY OF LIVING A HEALTHY LIFE WITH FUZZY LOGIC IN A GENDER PERSPECTIVE

Authors:

Tindara Addabbo, Gisella Facchinetti and Tommaso Pirotti

Abstract: The capability of living a healthy life may be considered a key dimension in the construction of individual well-being. It is itself the outcome of a complex set of indicators also including subjective indicators. This paper measures health at an individual level by using fuzzy logic to maintain the complexity of its definition while providing a crisp indicator of the level of health which may be disaggregated in relevant intermediate variables. The system has been implemented on the basis of the Italian National Statistical Institute (ISTAT) survey on health conditions, the results of which show a higher level of gender inequality in health than ,may be obtained by the traditional techniques used to measure health. We do find that when controlling for age, women are still characterized by poorer health conditions. Data disaggregated by regional area show a degree of variability in the outcome which may be connected to the varying policies implemented in different regions of Italy.

Paper Nr: 38
Title:

ON THE ABSOLUTE VALUE OF TRAPEZOIDAL FUZZY NUMBERS AND THE MANHATTAN DISTANCE OF FUZZY VECTORS

Authors:

Julio Rojas-Mora, Jaime Gil-Lafuente and Didier Josselin

Abstract: The computation of the Manhattan distance for fuzzy vectors composed of trapezoidal fuzzy numbers (TrFN) requires the application of the absolute value to the differences between components. The membership function of the absolute value of a fuzzy number has been defined by Dubois and Prade as well as by Chen and Wang. The first one only removes the negative values of the fuzzy number, increasing its expected value. Conversely, Chen and Wang’s definition maintains the expected value, but can produce a TrFN with negative values. In this paper, we present the “positive correction” of the absolute value, a method to remove the negative values of a TrFN while maintaining its expected value. This operation also complies with a logic principle of any uncertain distance: reducing the distance should also reduce its uncertainty.

Paper Nr: 40
Title:

ON THE EXTENSION OF THE MEDIAN CENTER AND THE MIN-MAX CENTER TO FUZZY DEMAND POINTS

Authors:

Julio Rojas-Mora, Didier Josselin and Marc Ciligot-Travain

Abstract: A common research topic has been the search of an optimal center, according to some objective function that considers the distance between the potential solutions and a given set of points. For crisp data, closed form expressions obtained are the median center, for the Manhattan distance, and the min-max center, for the Chebyshev distance. In this paper, we prove that these closed form expressions can be extended to fuzzy sets by modeling data points with fuzzy numbers, obtaining centers that, through their membership function, model the “appropriateness” of the final location.

Paper Nr: 53
Title:

A STEPWISE PROCEDURE TO SELECT VARIABLES IN A FUZZY LEAST SQUARE REGRESSION MODEL

Authors:

Francesco Campobasso and Annarita Fanizzi

Abstract: Fuzzy regression techniques can be used to fit fuzzy data into a regression model. Diamond treated the case of a simple model introducing a metrics into the space of triangular fuzzy numbers. In previous works we provided some theoretical results about the estimates of a multiple regression model with a non-fuzzy intercept; in this paper we show how the sum of squares of the dependent variable can be decomposed in exactly the same way as the classical OLS estimation procedure only when the intercept is fuzzy asymmetric. Such a decomposition allows us to introduce a stepwise procedure which simplifies, in terms of computational, the identification of the most significant independent variables in the model.

Short Papers
Paper Nr: 25
Title:

A FUZZY LOGIC APPROACH USED IN THE INVERSE KINEMATIC ALGORITHM OF A SPACE ZERO-G FREE FLYING ROBOT

Authors:

Andrea Bulgarelli, Alessio Aboudan, Carlo Menon, Massimo Trifoglio and Fulvio Gianotti

Abstract: A fuzzy algorithm for the fixed attitude restricted motion problem of free-flying robots is proposed in this paper. One of the main applications is to guide the robotic arm of a space servicing satellite: in such a mission, one of the priorities is to reduce disturbances on the satellite attitude induced by robotic arm movements so as not to perturb the pointing position of the satellite. A robot whose base has both mass and inertia of the same order of magnitude of its robotic arm is considered - in this configuration the disturbance of the satellite attitude is not negligible. Objective is to plan the robot’s arm motion in such a way as the end-effector tracks a desired trajectory while disturbances on the base’s attitude are minimized. This objective is achieved by taking into account the coupling between the arm and the floating base of the robot in the kinematic inversion of the guiding control, controlling the gain matrix of the subtask introduced in the kinematic inversion equation by means of a fuzzy algorithm. The proposed strategy combines the advantages of the inverse kinematic algorithm and a fuzzy logic approach.

Paper Nr: 26
Title:

SPATIAL-BASED FUZZY CLASSIFICATION OF LAND SUITABILITY INDEX FOR AGRICULTURE DEVELOPMENT - A Model Validation Perspective

Authors:

Sumbangan Baja, Andi Ramlan and Muhammad Ramli

Abstract: The primary aim of this research is to develop and test fuzzy modeling procedures to assess spatial distribution of actual corn yields in the field in relation to land characteristics. This experiment implements a fuzzy set methodology to generate a land suitability index (LSI) for corn development. It also uses a direct yield record method in the fields, and utilizes geographic information systems (GIS) in spatial analysis, in synchrony with global positioning system (GPS). This study produced a set of spatial information on LSI on a cell-by-cell basis in the study area. A simple regression method was also employed to calculate spatial correlation between two sets of information (i.e., corn yield in kg/ha and fuzzy set-based LSI). Although the correlation coefficient (R2) is relatively low, the scatter points have shown a good indication that the higher the LSI the better yield can be produced in the area under consideration. Spatial interpolation was then undertaken to map predicted corn yields on a regional basis. Spatial segmentation of land area in form of a fuzzy-based land suitability index map can assist land managers or decision makers in allocating future corn cultivation area in the study region.

Paper Nr: 30
Title:

A FUZZY SCHEME FOR IMAGE NOISE REDUCTION

Authors:

Philippe Vautrot, Michel Herbin and Laurent Hussenet

Abstract: The improvement of acquisition devices increases the need for processing of multicomponent images. In this context, the noise reduction is a preliminary preprocessing step affecting the results of the other image operations. This paper proposes a framework explaining usual noise reduction methods by the means of two fuzzy logic techniques: first a pixel fuzzification and second a defuzzification for estimating the filtered values. A new density-based filter is built for removing both impulse noise and Gaussian noise. The filter we propose is robust against outliers and it improves the classical bilateral approach for noise reduction of multicomponent images.

Paper Nr: 43
Title:

SOLVING FUZZY LINEAR SYSTEMS IN THE STOCHASTIC ARITHMETIC BY APPLYING CADNA LIBRARY

Authors:

Mohammad Ali Fariborzi Araghi and Hassan Fattahi

Abstract: In this paper, a fuzzy linear system with crisp coefficient matrix is considered in order to solve in the stochastic arithmetic. The fuzzy CESTAC method is applied in order to validate the computed results. The Gauss-Seidel and Jacobi iterative methods are used for solving a given fuzzy linear system. In order to implement the proposed algorithm, the CADNA library is applied to find the optimal number of iterations. Finally, two numerical examples are solved based on the given algorithm in the stochastic arithmetic.

Paper Nr: 47
Title:

A FUZZY LOGIC MODEL FOR NETWORK SIGNAL CONTROL AND TRANSIT PREEMPTION

Authors:

Yaser E. Hawas

Abstract: The majority of the fuzzy controllers for traffic signal control in the literature operate using raw data from single point detectors installed on the intersection’s various approaches. The input variables to the fuzzy logic controllers are usually simple estimates of traffic measures such as flow, speed or occupancy, estimated from such single detector readings. A room for improvement is sought herein by developing a fuzzy logic model (FLM) that could be integrated with smarter “processing” tools to estimate several traffic measures from multiple detectors on each approach. The estimates obtained from this processing tool are integrated as input knowledge into the FLM. The devised FLM structure is presented. A mesoscopic simulation model is devised to test the effectiveness of the FLM. The premise of the presented FLM is that it accounts for the network congestion downstream the individual traffic signals. This makes the FLM applicable for network rather than isolated type of signal control. Furthermore, the FLM accounts for transit pre-emption control as warranted. Several simulation-based experiments are presented including the basic FLM for isolated signal control, the FLM control enabling downstream congestion effect, and the one enabling transit pre-emption. The results are presented and discussed in details.

Posters
Paper Nr: 8
Title:

A HYBRID APPROACH TO LOCALLY OPTIMIZED INTERPRETABLE PARTITIONS OF FUZZY NEURAL MODELS

Authors:

Wanqing Zhao, Kang Li, George W. Irwin and Qun Niu

Abstract: Many learning methods have been proposed for Takagi-Sugeno-Kang fuzzy neural modelling. However, despite achieving good global performance, the local models obtained often exhibit eccentric behaviour which is hard to interpret. The problem here is to find a set of input space partitions and, hence, to identify the corresponding local models which can be easily understood in terms of system behaviour. A new hybrid approach for the construction of a locally optimized, functional-link-based fuzzy neural model is proposed in this paper. Unlike the usual linear polynomial models used for the rule consequent, the functional link artificial neural network (FLANN) is employed here to achieve a nonlinear mapping from the original model input space. Our hybrid learning method employs a modified differential evolution method to give the best fuzzy partitions along with the weighted fast recursive algorithm for the identification of each local FLANN. Results from a motorcycle crash dataset are included to illustrate the interpretability of the resultant model structure and the efficiency of the new learning technique.

Paper Nr: 12
Title:

APPROXIMATE REASONING IN CANCER SURGERY

Authors:

Elisabeth Rakus-Andersson

Abstract: The compositional rule of inference, grounded on the modus ponens law, is one of the most effective fuzzy systems. We modify the classical version of the rule (Zadeh, 1973, 1979) to propose an original model, which concerns determining an operation chance for gastric cancer patients. The operation prognosis will be dependent on values of biological markers indicating the progress of the disease.

Paper Nr: 21
Title:

A NEW METHOTOLOGY FOR ADAPTIVE FUZZY CONTROLLER. COMPARISON PERFORMANCE AGAINST SEVERAL CONTROL ALGORITHMS IN A REAL TIME CONTROL PROCESS

Authors:

Rafik Lasri, Ignacio Rojas, Héctor Pomares and Fernando Rojas

Abstract: This article presents a comparative study of various control algorithms. An adaptive fuzzy logic controller is set to prove its effectiveness against other conventional controllers in a simulated control process as well as in a real environment. Through a training board that allows us to control the temperature, we can compare the behavior of each used algorithm. The adaptive fuzzy logic controller will be required to present a real high performance in temperature control, having in mind that the adaptive algorithm starts with no rules set i.e., empty rule base or by assigning arbitrary values to the rules without any information off-line. The comparison of results clearly shows the great contribution that the policy of an adaptive algorithm brings; ease of implementation and high accuracy.

Paper Nr: 24
Title:

MULTILAYER SPLINE-BASED FUZZY NEURAL NETWORK WITH DETERMINISTIC INITIALIZATION

Authors:

Vitaliy Kolodyazhniy

Abstract: A multilayer spline-based fuzzy neural network (MS-FNN) is proposed. It is based on the concept of multilayer perceptron (MLP) with B-spline receptive field functions (Spline Net). In this paper, B-splines are considered in the framework of fuzzy set theory as membership functions such that the entire network can be represented in form of fuzzy rules. MS-FNN does not rely on tensor-product construction of basis functions. Instead, it is constructed as a multilayered superposition of univariate synaptic functions and thus avoids the curse of dimensionality similarly to MLP, yet with improved local properties. Additionally, a fully deterministic initialization procedure based on principal component analysis is proposed for MS-FNN, in contrast to the usual random initialization of multilayer networks. Excellent performance of MS-FNN with one and two hidden layers, different activation functions, and B-splines of different orders is demonstrated for time series prediction and classification problems.

Paper Nr: 28
Title:

APPLYING FUZZY COMPARATORS ON DATA MINING

Authors:

Angélica Urrutia, José Galindo, Leonid Tineo, José Morales and Claudio Gutiérrez

Abstract: This paper presents a fuzzy comparators module for Data Mining. This module allows querying data obtained by the application of existing data mining algorithms in SQL Server 2008. It provides the end user of a tool that gives useful information and knowledge about variables that have direct impact on the analysis of management indicators. The main contributions of this work are: first analysis and implementation of algorithms relaxed using fuzzy comparators, second deployment in a case to analyze the results.

Paper Nr: 29
Title:

FUZZY CONNECTEDNESS IN SEGMENTATION OF MEDICAL IMAGES - A Look at the Pros and Cons

Authors:

Pawel Badura, Jacek Kawa, Joanna Czajkowska, Marcin Rudzki and Ewa Pietka

Abstract: An attempt to recapitulate and conclude numerous experiences with the fuzzy connectedness theory applied to medical image segmentation is made in this paper. The fuzzy connectedness principles introduced in 1996 have been developed and tested in dozens of studies in past 15 years; many advantages, as well as shortcomings have been discovered and described. Some aspects of the method and its applications have been summarized here, including the examples of specific 2D and 3D medical studies with various objects, subjected to fuzzy connected segmentation. Deliberation about the usefulness of multiseeded and multiobject variants is also present. An algorithm optimized for matrix computations-based programming languages is introduced. Finally, 3 fuzzy connectedness-based computer aided diagnosis systems are described and evaluated.

Paper Nr: 32
Title:

FEATURE SELECTION BASED ON IMPORTANCE AND INTERACTION INDEXES - Hierarchical Fuzzy Rule Classifier Application

Authors:

Vincent Bombardier, Laurent Wendling and Emmanuel Schmitt

Abstract: This paper proposed an extension of an iterative method to select suitable features for pattern recognition context. The main improvement is to replace its iterative step with another criterion based on importance and interaction indexes, providing suitable feature reduced set. This new scheme is embedded on a hierarchical fuzzy rule classification system. At last, each node gathers a set of classes having a similar aspect. The aim of the proposed method is to automatically extract an efficient subset of suitable features for each node. A selection of features is given. The associated criterion is directly based on importance index and assessment of positive and negative interaction between features. An experimental study, made in a wood defect recognition industrial context, shows the proposed method is efficient to producing significantly fewer rules.

Paper Nr: 33
Title:

SATISFIABILITY DEGREE THEORY FOR TEMPORAL LOGIC

Authors:

Jian Luo, Guiming Luo and Mo Xia

Abstract: The truth value of propositional logic is not capable of representing the real word full of complexity and diversity. The requirements of the proposition satisfiability are reviewed in this paper. Every state is labeled with a vector, which is defined by the proposition satisfiability degree. The satisfiability degree for temporal logic is proposed based on the vector of satisfiability degree. It is used to interpret the truth degree of the temporal logic instead of true or false. A sound and precise reasoning system for temporal logic is established and the computation is given. One example of a leadership election is included to show that uncertain information can be quantized by the satisfiability degree.

Paper Nr: 35
Title:

AN ALGORITHM FOR SATISFIABILITY DEGREE COMPUTATION

Authors:

Jian Luo, Guiming Luo and Mo Xia

Abstract: Satisfiability degree describes the satisfiable extent of a proposition based on the truth table by finding out the proportion of interpretations that make the proposition true. This paper considers an algorithm for computing satisfiability degree. The proposed algorithm divides a large formula into two smaller formulas that can be further simplified by using unit clauses; once the smaller formulas contains only a clause or unit clauses, their satisfiability degrees can be directly computed. The satisfiability degree of the large formula is the difference of the two smaller ones. The correctness of the algorithm is proved and it has lower time complexity and space complexity than all the existing algorithms, such as the enumeration algorithm, the backtracking algorithm, the propositional matrix algorithm and so on. That conclusion is further verified by experimental results.

Paper Nr: 39
Title:

MODELLING A FUZZY SYSTEM FOR TEACHERS’ TRAINING DESIGN

Authors:

Eliza Stefanova and Svetla Boytcheva

Abstract: This paper presents a model, based on fuzzy logic, aiming to support teachers’ training design. The complexity of the task of technology utilisation in education, leads the authors to decision to base its adaptive system on fuzzy controller. We shortly describe the system architecture and its functionality. The presentation includes also fuzzy model implemented in the kernel of the system, its components, linguistic variables and values. Further steps for improvement of the system performance are sketched as well.

Paper Nr: 42
Title:

FUZZY CLASSIFIER BASED ON SUPERVISED CLUSTERING WITH NONPARAMETRIC ESTIMATION OF LOCAL PROBABILISTIC DENSITIES IN DEFAULT PREDICTION OF SMALL ENTERPRISES

Authors:

Maria Luiza F. Velloso, Nival N. Almeida, Thales Ávila Carneiro and José Augusto Gonçalves do Canto

Abstract: The accuracy-complexity trade-off has been an important issue in system modeling. Parsimonious modelling is preferred to complex modelling and, of course, accurate modelling is preferred to inaccurate modelling. In system modelling with fuzzy rule-based systems, the accuracy-complexity tradeoff is often referred as the interpretability-accuracy trade-off, and high interpretability is the main advantage of fuzzy rule-based systems over other nonlinear systems. In many applications, gaining knowledge about the system, in an understandable way, is as important as getting accurate results. The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper we use a fuzzy model structure where each rule represents more than one class with different probabilities. The rules are extracted through clustering and the probabilities are estimated in a local (cluster by cluster) non-parametric way. This approach is applied to predict default in small and medium enterprises in Brazil, using indexes that reflect the financial situation of enterprise, such as profitable capability, operating efficiency, repayment capability and situation of enterprise’s cash flow. The preliminary results show a significant improvement in the interpretability, without accuracy loss, compared with other approaches.

Paper Nr: 51
Title:

HYBRID ALGORITHM FOR FUZZY MODEL PARAMETER ESTIMATION BASED ON GENETIC ALGORITHM AND DERIVATIVE BASED METHODS

Authors:

A. Lavygina and I. Hodashinsky

Abstract: Hybrid method for estimation of fuzzy model parameters is presented. The main idea of the method is to apply gradient descent method or Kalman filter as a mutation operator of genetic algorithm for estimation of antecedent parameters of fuzzy “IF-THEN” rules. Thus, part of the individuals in the population mutate by means of gradient descent method or Kalman filter, the others mutate in an ordinary way. Once antecedents are tuned, consequents tuning is performed with the least squares method. The results of computer experiment are presented.

Paper Nr: 52
Title:

FUZZY LOGIC BASED QUALITY OF SERVICE MODELS

Authors:

João Antunes, André Vasconcelos and José Tribolet

Abstract: The continuous monitoring of information systems’ quality of service increases importance as business becomes more and more dependent of those systems. In order to obtain that view, quality models need to be defined for those systems. Because of its complexity and today modelling frameworks, quality models tend to result in a poor representation of reality, mainly because of their lack of ability to represent uncertainty. In this work, we investigate the use of fuzzy logic’s properties to create a new kind of quality of service models, which handles uncertainty and imprecision naturally. The objective is to obtain models that are a better representation of reality and easier to create and understand. This article presents the investigation on related topics to support the identified problem and motivations, followed by a solution proposal and a validation scenario.