ICFC 2009 Abstracts


Full Papers
Paper Nr: 9
Title:

REVERSE ENGINEERING AND SYMBOLIC KNOWLEDGE EXTRACTION ON ŁUKASIEWICZ LOGICS USING NEURAL NETWORKS

Authors:

Carlos Leandro

Abstract: This work describes a methodology that combines logic-based systems and connectionist systems. Our approach uses finite truth-valued Łukasiewicz logic, where we take advantage of fact, presented in (Castro and Trillas, 1998), wherein every connective can be defined by a neuron in an artificial network having, by activation function, the identity truncated to zero and one. This allowed the injection of formulas into a network architecture, and also simplified symbolic rule extraction. Neural networks are trained using the Levenderg-Marquardt algorithm, where we restricted the knowledge dissemination in the network structure, and the generated network is simplified applying the ”Optimal Brain Surgeon” algorithm proposed by B. Hassibi, D. G. Stork and G.J. Wolf. This procedure reduces neural network plasticity without drastically damaging the learning performance, thus making the descriptive power of produced neural networks similar to the descriptive power of Łukasiewicz logic language and simplifying the translation between symbolic and connectionist structures. We used this method in the reverse engineering problem of finding the formula used on the generation of a given truth table. For real data sets the method is particularly useful for attribute selection, on binary classification problems defined using nominal attributes, where each instance has a level of uncertainty associated with it.

Paper Nr: 17
Title:

FUZZY TOPOGRAPHIC MODELING IN WIRELESS SIGNAL TRACKING ANALYSIS

Authors:

Eddie C. L. Chan, George Baciu and S. C. Mak

Abstract: Fuzzy logic modelling can be applied to evaluate the behaviour of Wireless Local Area Networks (WLAN) received signal strength (RSS). The behavior study of WLAN signal strength is a pivotal part of WLAN tracking analysis. Previous analytical model has not been addressed effectively for analyzing how the WLAN infrastructure affected the accuracy of tracking. In this paper, we propose a novel fuzzy spatio-temporal topographic model. We implemented the proposed model with a large (9.34 hectare), built-up university, over 2,000 access points to survey and collect WLAN received signal strength (RSS). We applied the Nelder-Mead (NM) method to simplify our previous work on fuzzy color map into a topographic (line-based) map. The new model can provide a detail and quantitative strong representation of WLAN RSS. Finally, it serves as a quicker reference and efficient analysis tool for improving the design of WLAN infrastructure.

Paper Nr: 25
Title:

FUZZY MUTUAL INFORMATION FOR REVERSE ENGINEERING OF GENE REGULATORY NETWORKS

Authors:

Silvana Badaloni, Marco Falda, Paolo Massignan and Francesco Sambo

Abstract: The aim of this work is to provide a new definition of Mutual Information using concepts from Fuzzy Sets theory. With this approach, we extended the model on which the well-known REVEAL algorithm for Reverse Engineering of gene regulatory networks is based and we designed a new flexible version of it, called FuzzyReveal. The predictive power of our new version of the algorithm is promising, being comparable with a state-of-the-art algorithm on a set of simulated problems.

Paper Nr: 26
Title:

FUZZY WEIGHTED AVERAGE - Analytical Solution

Authors:

Pim van den Broek and Joost Noppen

Abstract: An algorithm is presented for the computation of analytical expressions for the extremal values of the α-cuts of the fuzzy weighted average, for triangular or trapeizoidal weights and attributes. Also, an algorithm for the computation of the inverses of these expressions is given, providing exact membership functions of the fuzzy weighted average. Up to now, only algorithms exist for the computation of the extremal values of the α-cuts for a fixed value of α. To illustrate the power of our algorithms, they are applied to several examples from the literature, providing exact membership functions in each case.

Short Papers
Paper Nr: 4
Title:

LMI APPROACH FOR AIR-MANAGEMENT IN DIESEL ENGINES USING PDC FUZZY CONTROLLERS

Authors:

S. García-Nieto, J. Salcedo, J. M. Herrero and C. Ramos

Abstract: Air management control in a turbocharged diesel engine presents itself as a challenge due to its nonlinear behavior, then classic control techniques are unable to provide the required performance. Hence, it is proposed to design fuzzy controllers based on PDC structure (Parallel Distributed Compensation) using a previously obtained Takagi-Sugeno fuzzy model for the engine. Controller parameters are obtained from a minimization problem subject to LMIs (Linear Matrix Inequalities).

Paper Nr: 7
Title:

TEMPORAL MINING IN IMPRECISE ARCHÆOLOGICAL KNOWLEDGE

Authors:

Cyril de Runz and Eric Desjardin

Abstract: In this paper, we propose a new temporal data mining method considering a set of arch ae ological objects which are temporally represented with fuzzy numbers. Our method uses an index which quantifies the anteriority between two fuzzy numbers for the construction of a weighted oriented graph. The vertices of the graph correspond to the temporal objects. Using this anteriority graph, we estimate the potential of anteriority, of posteriority and the relative temporal position of each object. We focus on excavation data from the ancient Reims stored in a Geographical Information System (GIS). We visualize the discovered temporal positions of objects and weighted relations between them in a layer of the GIS.

Paper Nr: 15
Title:

FUZZY SET THEORY BASED STUDENT EVALUATION

Authors:

Zsolt Csaba Johanyák

Abstract: The evaluation of students’ learning achievements contains in several cases a lot of decisions that are based on the expertise and the opinion of the evaluator. Often this opinion is from nature vague and therefore this field is a good application area for fuzzy set theory based supporting methods and software implementations. In this paper, a new method called FUSBE (Fuzzy Set Theory Based Evaluation) is presented. It supports the scoring and grading of the students allowing the evaluator to express his or her judgment by the means of fuzzy sets that are later aggregated using fuzzy arithmetic. The method is transparent and easy-to-implement.

Paper Nr: 18
Title:

ON CONGRUENCES AND HOMOMORPHISMS ON SOME NON-DETERMINISTIC ALGEBRAS

Authors:

I. P. Cabrera, P. Cordero, G. Gutiérrez, J. Martínez and M. Ojeda-Aciego

Abstract: Starting with the underlying motivation of developing a general theory of L-fuzzy sets where L is a multilattice (a particular case of non-deterministic algebra), we study the relationship between the crisp notions of congruence, homomorphism and substructure on some non-deterministic algebras which have been used in the literature, i.e. hypergroups, and join spaces. Moreover, we provide suitable extensions of these notions to the fuzzy case.

Posters
Paper Nr: 3
Title:

ON TWO INTERPRETATIONS OF COMPETITIVE CONDITIONAL FUZZY PREFERENCES

Authors:

Patrick Bosc and Olivier Pivert

Abstract: This paper introduces a new type of database queries involving fuzzy preferences. The idea is to consider competitive conditional preference clauses structured as a tree, where each children's set of a node corresponds to a disjunction of non mutually exclusive fuzzy predicates (thus the notion of competition). The paper defines two possible interpretations of such queries and outlines two evaluation techniques which follow from them.

Paper Nr: 28
Title:

SOME RESULTS ON A MULTIVARIATE GENERALIZATION OF THE FUZZY LEAST SQUARE REGRESSION

Authors:

Francesco Campobasso, Annarita Fanizzi and Marina Tarantini

Abstract: Fuzzy regression techniques can be used to fit fuzzy data into a regression model, where the deviations between the dependent variable and the model are connected with the uncertain nature either of the variables or of their coefficients. P.M. Diamond (1988) treated the case of a simple fuzzy regression of an uncertain dependent variable on a single uncertain independent variable, introducing a metrics into the space of triangular fuzzy numbers. In this work we managed more than a single independent variable, determining the corresponding estimates and providing some theoretical results about the decomposition of the sum of squares of the dependent variable according to Diamond’s metric, in order to identify its components.

Paper Nr: 29
Title:

AN INVESTIGATION INTO THE DISTRIBUTION OF MEMBERSHIP GRADES FOR NON-STATIONARY FUZZY SETS

Authors:

Pragnesh A. Gajjar and Jonathan Garibaldi

Abstract: In this paper we study some properties related to the distribution of membership grades for non-stationary fuzzy sets. We obtain the formulation for the distribution, where the non-stationary fuzzy sets are obtained by generating instantiations about the center values. The two cases considered are for Triangular and Gaussian underlying membership functions. The analytical results obtained are then compared with computer generated results, for completeness.

Paper Nr: 32
Title:

EASY FUZZY TOOL FOR EMOTION RECOGNITION - Prototype from Voice Speech Analysis

Authors:

Mahfuza Farooque and Susana Munoz Hernández

Abstract: In human beings relations it is very important dealing with emotions. Most people is able to deduce the emotion of one person just listening his/her speech. Voice speech characteristics can help us to identify people emotions. Emotion recognition is a very interesting field in modern science and technology but to automate it is not an easy task. Many researchers and engineers are working to recognize this prospective field but the difficulty is that emotions are not clear. They are not a crisp topic. In this paper we propose to use fuzzy reasoning for emotion recognition. We based our work in some previous studies about the specific characteristics of voice speech for each human emotion (speech rate, pitch average, intensity and voice quality). We provide a simple an useful prototype that implements emotion recognition using a fuzzy model. We have used RFuzzy (a fuzzy logic reasoner over a Prolog compiler) and we have obtained a simple and efficient prototype that is able to identify the emotion of a person from his/her voice speech characteristics. We are trying to recognize sadness, happiness, anger, excitement and plain emotion. We have made some experiments and we provide the results that are 90% successful in the identification of emotions. Our tool is constructive, so it can be used not only to identify emotions automatically but also to recognize the people that have an emotion through their different speeches. Our prototype analyzes an emotional speech and obtains the percentage of each emotion that is detected. So it can provide many constructive answers according to our queries demand. Our prototype is an easy tool for emotion recognition that can be modify and improved by adding new rules from speech and face analysis.

Paper Nr: 37
Title:

IMPLEMENTATION OF A FUZZY LOGIC SYSTEM ON A FPGA FOR A SERVO CONTROLLER

Authors:

Arturo Téllez V., Luis Villa V., Herón Molina L. and Oscar Camacho N.

Abstract: In this paper we propose a digital fuzzy logic system implemented on a field programmable gate array (FPGA) in order to control a servo controller. The fuzzy logic controller (FLC) is designed as a combinational circuit and does not depend on a clock signal. So the advantage is that the fuzzy system is enough fast to control a servo controller. For the implementation of the membership functions (MF) we propose to use dynamic MF, i.e. the parameters that define the each MF are adapted on line. Also, for the design of fuzzy system a new methodology was developed so the design and implementation of the fuzzy system is easy to do. The fuzzy system was programmed in MatlLab and was proved that the fuzzy system is capable to control a servo motor. Finally the performance of the fuzzy system was proved directly on the FPGA.

Paper Nr: 38
Title:

C-FUZZY DECISION TREES IN DEFAULT PREDICTION OF SMALL ENTERPRISES

Authors:

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

Abstract: This work uses fuzzy c-tree in order 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, etc. Fuzzy c-trees are based on information granules—multivariable entities characterized by high homogeneity (low variability). The results are compared with those produced by the “standard” version of the decision tree, the C4.5 tree. The experimental study illustrates a better performance of the C-tree.