FCTA 2015 Abstracts

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

A Modified Fuzzy Lee-Carter Method for Modeling Human Mortality
Authors:

Duygun Fatih Demirel and Melek Basak
Abstract: Human mortality modeling and forecasting are important study fields since mortality rates are essential in financial and social policy making. Among many others, Lee Carter (LC) model is one of the most popular stochastic method in mortality forecasting. Koissi and Shapiro fuzzified the standard LC model and eliminated the assumptions of homoscedasticity and the ambiguity on the size of the error term variances. In this study, a modified version of fuzzy LC model incorporating singular value decomposition (SVD) technique is proposed. Utilizing SVD instead of ordinary least squares in the fuzzy LC model allows the model to capture existing fluctuations in mortality rates and yields a better fit. The proposed method is applied to Finland mortality data for years 1925 to 2009. The results are compared with Koissi and Shapiro’s fuzzy LC method and the standard LC method. Numerical findings show that proposed method gives statistically better results in generating small spreads and in estimating mortality rates when compared with Koissi and Shapiro’s method.
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Paper Nr: 4
Title:

Synchronization of Uncertain Chaotic Systems using Generalized Predictive Control based on Fuzzy PID Controllers
Authors:

Zakaria Driss and Noura Mansouri
Abstract: In this paper, we investigate the synchronization of chaotic systems with unknown parameters using generalized predictive control based on fuzzy PID controllers. In order to verify the efficiency of the proposed method, fuzzy PD+I and fuzzy PI+D controllers are successively used with and without prediction terms for the synchronization of two uncertain Lorenz systems. For fuzzy PD+I controller, the prediction terms seem to be efficient for the synchronization. However, with the fuzzy PI+D controller, they make a noise and worsen the performance of the controller
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Paper Nr: 13
Title:

An Order Hyperresolution Calculus for Gödel Logic with Truth Constants and Equality, Strict Order, Delta
Authors:

Dušan Guller
Abstract: In (Guller, 2014), we have generalised the well-known hyperresolution principle to the first-order Godel logic ¨ with truth constants. This paper is a continuation of our work. We propose a hyperresolution calculus suitable for automated deduction in a useful expansion of Godel logic by intermediate truth constants and the equality, ¨ P, strict order, ≺, projection, ∆, operators. We solve the deduction problem of a formula from a countable theory in this expansion. We expand Godel logic by a countable set of intermediate truth constants ¯ ¨ c, c ∈ (0,1). Our approach is based on translation of a formula to an equivalent satisfiable finite order clausal theory, consisting of order clauses. An order clause is a finite set of order literals of the form ε1  ε2 where εi is an atom or a quantified atom, and  is the connective P or ≺. P and ≺ are interpreted by the equality and standard strict linear order on [0,1], respectively. We shall investigate the so-called canonical standard completeness, where the semantics of Godel logic is given by the standard ¨ G-algebra and truth constants are interpreted by ’themselves’. The hyperresolution calculus is refutation sound and complete for a countable order clausal theory under a certain condition for the set of truth constants occurring in the theory. As an interesting consequence, we get an affirmative solution to the open problem of recursive enumerability of unsatisfiable formulae in Godel logic with truth constants and the equality, ¨ P, strict order, ≺, projection, ∆, operators.
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Paper Nr: 19
Title:

Using Evidence Theory in Land Cover Change Prediction to Model Imperfection Propagation with Correlated Inputs Parameters
Authors:

Ahlem Ferchichi, Wadii Boulila and Imed Riadh Farah
Abstract: The identification and the propagation of imperfection are important. In general, imperfection in land cover change (LCC) prediction process can be categorized as both aleatory and epistemic. This imperfection, which can be subdivided into parameter and structural model imperfection, is recognized to have an important impact on results. On the other hand, correlation of input system parameters is often neglected when modeling this system. However, correlation of parameters often blurs the model imperfection and makes it difficult to determine parameter imperfection. Several studies in literature depicts that evidence theory can be applied to model aleatory and epistemic imperfection and to solve multidimensional problems, with consideration of the correlation among parameters. The effective contribution of this paper is to propagate the imperfection associated with both correlated input parameters and LCC prediction model itself using the evidence theory. The proposed approach is divided into two main steps: 1) imperfection identification step is used to identify the types of imperfection (aleatory and/or epistemic), the sources of imperfections, and the correlations of the uncertain input parameters and the used LCC prediction model, and 2) imperfection propagation step is used to propagate aleatory and epistemic imperfection of correlated input parameters and model structure using the evidence theory. The results show the importance to propagate both parameter and model structure imperfection and to consider correlation among input parameters in LCC prediction model. In this study, the changes prediction of land cover in Saint-Denis City, Reunion Island of next 5 years (2016) was anticipated using multi-temporal Spot-4 satellite images in 2006 and 2011. Results show good performances of the proposed approach in improving prediction of the LCC of the Saint-Denis City on Reunion Island.
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Paper Nr: 22
Title:

Choosing Suitable Similarity Measures to Compare Intuitionistic Fuzzy Sets that Represent Experience-Based Evaluation Sets
Authors:

Marcelo Loor and Guy De Tré
Abstract: Which similarity measures can be used to compare two Atanassov’s intuitionistic fuzzy sets (IFSs) that respectively represent two experience-based evaluation sets? To find an answer to this question, several similarity measures were tested in comparisons between pairs of IFSs that result from simulations of different experience-based evaluation processes. In such a simulation, a support vector learning algorithm was used to learn how a human editor categorizes newswire stories under a specific scenario and, then, the resulting knowledge was used to evaluate the level to which other newswire stories fit into each of the learned categories. This paper presents our findings about how each of the chosen similarity measures reflected the perceived similarity among the simulated experience-based evaluation sets.
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Paper Nr: 27
Title:

Bayesian Logistic Regression using Vectorial Centroid for Interval Type-2 Fuzzy Sets
Authors:

Ku Muhammad Naim Ku Khalif and Alexander Gegov
Abstract: It is necessary to represent the probabilities of fuzzy events based on a Bayesian knowledge. Inspired by such real applications, in this research study, the theoretical foundations of Vectorial Centroid of interval type-2 fuzzy sets with Bayesian logistic regression is introduced. This includes official models, elementary operations, basic properties and advanced application. The Vectorial Centroid method for interval type-2 fuzzy set takes a broad view by exampled labelled by a classical Vectorial Centroid defuzzification method for type-1 fuzzy sets. Rather than using type-1 fuzzy sets for implementing fuzzy events, type-2 fuzzy sets are recommended based on the involvement of uncertainty quantity. It also highlights the incorporation of fuzzy sets with Bayesian logistic regression allows the use of fuzzy attributes by considering the need of human intuition in data analysis. It is worth adding here that this proposed methodology then applied for BUPA liver-disorder dataset and validated theoretically and empirically.
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Paper Nr: 30
Title:

Fuzzy Color Descriptors to Index Roman Mosaic-images
Authors:

Wafa Maghrebi, Mohamed A. Khabou and Adel M. Alimi
Abstract: We present efficient fuzzy color descriptors to index and retrieve images by content. The proposed approach uses fuzzy image dominant colors and fuzzy color histogram features to index images based on regions. Similar images are extracted using a fuzzy similarity and histogram intersection measures. Tests demonstrate that the fusion of the fuzzy features increase the precision of the system. The proposed index is tested on two databases: the first one containing 800 images of historical Roman mosaics from the 1st to 4th century, whereas the second database is composed of 1000 images of Corel images database. Evaluation tests and comparison with several other prevailing approaches prove a remarkable performance of our proposed approach.
Short Papers
Paper Nr: 6
Title:

Evolving Black Box Recursive Modeling Algorithm
Authors:

Orlando Donato Rocha Filho and Ginalber Luiz de Oliveira Serra
Abstract: In this paper an evolving recursive fuzzy cluster algorithm based on maximum likelihood criterion using the recursive instrumental variable parameter estimation for nonlinear system identification, is proposed. The performance of the proposed methodology is illustrated for black box modeling of a thermal plant from real– time acquisition data plataform. The experimental results are evaluated from metrics used in the literature to show the efficiency of the proposed online evolving recursive fuzzy clustering algorithm.
Paper Nr: 7
Title:

Hybrid Controller based on Fuzzy Logic for Doubly Fed Induction Generator used in a Chain of Wind Power Conversion
Authors:

Jean N. Razafinjaka and Tsiory Patrick Andrianantenaina
Abstract: This paper deals with a study of hybridization based on fuzzy logic controller and a polynomial RST one. This new controller is applied on a Doubly-Fed Induction Generator (DFIG) dedicated in a chain of wind power conversion. These two types or controller have completely different structures: the RST controller uses directly the input and the output to form the command law and the fuzzy logic one needs the error and its variation as inputs. The new proposal consists first to make leave the a priori command from the RST structure and then to form the error and the difference of two consecutive error values for the fuzzy logic controller. The results of simulation show that this technique is realizable and leads to good performances on tracking test, disturbance rejection and robustness with respect of operating variation and parametric variation.
Paper Nr: 8
Title:

Fuzzy Semi-Quantales, (L,M) Quasi-Fuzzy Topological Spaces and Their Duality
Authors:

Mustafa Demirci
Abstract: The present paper introduces M-fuzzy semi-quantales, fuzzifying semi-quantales, and (L,M)-quasi-fuzzy topological spaces, providing a common framework for (L,M)-fuzzy topological spaces of Kubiak and Sostak, ˇ L-quasi-fuzzy topological spaces of Rodabaugh and L-fuzzy topological spaces of Hohle and ¨ Sostak. In this ˇ paper, we set up a dual adjunction between the category of (L,M)-quasi-fuzzy topological spaces and the category of M-fuzzy semi-quantales, and then show that this adjunction includes a dual equivalence between the category of (L,M)-sober (L,M)-quasi-fuzzy topological spaces and the category of (L,M)-spatial M-fuzzy semi-quantales.
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Paper Nr: 10
Title:

Interval Type 2- Fuzzy Rule based System Approach for Selection of Alternatives using TOPSIS
Authors:

Abdul Malek Yaakob, Ku M. Naim Ku Khalif, Alexander Gegov and Siti Fatimah Abdul Rahman
Abstract: The paper considers fuzzy rule based system for multi criteria group decision making problem. A novel version of TOPSIS method using interval type 2 fuzzy rule based system approach is proposed with the objective of improving the type 2 TOPSIS ability to deal with ambiguity through the combination of the mathematical process involved in the type 2 TOPSIS with the expert empirical knowledge. On the other hand, a hybrid analysis of decision making process that requires the use of human sensitivity to reflect influence degree of decision maker can be expressed by a fuzzy rule base. To ensure practicality and effectiveness of proposed method, stock selection problem is studied. The ranking based on proposed method is validated comparatively using Kendall’s Tau rank correlation. Based on the result, the proposed method outperforms the established non-rule based version of type 2 TOPSIS in term of ranking performance.
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Paper Nr: 14
Title:

LEADER EU Program and Its Governance - A Fuzzy Assessment Model
Authors:

Luca Anzilli, Gisella Facchinetti, Giovanni Mastroleo and Alessandra Tafuro
Abstract: In regard to the LEADER program (European Union initiative for rural development), in the paper the authors propose a model for assessing the governance system of Local Action Groups (LAGs) in terms of structure, decision making processes and principles that ensure a clear and transparent activity thus creating significant value for the community. Governance, in particular, is a highly important theme when it evaluates the impacts of LEADER measures: if the quality of their governance is high, they could contribute to make the rural development process more efficient in each region of EU. The empirical literature on this subject is not well developed and the authors hope and expect that this new assessment model will produce important ideas for making governance of the LAGs more effective. It is based on a Fuzzy Expert System and here are presented results for Puglia (Italy) LAGs.
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Paper Nr: 20
Title:

Fuzzy Control of a Water Pump for an Agricultural Plant Growth System
Authors:

José Dias, João Paulo Coelho and José Alexandre Gonçalves
Abstract: At the present time there is a high pressure toward the improvement of all the production processes. Those improvements can be sensed in several directions in particular those that involve energy efficiency. The definition of tight energy efficiency improvement policies is transversal to several operational areas ranging from industry to public services. As can be expected, agricultural processes are not immune to this tendency. This statement takes more severe contours when dealing with indoor productions where it is required to artificially control the climate inside the building or a partial growing zone. Regarding the latter, this paper presents an innovative system that improves energy efficiency of a trees growing platform. This new system requires the control of both a water pump and a gas heating system based on information provided by an array of sensors. In order to do this, a multi-input, multi-output regulator was implemented by means of a Fuzzy logic control strategy. Presented results show that it is possible to simultaneously keep track of the desired growing temperature set-point while maintaining actuators stress within an acceptable range.
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Paper Nr: 21
Title:

Fuzzy Modeling of Development of Sheets Number in Different Irrigation Levels of Irrigated Lettuce with Magnetically Treated Water
Authors:

Fernando F. Putti, Luís Roberto Almeida Gabriel Filho, Camila Pires Cremasco and Antonio Evaldo Klar
Abstract: In the wake of the worldwide water supply crisis, several methods are being used to optimize the use of water, mainly in agriculture, which is the main consuming factor. Magnetically treated water for agriculture is beneficent due to an increase in quality and productivity. Current assay evaluates the effects of magnetically treated water in lettuce cultivations throughout its cycle and determines the intermediate rates by fuzzy models submitted at different reposition rates and assessed throughout the cycles. The assay was conducted in randomized blocks with a 4 x 5 factor scheme, with 5 reposition laminas and 4 dates after transplant. Development was evaluated by fuzzy mathematical modeling and by multiple polynomial regressions. Results were compared with data collected on the field. The highest development occurred for treatments irrigated with magnetically treated water, featuring a greater green aerial phytomass and number of leaves throughout the cycle. The fuzzy model provided a more exact adjustment when compared with results from statistical models.
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Paper Nr: 24
Title:

Many-Valued Logic through Its History
Authors:

Angel Garrido
Abstract: Our purpose is to contribute here to the searching for the origins of many-valued logics and, within them, as a special case that of “Fuzzy Logic”, also called by different manners, as Diffuse Logic, either Heuristic Logic, or `logique floue´ (in French), etc. It is also our goal to relate how was welcome to many-valued logics in our Iberian Peninsula, which is just another province of the world philosophical universe.
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Paper Nr: 29
Title:

Hybrid Methodology Focused on the Model of Binary Patterns and the Theory of Fuzzy Logic for Facial Biometric Verification and Identification
Authors:

Sergio González Nava, Alberto J. Rosales Silva, Nidiyare Hevia Montiel, Francisco Javier Gallegos Funes and Mario Dehesa González
Abstract: It is proposed a methodology to improve verification and identification biometric facial indicators based in hybridization binary pattern models and the fuzzy logic theory, making besides use of the traditional image pre-processing models, feature extraction and classifiers to validate the performance of the proposal methodology. The facial recognition is complicated due to the variability of the facial appearance related the same person, and the small characteristic samples for each person in adverse conditions. To fix this, is considered the binary pattern models as an excellent choice to the local face representations, whose more important properties is their tolerance against the variations of luminance, scale and rotation. However, the binary pattern model is sensitive to small variations of the pixel intensities, generally caused by the noise, which introduce uncertainty to the texture and contrast representation of the facial image. Using fuzzy logic in the binary patterns calculation, leads to a texture representation model that takes into account the uncertainty of the contained information in each image, providing a better representation of texture and contrast measure. In combination with traditional algorithms in the pre-processing stage, as photometric and histogram normalization, the feature extraction stage is achieved using linear discriminants and Gabor wavelets to provide finally a stage of the support vector machines classification.
Paper Nr: 33
Title:

Template-based Affine Registration of Autistic Brain Images
Authors:

Porawat Visutsak
Abstract: This paper presents a new method for the study of autistic brain image called “Template-based affine registration”, based on the transformation of the grid-line from a source image to a target image. By using the locations of grid of both source and target images as the control structure, together with a smart transition of grid computed by bilinear and affine transformations. Besides, the new locations of grid of a target image corresponding to a source image are the best-move of all feature points translated from a source to a target. The template named after the point set extracted from source image, the simple idea is to use the affine transformation for mapping the target point set to the template. The transformation process is used effectively by using the incorporating transition of grid to maintain geometric alignment throughout the process; the proposed method achieves a smooth transformation for image registration.
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Paper Nr: 35
Title:

A Fuzzy Poisson Naive Bayes Classifier for Epidemiological Purposes
Authors:

Ronei Marcos de Moraes and Liliane S. Machado
Abstract: Statistical methods have been used to classify data in different areas. In epidemiological studies, some measures follow specific statistical distribution and compatible classifiers can be designed for those cases. Classifiers based on measures that follow Poisson distributions can be found in the scientific literature. Due to uncertainty on epidemiological measures, a fuzzy approach may be interesting and the present work proposes a new classifier named Fuzzy Poisson Naive Bayes (FPNB). The theoretical development is presented as well as results of its application on simulated multidimensional data. A brief comparison with a classical Poisson Naive Bayes classifier and with a Naive Bayes classifier is performed too.
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Paper Nr: 16
Title:

Ranking of Interval Type-2 Fuzzy Numbers based on Centroid Point and Spread
Authors:

Ahmad Syafadhli Abu Bakar, Ku Muhammad Naim Ku Khalif and Alexander Gegov
Abstract: A concept of interval type-2 fuzzy numbers is introduced in decision making analysis as this concept is capable to effectively deal with the uncertainty in the information about a decision. It considers two types of uncertainty namely inter and intra personal uncertainties, in enhancing the representation of type-1 fuzzy numbers in the literature of fuzzy sets. As interval type-2 fuzzy numbers are crucial in decision making, this paper proposes a methodology for ranking interval type-2 fuzzy numbers. This methodology consists of two parts namely the interval type-2 fuzzy numbers reduction methodology as the first part and ranking of type-1 fuzzy numbers as the second part. In this study, established reduction methodology of interval type-2 fuzzy numbers into type-1 fuzzy numbers is extended to reduction into standardised generalised type-1 fuzzy numbers as the extension complements the capability of the methodology on dealing with both positive and negative data values. It is worth adding here that this methodology is analysed using thorough empirical comparison with some established ranking methods for consistency evaluation. This methodology is considered as a generic decision making procedure, especially when interval type-2 fuzzy numbers are applied to real decision making problems.
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Paper Nr: 17
Title:

Multilinear Objective Function-based Clustering
Authors:

Giovanni Rossi
Abstract: The input of most clustering algorithms is a symmetric matrix quantifying similarity within data pairs. Such a matrix is here turned into a quadratic set function measuring cluster score or similarity within data subsets larger than pairs. In general, any set function reasonably assigning a cluster score to data subsets gives rise to an objective function-based clustering problem. When considered in pseudo-Boolean form, cluster score enables to evaluate fuzzy clusters through multilinear extension MLE, while the global score of fuzzy clusterings simply is the sum over constituents fuzzy clusters of their MLE score. This is shown to be no greater than the global score of hard clusterings or partitions of the data set, thereby expanding a known result on extremizers of pseudo-Boolean functions. Yet, a multilinear objective function allows to search for optimality in the interior of the hypercube. The proposed method only requires a fuzzy clustering as initial candidate solution, for the appropriate number of clusters is implicitly extracted from the given data set.
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Paper Nr: 18
Title:

Contribution to Automatic Design of a Hierarchical Fuzzy Rule Classifier
Authors:

Cristhian Molina, Vincent Bombardier and Patrick Charpentier
Abstract: In this paper, two ways for automatically designing a hierarchical classifier is checked. This study deals with a specific context where is necessary to work with a few number of training samples (and often unbalanced), to manage the subjectivity of the different output classes and to take into account an imprecision degree in the input data. The aim is also to create an interpretable classification system by reducing its dimensionality with the use of Feature Selection and Fuzzy Association Rules generation. The obtained results over an industrial wood datasets prove their efficacy to select input feature and they are used to make some conclusions about their performance. Finally, an original methodology to automatically build a hierarchical classifier is proposed by merging the both previous methods. Each node of the hierarchical structure corresponds to a Fuzzy Rules Classifier with selected inputs and macro classes for output. The leaves are the outputs of the classification system.
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Paper Nr: 25
Title:

A New Approach to Aggregation of Inconsistent Expert Opinions
Authors:

Andrzej Piegat and Karina Tomaszewska
Abstract: The aim of this paper is to present a new way of aggregation two expert opinions. These opinions are disjoint and inconsistent, thus it is difficult to find a common solution using currently known methods. The authors suggest using horizontal membership function and RDM (Relative Distance Measure) method to get complete and unambiguous result. A general outline of this approach is presented and its equations are shown. The numerical example is given to illustrate the efficiency of the proposed method to practical issues in decision-making problems.
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