PROGRAM
8th
WSEAS International Conference on
FUZZY SYSTEMS
(FS '07)
Vancouver, Canada, June 18-19, 2007
Monday, June 18 2007
PLENARY LECTURE 1
Partitioning Capabilities of Multi-Layer Perceptrons
Assistant Professor Che-Chern Lin
Department of Industrial Technology Education
National Kaohsiung Normal University
Taiwan
Abstract: Recently, multi-layer-structured neural networks have been widely used in many areas. The back-propagation algorithm is one of popular training algorithms where a multi-layered network structure is designed to map inputs to outputs using well-trained weights. To minimize classification errors, it probably takes a lot of computational time in updating weights. Multi-layer perceptrons (MLPs) are different multi-layer-structured neural networks for classifications. This lecture theoretically discusses the partitioning capabilities of MLPs. We first explain how a MLP forms its decision region and how it performs mappings from inputs to desired outputs. A general discussion on the partitioning capabilities of a single-layer perceptron, two-layer perceptron, and three-layer perceptron is also given. The implementation feasibilities on more complicated decision regions using MLPs are finally covered in this lecture.
PLENARY LECTURE 2
An Extension of the Crisp Ontology for Uncertain Information Modeling – Fuzzy Ontology Map
Associate Professor James Liu
Department of Computing
Hong Kong Polytechnic University
Abstract: In the current World Wide Web (WWW), users find it difficult to locate relevant information using search engines. This may be due to the fact that the current World Wide Web lacks semantic markup. One of the possible solutions for this problem is Semantic Web. In the latest Semantic Web technology, descriptive markup languages, such as Resource Description Framework (RDF) and Web Ontology Language (OWL), were proposed to model the web content in a machine-readable way which assists information gathering and automatic searching by software agents. Since these ontology markup languages deal with ‘hard’ semantics with the description and manipulation of crisp data, they are not capable of representing uncertain information while using current ontology representation.
This talk presents an extension of the current ontology representation which supports uncertain information modeling. The extension is called Fuzzy Ontology Map (FOM) which is based on the integration of fuzzy theory and graph theory. The FOM is a connection matrix which collects the membership values between classes in the ontology graph. Thus, a fuzzy ontology could be created by using the FOM and the ontology document (RDF/OWL). It is possible to use an FOCM for better knowledge management and information searching. We’ll focus on Web applications to develop systems that can deal with imprecise or vague information. Practical examples will be given and possible extension of the methodology incorporating the use of high level Petri nets will be provided for future work consideration.
PLENARY LECTURE 3
Applications of Meta-heuristics for Combinatorial Optimization Problems
Associate Professor Reza Tavakkoli-Moghaddam
Department of Industrial Engineering, Faculty of Engineering
University of Tehran, Tehran, Iran
Department of Mechanical Engineering
The University of British Columbia
Vancouver, Canada
Abstract: This plenary lecture deals with various applications of meta-heuristics to solve a number of the combinatorial optimization problems (COPs). It is divided into two main sections: (1) meta-heuristics and (2) optimization problems. In the original definition, meta-heuristics are stochastic/approximate solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of the solution space of the combinatorial optimization problems. With the development of complexity theory in the early 70's, it became clear that, since most of COPs were indeed NP-hard problems, there was little hope of ever finding efficient exact solution procedures for them. This realization emphasized the role of heuristics and meta-heuristics for solving such hard problems that were encountered in real-life applications and that needed to be tackled, whether or not they were NP-hard. Each meta-heuristic method balances the exploration and exploitation of the solution space using a specific strategy. The strategy is mainly inspired by the natural phenomena such as evolution, group cooperation, group competition, short/long-term memory, body immune system, self-replication, learning, and DNA/molecular computing or by other sciences such as annealing process, and quantum computations. Each strategy trades off between the effort and time spent to explore the new regions of the solution space and to exploit the explored regions. The solution representation and operator design are most significant aspects of implementing each meta-heuristic method. An important issue is that these two aspects are variety from a problem to another. In general, how to design operators is extremely depended on the structure of the solution representation and neighborhood definition. Thus, it is possible to consider the several ways for implementing a meta-heuristic method for a given problem. The closer the problems to the real-world situations, the harder the implementation of the meta-heuristics will be.
The focus of this talk is on considering the above-mentioned aspects of the foregoing meta-heuristics by a number of various examples. These typical examples are: the dynamic cell formation problem, flexible flow lines scheduling problem, aggregate production planning problem, resource-constraint project planning problem, and vehicle routing problem. The considered examples are taken from the newly published work by the author.
PLENARY LECTURE 4
Industrial
Applications using Artificial Intelligence and Statistical Techniques
Professor Anna Gabriela
Perez de Rivas
Universidad de Los Andes
Facultad de Ciencias Economicas y Sociales
Escuela de Estadistica
Merida - Venezuela
Abstract: In this plenary it will be presented some industrial applications using artificial intelligence techniques as: Expert Systems, Neural Networks, Fuzzy Logic, Neo-Fuzzy Systems and Genetic Algorithms that have been used together with Statistical Techniques as: Data Analysis, Cluster Analysis, Time-series, Data Imputation, among others.
These applications include methodologies for designing virtual sensors, fault detection and isolation systems, classifier systems and controller Auto-Tuning systems. Some of these applications have been developed for oil companies.
Also, it will be included some research in neural networks that have been developed using variable structure control- based learning algorithms.
SESSION: Fuzzy Systems & Applications
Chair: Yuan-Horng Lin, Sen Chi Yu
Concept Structure Analysis Method based on Integration of FLMP and ISM with Application in Equality Axiom Concepts |
Yuan-Horng Lin, Wen-Liang Hung, Sen-Chi Yu |
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The Feasibility Study of Applying Fuzzy Structural Modeling on Knowledge Structure Analysis |
Yuan-Horng Lin, He-Kai Chen |
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Comparisons of Possibility- and Probability-based Classification: An Example of Depression Severity Clustering |
Sen-chi Yu and Yuan-horng Lin |
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Neuro-Fuzzy Approach to Calibrate Function Points |
Wei Xia, Luiz Fernando Capretz,Danny Ho |
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Website Structures Ranking: Applying Extended ELECTRE III Method Based on Fuzzy Notions |
Hamed Qahri saremi, Gholam ali Montazer, |
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An Evaluation Model for determining Insurance Policy Using AHP and Fuzzy Logic: Case Studies of Life and Annuity Insurances |
Chin-Sheng Huang, Yu-Ju Lin, Che-Chern Lin |
SESSION: Fuzzy Systems & Applications I
Chair: Mohammadreza Rafiei, Miroslav Pokorny
An improved algorithm for online identification of evolving TS fuzzy models |
Esmaeel Banysaeed, Mohammadreza Rafiei , Mohammad Haddad |
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Non-Linear System State Analysis via Takagi-Sugeno Fuzzy Modelling |
Miroslav Pokorny, Pavel Fojtik |
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Fuzzy approach to ecological data analysis |
Arkadiusz Salski |
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Newsvendor pricing with fuzzy demand |
H. Ziya Ulukan, Duygu Ekici |
Tuesday, June 19 2007
SESSION: Fuzzy Control & Dynamical Systems
Chair: Mohammadreza Anvari, Shuzlina Abdul Rahman
Intelligent Water Dispersal Controller Using Mamdani Approach |
Shuzlina Abdul Rahman, Izham Fariz Ahmad Jinan, Kushairah Jazahanim, Azlinah Mohamed |
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Reducing Flare Emissions from Chemical Plants and Refineries Through the Application of Fuzzy Control System |
A. Alizadeh-Attar, H. R. Ghoohestani, I. Nasr Isfahani |
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A new fuzzy logic controller and its performance |
Shanshan Zhang, Guanrong Chen |
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Transitivity and Topological Entropy on Fuzzy Dynamical Systems Through Fuzzy Observation |
M. H. Anvari |
SESSION: Fuzzy Applications on the Web
Chair: James N.K. Liu, Gholam Ali Montazer
Relational fuzzy approach for mining user profiles |
Giovanna Castellano, Anna Maria Fanelli, Maria Alessandra Torsello |
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An Intelligent System Integrated with Fuzzy Ontology for Product Recommendation and Retrieval |
James N.K. Liu |
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Ranking of Website Structures Using Fuzzy TOPSIS Method with Type-2 Fuzzy Numbers |
Hamed Qahri saremi, Gholam ali Montazer, Farzad Haghighi rad |
SESSION: Fuzzy Systems & Applications II
Chair: J. J. Medel Juárez, Reza Tavakkoli-Moghaddam
RTFDF Description for ARMA Systems |
J. C. García Infante, J. J. Medel Juárez, P. Guevara López. |
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A fuzzy mixed-integer goal programming model for parallel machine scheduling problem |
R. Tavakkoli-Moghaddam, A.H. Gharehgozli, M. Rabbani, N. Zaerpour |
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Acceptance/Rejection of Incoming Orders by Analytical Hierarchy Process in Make-to-Order environments |
A. H. Gharehgozli, R. Tavakkoli-Moghaddam, M. Rabbani, N. Zaerpour |
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Application of Fuzzy Lead Time to a Material Requirement Planning System |
R. Tavakoli-Moghaddam, M. Bagherpour, A. A Noora, F. Sassani |
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Fuzzy Quality Systems |
Edson Pacheco Paladini |
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Design and Synthesis of Temperature Controller using Fuzzy for Industrial Application |
Md. Shabiul Islam, Mukter Zaman, M.S. Bhuyan, Masuri Othman |