ICFC 2010 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 3
Title:

OFP_CLASS: AN ALGORITHM TO GENERATE OPTIMIZED FUZZY PARTITIONS TO CLASSIFICATION
Authors:

José M. Cadenas, M. del Carmen Garrido, Raquel Martínez and Enrique Muñoz
Abstract: The discretization of values is a important role in data mining and knowledge discovery. The representation of information through intervals is more concise and easier to understand at certain levels of knowledge than the representation by mean continuous values. In this paper, we propose a method for discretizing continuous attributes by means a series of fuzzy sets which constitute a fuzzy partition of this attribute’s domain. We present an algorithm, which carries out a fuzzy discretization of continuous attributes in two stages. In the first stage a fuzzy decision tree is used and the genetic algorithm is used in the second stage. In this second stage the cardinality of the partition is defined. After defining the fuzzy partitions these are evaluated by a fuzzy decision tree which is also detailed in this study.
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Paper Nr: 5
Title:

DEVELOPMENT OF A FUZZY CALCULATOR FOR CONTINUOUS FUNCTIONS OF NON-INTERACTIVE FUZZY VARIABLES
Authors:

Karolien Scheerlinck, Hilde Vernieuwe and Bernard De Baets
Abstract: The goal of this paper is to develop a Fuzzy Calculator, making it possible to calculate functions of fuzzy intervals, as prescribed by the extension principle of Zadeh. The extension principle can be reversed, resulting in fixed a-levels for which the minimum and the maximum of the function has to be determined. This optimization problem can be tackled by different algorithms: Gradient Descent, SIMPSA, Particle Swarm Optimization and Particle Swarm optimization in combination with Gradient Descent. Two approaches are used to determine the number of a-levels: it is either fixed to a predetermined value, or it is initially chosen very small and subsequently expanded according to a suitable criterion. Both a non-parallel and a parallel implementation of the Fuzzy Calculator are designed. In the parallel version, communication is used to optimize the internal workings of PSO. The Fuzzy Calculator is applied to a number of test functions. The different combinations of optimization algorithms are evaluated, both by the final result and by the number of required model evaluations. The results indicate that the parallel implementation of the Fuzzy Calculator starting with a small number of a-levels and using PSO with Gradient Descent leads to the most accurate membership function.
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Paper Nr: 7
Title:

REASONING WITH THE FUZZY DESCRIPTION LOGIC fZSI
Authors:

Jidi Zhao, Harold Boley and Weichang Du
Abstract: While applications in different areas have shown the necessity of dealing with uncertain knowledge, Semantic Web techniques based on standard Description Logics do not have such a capability. Motivated by this discrepancy, we introduce an expressive fuzzy description logic, fZSI , which extends the classic Description Logic SI to deal with uncertain knowledge about concepts and roles as well as instances of concepts and roles. In the family of Fuzzy Logics it is semantically based on Zadeh Logic, which naturally interprets uncertain knowledge about concepts and roles as fuzzy sets and fuzzy relations, and interprets uncertain knowledge about instances as elements with degrees of membership. The paper focuses on several reasoning methods for the main reasoning problems in fZSI, including consistency checking, instance range entailment, and f-retrieval problems.
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Paper Nr: 23
Title:

A DPLL PROCEDURE FOR THE PROPOSITIONAL GÖDEL LOGIC
Authors:

Dušan Guller
Abstract: In the paper, we investigate the satisfiability and validity problems of a formula in the propositional Gödel logic. Our approach is based on the translation of a formula to an equivalent CNF one which contains literals of the augmented form: either a or a→b or (a→b)→b, where a, b are propositional atoms or the propositional constants 0, 1. A CNF formula is further translated to an equisatisfiable finite order clausal theory which consists of order clauses, finite sets of order literals of the forms a ≖ b or a ≺ b. ≖ and ≺ are interpreted by the equality and strict linear order on [0,1], respectively. A variant of the DPLL procedure for deciding the satisfiability of a finite order clausal theory is proposed. The DPLL procedure is proved to be refutation sound and complete. Finally, we reduce the validity problem of a formula (tautology checking) to the unsatisfiability of a finite order clausal theory.
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Paper Nr: 27
Title:

TOWARDS FUZZY GRANULARITY CONTROL IN PARALLEL/DISTRIBUTED COMPUTING
Authors:

T. Trigo de la Vega, P. Lopez-García and S. Muñoz-Hernandez
Abstract: Automatic parallelization has become a mainstream research topic for different reasons. For example, multicore architectures, which are now present even in laptops, have awakened an interest in software tools that can exploit the computing power of parallel processors. Distributed and (multi)agent systems also benefit from techniques and tools for deciding in which locations should processes be run to make a better use of the available resources. Any decision on whether to execute some processes in parallel or sequentially must ensure correctness (i.e., the parallel execution obtains the same results as the sequential), but also has to take into account a number of practical overheads, such as those associated with tasks creation, possible migration of tasks to remote processors, the associated communication overheads, etc. Due to these overheads and if the granularity of parallel tasks, i.e., the “work available” underneath them, is too small, it may happen that the costs are larger than the benefits in their parallel execution. Thus, the aim of granularity control is to change parallel execution to sequential execution or vice-versa based on some conditions related to grain size and overheads. In this work, we have applied fuzzy logic to automatic granularity control in parallel/distributed computing and proposed fuzzy conditions for deciding whether to execute some given tasks in parallel or sequentially. We have compared our proposed fuzzy conditions with existing (conservative) sufficient conditions and our experiments showed that the proposed fuzzy conditions result in more efficient executions on average than the conservative conditions.
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Paper Nr: 36
Title:

REPRESENTATION THEOREM FOR FUZZY FUNCTIONS - Graded Form
Authors:

Martina Daňková
Abstract: In this contribution, we will extend results relating to representability of a fuzzy function using a crisp function. And additionally, we show for which functions there exist fuzzy function of a specific form. Our notion of fuzzy function has a graded character. More precisely, any fuzzy relation has a property of being a fuzzy function that is expressed by a truth degree. And it consists of two natural properties: extensionality and functionality. We will also provide a separate study of these two properties.
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Short Papers
Paper Nr: 13
Title:

TRADINNOVA-FUZ: FUZZY PORTFOLIO INVESTMENT - Dynamic Stock Portfolio Decision-making Assistance Model based on a Fuzzy Inference System
Authors:

Isidoro J. Casanova
Abstract: This paper describes a decision system based on rules for the management of a stock portfolio using a fuzzy inference system to select the stocks to be incorporated. This system simulates the intelligent behavior of an investor, carrying out the buying and selling of stocks, such that during each day the best stocks will be selected to be incorporated in the portfolio with the use of technical indicators using a fuzzy logic based approach. The proposed novel fuzzy system only has a simple strict set of rules to decide if a share is bought or not, unlike other systems that also include rules for the sale and have a lot of complicated rules. The system has been tested in 3 time periods (1 year, 3 years and 5 years), simulating the purchase/sale of stocks in the Spanish continuous market and the results have been compared with the revaluations obtained by the best investment funds operating in Spain.
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Paper Nr: 15
Title:

FUZZY ANP - A Analytical Network Model for Result Merging for Metasearch using Fuzzy Linguistic Quantifiers
Authors:

Arijit De and Elizabeth Diaz
Abstract: Search Engines are tools for searching the World Wide Web or any other large data collection. Search engines typically accept a user query and returns a list of relevant documents. These documents are generally returned as a result list for the user to see. A metasearch engine is a tool that allows an information seeker to search information on the world wide web through multiple search engines. A key function of a metasearch engine is to aggregate search results returned by many search engines. Result aggregation is an important task for a metasearch engine. In this paper we propose a model for result aggregation for metasearch, Fuzzy ANP, that employs fuzzy linguistic quantifier guided approach to result merging using Saty's Analytical Network Process. We compare our model to two existing result merging models, the Borda Fuse model and the OWA model for metasearch. Our results show that our model outperforms the OWA model and Borda-Fuse model significantly.
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Paper Nr: 25
Title:

A COMPARATIVE STUDY TO DESIGN A CODE BOOK FOR VECTOR QUANTIZATION
Authors:

Yoshitaka Takeda, Eiki Noro, Junji Maeda and Yukinori Suzuki
Abstract: In this paper, we examined six algorithms to construct an optimal code book (CB) for vector quantization (VQ) experimentally. Four algorithms are GLA (generalized Lloyd algorithm), FCM (fuzzy c meams), GA (genetic algorithm), and AP (affinity propagation). The other two algorithms are hybrid methods: AP+GLA and GA+FCM. Performance of the algorithms was evaluated by both PSNR (peak-signal-to-noise-ratio) and NPIQM (normalized perceptual image quality measure) of decoded images. Computational experiments showed that the performance of each algorithm could be categorized as higher performance and lower performance. GLA, AP and AP+GLA belong to the higher performance group, while FCM, GA and GA+FCM belong to the lower performance group. AP+GLA shows the best performance of algorithms in the higher performance group. Thus, AP+GLA is an optimal algorithm for constructing a CB for VQ.
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Paper Nr: 29
Title:

USING FUZZY AND FRACTAL METHODS FOR ANALYZING MARKET TIME SERIES
Authors:

P. Kroha and M. Lauschke
Abstract: In this contribution, we investigate the possibilities of using fuzzy and fractal methods for analyzing time series of market data. First, we implemented and tested a fuzzy component that provides fuzzyfication by the Mamdani Larsen inference method with static rules using not only Gauss but also Cauchy and Mandelbrot distribution. Second, we implemented and tested a fractal component that provides fuzzy clustering by the Takagi Sugeno method with dynamic fuzzy rules. Looking for an optimum, we simulated many parameter combinations and compared the results. We present some interesting results of our experiments.
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Paper Nr: 33
Title:

BOUSI∼PROLOG - A Fuzzy Logic Programming Language for Modeling Vague Knowledge and Approximate Reasoning
Authors:

Pascual Julián Iranzo and Clemente Rubio Manzano
Abstract: Bousi∼Prolog is an extension of the standard Prolog language. Its operational semantics is an adaptation of the SLD resolution principle where classical unification has been replaced by a fuzzy unification algorithm based on fuzzy relations defined on a syntactic domain. In this paper we describe how Bousi∼Prolog may contribute to resolve several problems extracted from different application areas, where it is mandatory to deal with vagueness and uncertain knowledge, such as: flexible deductive databases, fuzzy control, fuzzy experts systems, data retrieval or approximate reasoning. Hence, through several (small but meaningful) examples we show the great potential of this programming language.
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Paper Nr: 37
Title:

AN INVESTIGATION OF THE EFFECT OF INPUT REPRESENTATION IN ANFIS MODELLING OF BREAST CANCER SURVIVAL
Authors:

Hazlina Hamdan and Jonathan M. Garibaldi
Abstract: Fuzzy inference systems have been applied in recent years in various medical fields due to their ability to obtain good results featuring white-box models. Adaptive Neuro-Fuzzy Inference System (ANFIS), which combines adaptive neural network capabilities with the fuzzy logic qualitative approach, has been previously used in modelling survival of breast cancer patients based on patient groups derived from the Nottingham Prognostic Index (NPI), as discussed in our previous paper. In this paper, we extend our previous work to examine whether the ANFIS model can be trained to better match the data with the NPI variable represented as a real number, rather than a categorical group. Two input models have been developed and trained with different structures of ANFIS. The performance of these models, in the capability to predict the survival rate in survival of patients following operative surgery for breast cancer, is examined.
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Paper Nr: 38
Title:

APPLICATION OF THE BANACH FIXED POINT THEOREM ON FUZZY QUASI-METRIC SPACES TO STUDY THE COST OF ALGORITHMS WITH TWO RECURRENCE EQUATIONS
Authors:

Francisco Castro-Company, Salvador Romaguera and Pedro Tirado
Abstract: Considering recursiveness as a unifying theory for algorithm related problems, we take advantage of algorithms formulation in terms of recurrence equations to show the existence and uniqueness of solution for the two recurrence equations associated to a kind of algorithms defined as two procedures depending the one on the other by applying the Banach contraction principle in a suitable product of fuzzy quasi-metrics on the domain of words.
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Paper Nr: 6
Title:

TUNING METHOD FOR PID CONTROL SCHEME - A Blended Ant System Optimization Technique with Takagi Sugeno Fuzzy System
Authors:

A. H. Besheer
Abstract: This paper addresses the ant colony system optimization method that is used for tuning the parameters of three independent fuzzy systems to optimally determined different gains of PID controller. Each fuzzy module is utilized to obtain different PID parameters. The objective of Ant Colony Optimization is to improve both the design efficiency of fuzzy systems and its performance. The optimum relationship between the PID controller gains and the parameters of fuzzy modules is explored using Ant system algorithm. Firstly, the design of typical Takagi-Seguno fuzzy PID controller is presented. Then, the well known ant colony optimization method is applied to the problem of tuning the parameters of Takagi-Seguno fuzzy rule base. Finally, the optimal PID gains are obtained. Simulation examples are provided to illustrate the effectiveness of the proposed technique.
Paper Nr: 9
Title:

FAULT DIAGNOSIS IN ROTATING MACHINERY USING FUZZY MEASURES AND FUZZY INTEGRALS

Authors:

Masahiro Tsunoyama, Kensuke Masumori, Hayato Hori, Hirokazu Jinno, Masayuki Ogawa and Tatsuo Sato
Abstract: In the fault diagnosis of rotating machinery using fuzzy measures and fuzzy integrals, the optimization of membership functions and identification of fuzzy measures are important for accurate diagnosis. Herein, a method for optimizing membership functions is proposed based on the statistical properties of vibration spectra and identifying fuzzy measures based on interaction levels using partial correlation coefficients between spectra. The possibility of a given fault is obtained from fuzzy integrals using membership degrees determined by the membership function, and the fuzzy measures for the set of spectra. The method is also evaluated using the example of diagnosis of misalignment and unbalance faults.
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Paper Nr: 12
Title:

A FUZZY LOGIC INFERENCE APPROACH FOR THE ESTIMATION OF THE PASSENGERS FLOW DEMAND
Authors:

Aránzazu Berbey Alvarez, Rony Caballero George, Juan de Dios Sanz Bobi and Ramón Galán López
Abstract: This paper presents a new approach that designs the flow of passengers in mass transportation systems in presence of uncertainties. One of the techniques used for the prediction of passenger demand is the origin-destination matrices. However, this method is limited to urban areas and rarely to explicit stations. Otherwise, the gravity models based on friction functions can be another alternative; however, it is difficult to fit into practical achievements. Another solution might be the application of artificial intelligence techniques so as to include some intuitive knowledge provided by an expert to predict the flow demand of passengers’ trips in explicit stations. This paper proposes to combine a matrix of origin-destination trips of travel zones, with the intuitive knowledge, applying a fuzzy logic inference approach.
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Paper Nr: 24
Title:

TOKEN TRANSFER STRUCTURES BETWEEN V-PATH AND U-CIRCUIT

Authors:

Gi-Bum Lee, Jeong-Heon Heo, Han Zandong and Jin S. Lee
Abstract: This paper presents the token transfer structures derived from the relation of a V-path and a U-circuit in Petri Nets. The V-path shows the one-way route of a token, whereas the U-circuit describes the circulation structure of a token. The token transfer structures are made by considering the token flow. The token return structure and the token passing structure show that tokens can be continuously transferred from place to place. In case the T-link and the C-link circuit don’t have a complete minimum token, the daisy chain structure is constructed with the partial minimum token in a U-circuit structure.
Paper Nr: 43
Title:

CONVENTIONAL AND BAYESIAN VALIDATION FOR FUZZY CLUSTERING ANALYSIS
Authors:

Olfa Limam and Fouad Ben Abdelaziz
Abstract: Clustering analysis has been used for identifying similar objects and discovering distribution of patterns in large data sets. While hard clustering assigns an object to only one cluster, fuzzy clustering assigns one object to multiple clusters at the same time based on their degrees of membership. An important issue in clustering analysis is the validation of fuzzy partitions. In this paper, we consider the Bayesian like validation along with four conventional validity measures for two clustering algorithms namely, fuzzy c-means and fuzzy c-shell based. An empirical study is conducted on five data sets to compare their performances. Results show that the Bayesian validation score outperforms the conventional ones. However, a multiple objective approach is needed.
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