Showing papers on "Soft computing published in 1999"

Soft set theory—First results

Abstract: The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future.

Rough-Fuzzy Hybridization: A New Trend in Decision Making

Abstract: From the Publisher: This volume provides a collection of twenty articles containing new material and describing the basic concepts and characterizing features of rough set theory and its integration with fuzzy set theory, for developing an efficient soft computing strategy of machine learning. Written by leading experts from all over the world, the contributions demonstrate how rough-fuzzy hybridization can be made in various ways to provide flexible information processing capabilities for handling different real-life, ambiguous decision-making problems. This volume provides a balanced mix of theory and application.

Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing

Abstract: From the Publisher: The authors consolidate a wealth of information previously scattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network - classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems.

New directions in rough sets, data mining, and granular-soft computing, 7th International Workshop, RSFDGrC'99, Yamaguchi, Japan, November 9-11, 1999 : proceedings

Abstract: Invited Talks.- Decision Rules, Bayes' Rule and Rough Sets.- A New Direction in System Analysis: From Computation with Measurements to Computation with Perceptions.- On Text Mining Techniques for Personalization.- A Road to Discovery Science.- Rough Computing: Foundations and Applications.- Calculi of Granules Based on Rough Set Theory: Approximate Distributed Synthesis and Granular Semantics for Computing with Words.- Discovery of Rules about Complications.- Rough Genetic Algorithms.- Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach.- Rough Set Theory and Its Applications.- Toward Spatial Reasoning in the Framework of Rough Mereology.- An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information.- On the Extension of Rough Sets under Incomplete Information.- On Rough Relations: An Alternative Formulation.- Formal Rough Concept Analysis.- Noise Reduction in Telecommunication Channels Using Rough Sets and Neural Networks.- Rough Set Analysis of Electrostimulation Test Database for the Prediction of Post-Operative Profits in Cochlear Implanted Patients.- A Rough Set-Based Approach to Text Classification.- Modular Rough Fuzzy MLP: Evolutionary Design.- Approximate Reducts and Association Rules.- Handling Missing Values in Rough Set Analysis of Multi-attribute and Multi-criteria Decision Problems.- The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings.- Rough Problem Settings for Inductive Logic Programming.- Using Rough Sets with Heuristics for Feature Selection.- The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm.- Fuzzy Set Theory and Its Applications.- Level Cut Conditioning Approach to the Necessity Measure Specification.- Four c-Regression Methods and Classification Functions.- Context-Free Fuzzy Sets in Data Mining Context.- Applying Fuzzy Hypothesis Testing to Medical Data.- Generating a Macroeconomic Fuzzy Forecasting System Using Evolutionary Search.- Fuzzy Control of Nonlinear Systems Using Nonlinearized Parameterization.- Control of Chaotic Systems Using Fuzzy Model-Based Regulators.- Fuzzy Behavior-Based Control for the Obstacle Avoidance of Multi-link Manipulators.- Fuzzy Future Value and Annual Cash Flow Analyses.- Semi-linear Equation with Fuzzy Parameters.- Non-classical Logic and Approximate Reasoning.- First Order Rough Logic-Revisited.- A Generalized Decision Logic in Interval-Set-Valued Information Tables.- Many-Valued Dynamic Logic for Qualitative Decision Theory.- Incorporating Fuzzy Set Theory and Matrix Logic in Multi-layer Logic.- Fuzzy Logic as Interfacing Media for Constraint Propagation Based on Theories of Chu Space and Information Flow.- Pattern Reasoning: A New Solution for Knowledge Acquisition Problem.- Probabilistic Inference and Bayesian Theorem Based on Logical Implication.- Reasoning with Neural Logic Networks.- The Resolution for Rough Propositional Logic with Lower (L) and Upper (H) Approximate Operators.- Information Granulation and Granular Computing.- Information Granules in Distributed Environment.- Evolving Granules for Classification for Discovering Difference in the Usage of Words.- Interval Evaluation by AHP with Rough Set Concept.- Interval Density Functions in Conflict Analysis.- Incorporating Personal Database Unification by Granular Computing.- Data Mining and Knowledge Discovery.- Knowledge-Driven Discovery of Operational Definitions.- A Closest Fit Approach to Missing Attribute Values in Preterm Birth Data.- Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density.- A Distance-Based Clustering and Selection of Association Rules on Numeric Attributes.- Knowledge Discovery for Protein Tertiary Substructures.- Integrating Classification and Association Rule Mining: A Concept Lattice Framework.- Using Rough Genetic and Kohonen's Neural Network for Conceptual Cluster Discovery in Data Mining.- Towards Automated Optimal Equity Portfolios Discovery in a Knowledge Sharing Financial Data Warehouse.- Rule-Evolver: An Evolutionary Approach for Data Mining.- Machine Learning.- Decision Making with Probabilistic Decision Tables.- The Iterated Version Space Learning.- An Empirical Study on Rule Quality Measures.- Rules as Attributes in Classifier Construction.- An Algorithm to Find the Optimized Network Structure in an Incremental Learning.- Patterns in Numerical Data: Practical Approximations to Kolmogorov Complexity.- Performance Prediction for Classification Systems.- Intelligent Agents and Systems.- Flexible Optimization and Evolution of Underwater Autonomous Agents.- Ontology-Based Multi-agent Model of an Information Security System.- Optimal Multi-scale Time Series Decomposition for Financial Forecasting Using Wavelet Thresholding Techniques.- Computerized Spelling Recognition of Words Expressed in the Sound Approach.- An Adaptive Handwriting Recognition System.

New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

Abstract: Invited Talks.- Decision Rules, Bayes' Rule and Rough Sets.- A New Direction in System Analysis: From Computation with Measurements to Computation with Perceptions.- On Text Mining Techniques for Personalization.- A Road to Discovery Science.- Rough Computing: Foundations and Applications.- Calculi of Granules Based on Rough Set Theory: Approximate Distributed Synthesis and Granular Semantics for Computing with Words.- Discovery of Rules about Complications.- Rough Genetic Algorithms.- Classifying Faults in High Voltage Power Systems: A Rough-Fuzzy Neural Computational Approach.- Rough Set Theory and Its Applications.- Toward Spatial Reasoning in the Framework of Rough Mereology.- An Algorithm for Finding Equivalence Relations from Tables with Non-deterministic Information.- On the Extension of Rough Sets under Incomplete Information.- On Rough Relations: An Alternative Formulation.- Formal Rough Concept Analysis.- Noise Reduction in Telecommunication Channels Using Rough Sets and Neural Networks.- Rough Set Analysis of Electrostimulation Test Database for the Prediction of Post-Operative Profits in Cochlear Implanted Patients.- A Rough Set-Based Approach to Text Classification.- Modular Rough Fuzzy MLP: Evolutionary Design.- Approximate Reducts and Association Rules.- Handling Missing Values in Rough Set Analysis of Multi-attribute and Multi-criteria Decision Problems.- The Generic Rough Set Inductive Logic Programming Model and Motifs in Strings.- Rough Problem Settings for Inductive Logic Programming.- Using Rough Sets with Heuristics for Feature Selection.- The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm.- Fuzzy Set Theory and Its Applications.- Level Cut Conditioning Approach to the Necessity Measure Specification.- Four c-Regression Methods and Classification Functions.- Context-Free Fuzzy Sets in Data Mining Context.- Applying Fuzzy Hypothesis Testing to Medical Data.- Generating a Macroeconomic Fuzzy Forecasting System Using Evolutionary Search.- Fuzzy Control of Nonlinear Systems Using Nonlinearized Parameterization.- Control of Chaotic Systems Using Fuzzy Model-Based Regulators.- Fuzzy Behavior-Based Control for the Obstacle Avoidance of Multi-link Manipulators.- Fuzzy Future Value and Annual Cash Flow Analyses.- Semi-linear Equation with Fuzzy Parameters.- Non-classical Logic and Approximate Reasoning.- First Order Rough Logic-Revisited.- A Generalized Decision Logic in Interval-Set-Valued Information Tables.- Many-Valued Dynamic Logic for Qualitative Decision Theory.- Incorporating Fuzzy Set Theory and Matrix Logic in Multi-layer Logic.- Fuzzy Logic as Interfacing Media for Constraint Propagation Based on Theories of Chu Space and Information Flow.- Pattern Reasoning: A New Solution for Knowledge Acquisition Problem.- Probabilistic Inference and Bayesian Theorem Based on Logical Implication.- Reasoning with Neural Logic Networks.- The Resolution for Rough Propositional Logic with Lower (L) and Upper (H) Approximate Operators.- Information Granulation and Granular Computing.- Information Granules in Distributed Environment.- Evolving Granules for Classification for Discovering Difference in the Usage of Words.- Interval Evaluation by AHP with Rough Set Concept.- Interval Density Functions in Conflict Analysis.- Incorporating Personal Database Unification by Granular Computing.- Data Mining and Knowledge Discovery.- Knowledge-Driven Discovery of Operational Definitions.- A Closest Fit Approach to Missing Attribute Values in Preterm Birth Data.- Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density.- A Distance-Based Clustering and Selection of Association Rules on Numeric Attributes.- Knowledge Discovery for Protein Tertiary Substructures.- Integrating Classification and Association Rule Mining: A Concept Lattice Framework.- Using Rough Genetic and Kohonen's Neural Network for Conceptual Cluster Discovery in Data Mining.- Towards Automated Optimal Equity Portfolios Discovery in a Knowledge Sharing Financial Data Warehouse.- Rule-Evolver: An Evolutionary Approach for Data Mining.- Machine Learning.- Decision Making with Probabilistic Decision Tables.- The Iterated Version Space Learning.- An Empirical Study on Rule Quality Measures.- Rules as Attributes in Classifier Construction.- An Algorithm to Find the Optimized Network Structure in an Incremental Learning.- Patterns in Numerical Data: Practical Approximations to Kolmogorov Complexity.- Performance Prediction for Classification Systems.- Intelligent Agents and Systems.- Flexible Optimization and Evolution of Underwater Autonomous Agents.- Ontology-Based Multi-agent Model of an Information Security System.- Optimal Multi-scale Time Series Decomposition for Financial Forecasting Using Wavelet Thresholding Techniques.- Computerized Spelling Recognition of Words Expressed in the Sound Approach.- An Adaptive Handwriting Recognition System.

Fuzzy logic systems for transportation engineering: the state of the art

Abstract: The paper presents a classification and analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Fuzzy logic is shown to be a very promising mathematical approach to modeling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation engineering problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Possibilities are shown regarding the further application of fuzzy logic in this field.

Applying soft computing approaches to river level forecasting

Abstract: This paper assesses one of many potential enhancements to conventional flood forecasting that can be achieved through the use of soft computing technologies. A methodology is outlined in which the forecasting data set is split into subsets before training with a series of neural networks. These networks are then recombined via a rule-based fuzzy logic model that has been optimized using a genetic algorithm. The methodology is demonstrated using historical time series data from the Ouse River catchment in northern England. The model forecasts are assessed on global performance statistics and on a more specific flood-related evaluation measure, and they are compared to benchmarks from a statistical model and naive predictions. The overall results indicate that this methodology may provide a well performing, low-cost solution, which may be readily integrated into existing operational flood forecasting and warning systems.

Hybrid soft computing systems : Industrial and commercial applications : Special issue on computational intelligence

Abstract: Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized hy imprecise, uncertain data and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLC's) tuned by neural networks (NN's) and evolutionary computing (EC), NN's tuned by EC or FLC's, and EC controlled by FLC's. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation.

Hybrid soft computing systems: industrial and commercial applications

Abstract: Soft computing (SC) is an association of computing methodologies that includes as its principal members fuzzy logic, neurocomputing, evolutionary computing and probabilistic computing. We present a collection of methods and tools that can be used to perform diagnostics, estimation, and control. These tools are a great match for real-world applications that are characterized by imprecise, uncertain data and incomplete domain knowledge. We outline the advantages of applying SC techniques and in particular the synergy derived from the use of hybrid SC systems. We illustrate some combinations of hybrid SC systems, such as fuzzy logic controllers (FLCs) tuned by neural networks (NNs) and evolutionary computing (EC), NNs tuned by EC or FLCs, and EC controlled by FLCs. We discuss three successful real-world examples of SC applications to industrial equipment diagnostics, freight train control, and residential property valuation.

Fuzzy logic-a modern perspective

Abstract: Traditionally, fuzzy logic has been viewed in the artificial intelligence (AI) community as an approach for managing uncertainty. In the 1990s, however, fuzzy logic has emerged as a paradigm for approximating a functional mapping. This complementary modern view about the technology offers new insights about the foundation of fuzzy logic, as well as new challenges regarding the identification of fuzzy models. In this paper, we first review some of the major milestones in the history of developing fuzzy logic technology. After a short summary of major concepts in fuzzy logic, we discuss a modern view about the foundation of two types of fuzzy rules. Finally, we review some of the research in addressing various challenges regarding automated identification of fuzzy rule-based models.

Nongradient Methods in Multidisciplinary Design Optimization-Status and Potential

Abstract: A number of multidisciplinary design optimization (MDO) problems are characterized by the presence of discrete and integer design variables, over and beyond the more traditional continuous variable problems. In continuous variable design problems, the design space may be nonconvex or even disjointed. Furthermore, the number of design variables and constraints may be quite large. The use of conventional gradient-based methods in such problems is fraught with hazards. First, these gradient-based methods cannot be used directly in the presence of discrete variables. Their use is facilitated by creating multiple equivalent continuous variable problems; in the presence of high dimensionality, the number of such problems to be solved can be quite large. Second, these methods have a propensity to converge to a relative optimum closest to the starting point, and this is a major weakness in the presence of multimodality in the design space. This paper primarily focuses on the use of nontraditional optimization methods in such problems, broadly classified today as soft computing strategies. These methods include techniques such as simulated annealing, genetic algorithms, Tabu search, and rule-based expert systems. It also examines issues pertinent to using these methods in MDO problems.

A survey of genetic feature selection in mining issues

Abstract: In this paper, we review the feature selection problem in mining issues. The application of soft computing techniques to data mining and knowledge discovery is now emerging in order to enhance the effectiveness of the traditional classification methods coming from machine learning. A survey of the approaches presented in the literature to select relevant features by using genetic algorithms is given. The different values of the genetic parameters utilized as well as the fitness functions are compared. A more detailed review of the proposals in the mining fields of databases, text and the Web is also given.

Unification of neural and wavelet networks and fuzzy systems

Abstract: Analyzes several commonly used soft computing paradigms (neural and wavelet networks and fuzzy systems, Bayesian classifiers, fuzzy partitions, etc.) and tries to outline similarities and differences among each other. These are exploited to produce the weighted radial basis functions paradigm which may act as a neuro-fuzzy unification paradigm. Training rules (both supervised and unsupervised) are also unified by the proposed algorithm. Analyzing differences and similarities among existing paradigms helps to understand that many soft computing paradigms are very similar to each other and can be grouped in just two major classes. The many reasons to unify soft computing paradigms are also shown in the paper. A conversion method is presented to convert perceptrons, radial basis functions, wavelet networks, and fuzzy systems from each other.

Soft Computing in Acoustics: Applications of Neural Networks, Fuzzy Logic and Rough Sets to Musical Acoustics

Abstract: Introduction Some Selected Soft Computing Tools and Techniques Preprocessing of Data in Acoustics Automatic Classification of Musical Instrument Sounds Automatic Recognition of Musical Phrases Intelligent Processing of Test Results Control Applications Conclusions.

Computational intelligence in control engineering

Abstract: From the Publisher: This reference/text describes the tremendous strides made by intelligent systems and soft computing for the control of industrial systems - presenting the theoretical and practical development of an autonomous decision-making methodology. Containing a case study of fuzzy controller design using MATLAB, Computational Intelligence in Control Engineering serves as an essential reference for electrical and electronics, mechanical, chemical, aeronautical, industrial, manufacturing, computer, production, and process engineers; computer scientists and applied physicists; and quality control experts; and as an ideal text for undergraduate and graduate students in these disciplines.

Soft computing in financial engineering

Abstract: Preface.- Introductory Sections: Imprecise Data, And Decision Making Under Fuzziness: M. Mares, R. Mesiar: Vagueness of Verbal Variables R.R. Yager, M.T. Lamata: Decision Making Under Uncertainty with Nonnumeric Payoffs S. Greco: Fuzzy Measures and Equilibrium Conditions on the Financial Market M. Mares: Sharing Vague Profit in Fuzzy Cooperation.- Time Series Analyses and Prediction: S. Giove, P. Pellizzari: Time Series Filtering and Reconstruction Using Fuzzy Weighted Local Regression O. Castillo, P. Melin: Automated Mathematical Modelling for Financial Time Series Prediction Combining Fuzzy Logic and Fractal Theory.- P.L. Belcarao, M. Corazza: A 2-Stage Artificial Neural Network Predictor with Application to Financial Time Series R. Kozma, N.K. Kasabov: Generic Neuro-Fuzzy-Chaos Methodology for Time-Series Analysis and Building Intelligent Adaptive Systems M. Krawczak: Dynamic Programming and Fuzzy Reinforcement of Backpropagation for Interest Rate Prediction.- Stock and Currency Markets: H. Tanaka, P. Guo: Portfolio Selection Based on Possibility Theory S. Siekman, R. Neuneier, H.G. Zimmermann, R. Kruse: Neuro-Fuzzy Methods Applied to the German Stock Index DAX M.A. Soares Macahado, L.A. Rodrigues Gaspar, A. Araujo de Freitas Jr., R. Castro Souza: IBOVESPA Neuro-Fuzzy Forecasting: A Case Study in Brazilian Capital Markets K.K. Yen, S. Goshray: Application of Fuzzy Regression Models to Predict Exchange Rates for Composite Currencies S. Goshray, K.K. Yen: A Fuzzy Inferencing Approach Towards the Chaotic Nature of Foreign Currency Interactions S. Tano: Application of Fuzzy Methodologies to Financial Fields: FOREX Case Studies and Generalizations.- Corporate Financial Analyses: S. Benferhat, H. Farreny, H. Prade: Possibilistic Rule-Based Inference: A Case Study in Financial Analysis R.A. Ribeiro, F. Moura-Pires: Financial Analysis of Non-Financial Companies with Neural Networks P. Hofmeister: Customer Segmentation with Fuzzy Clustering N. Vojdani, M. Bellmann: A Rejects Management Information System by Means of Fuzzy Logic.- Analyses and Calculation of Risk and Value: H.J. Rommelfanger: Fuzzy Logic Based Systems for Checking Credit Solvency of Small Business Firms R. Weber: Applications of Fuzzy Logic for Creditworthiness Evaluation R. Slowonski, C. Zapounodis, A.I. Dimitras, R. Susmaga: Rough Set Predictor of Business Failure M.R. Simonelli: Fuzzy Insurance Premium Principles G. Gim, Th. Whalen: Second Order Data Mining: Fire Risk Classification in a Newly-Developed Country P.P. Bonissone, W. Cheetham, D.C. Golibersuch, P. Khedkar: Automated Residential Property Valuation: An Accurate and Reliable Approach Based on Soft Computing.- Auditing and Reporting: S.K. Dutta, R.P. Srivastava: Theoretical Investigation of Belief Revision in Auditing E.H. Feroz, T.M. Kwon: Self-Organizing Fuzzy and MLP Approaches to Detecting Fraudulent Financial Reporting.

Towards a model of input-output behaviour of wastewater treatment plants using soft computing techniques

Abstract: Wastewater Treatment Plants (WWTPs) control and prediction under a wide range of operating conditions is an important goal in order to avoid breaking of environmental balance, keeping the system in stable operating conditions and suitable decision-making. In this respect, the availability of models characterizing WWTP behaviour as a dynamic system is a necessary first step. However, due to the high complexity of the WWTP processes and the heterogeneity, incompleteness and imprecision of WWTP data, and finding suitable models poses substantial problems. In this work, an approach via soft computing techniques is sought, in particular, by experimenting with fuzzy heterogeneous time-delay neural networks to characterize the time variation of outgoing variables. Experimental results show that these networks are able to characterize WWTP behaviour in a statistically satisfactory sense and also that they perform better than other well-established neural network models.

Soft Computing and Intelligent Systems: Theory and Applications

Abstract: From the Publisher: Invited researchers from around the world discuss the principles of and their experience with soft computing, an emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. Within the broad categories of foundations, theory, implications and applications, and future prospects, the 25 studies consider such topics as parallel and distributed architectures and biologically inspired computing, neural networks for identifying nonlinear systems, the knowledge- based adaptation of a neurofuzzy model in the predictive control of a heat exchanger, and toward intelligent machines. The field is so new that the bibliography claims comprehension.

Soft-computing-based approaches to the group technology problem: A state-of-the-art review

Abstract: This paper presents a state-of-the-art review synthesizing the literature on the use of soft-computing-based approaches, e.g. neural networks and fuzzy models to the group technology problem. The objectives of this paper are to discuss the trend and to identify the gaps for performing future research in this area.

Fuzzy Cognitive Map Approach to Process Control Systems Chrysostomos

Abstract: Corrventional control has signhicantly contributed to the solution of many control problems, but its contribution to solutions of increasingly complex dynamical systems has practical dWiculties. Requirements in control and in supervisory control cannot be met with existing conventional control theory and new methods are required that exploit past experience, can learn, and provide failure detection and identification. Soft computing thus becomes an important alternative to corrventional control. Fuzzy cognitive map (FCM) usage for control and modeling systems is expected to contribute much to the effort to create more intedigent control systems. FCM describes and models a system symbolically, using concepts to illustrate ditiferent aspects of system behavior that interact, showing system dynamics. A FCM integrates experienoe and knowledge on system operation due to how it is constructed, i.e., using human experts that know system operation and its behavior in ditiferent circumstances. Due to their dynamic nature, FCMs are exploited to represent and conduct system control. Political scientist R. Axelrodi) introduced cognitive maps for representing social scientific knowledge and describing methods used for decision making in social and political systems. B. Kosko6'\" enhanced cognitive maps considering fuzzy values for conoepts of the cognitive map and fUzzy degrees of interrelationships between conoepts. After this pioneering work, FCMs attracted the attention of scientists in many fields and have been used in durerent scientific problems. New FCMs have been proposed such as the extended FCM5) and the neural cognitive maps9). FCMs have been used for planning and making decisions in international relations and political developmentsM and have been proposed for generic decision analysis20) and disuibuted cooperative agents2i), FcMs have been used to analyze electrical circuitsi4) and to construct vimal worlds2). ln control themes, FCMs have been used to model and support plant contro14), represent failure models and effects analysis for a system modeliiNi2), and to model the control system supervisori5-i6). The objective of this paper is to define and constmet FCMs models for describing complex systems. Section 2 describes Fems and proposes a calculation rule. Section 3 proposes a soft computing methodology for constructing and developing FCMs. Section 4 implements FCM to model and control a chemical process. Section 5 suggest the use of two-level FCMs to conduct supervisory control and discusses the failure part of a supervisor-FCM. Section 6 gives conclusions and prospects.

Computational intelligence and applications

Abstract: P.S. Szczepaniak: Preface.- Keynotes: W. Pedrycz: Computational Intelligence: An Introduction.- D. Dubois, H. Prade: Fuzzy Information Engineering and Soft Computing.- M.M. Gupta: Fuzzy Neural Computing.- P.M. Frank: AI Methods AI Fault Diagnosis.- T. Kaczorek: Positive 2D Linear Systems.- Case Studies: M. Al-Rawi, A. Materka: Voiced/Unvoiced Speech Signal Segmentation Using Wavelet Analysis.- D. Baczynski, P. Helt, M. Parol, P. Piotrowski: ANN and EA in Electrical Distribution Network Optimisation.- J. Balicki: Evolutionary Neural Networks for Solving Multiobjective Optimization Problems.- D. Barrios, I.M. Galvan, P. Isasi, J. Rios, J.M. Zaldivar: A New Neural Network Approach to Allow Predicative Control in Real Time.- G. Brzykcy: Modelling User Interaction in Prolog Programs.- L. Constant, P. Lagarrigues, B. Dagues, I. Rivals, L. Personnaz: Modeling of Electromechanical System Using Feedback Neural Network.- M. Dolinska, K.J. Cios: Dynamic Multiobjective Pricing Strategy Using Fuzzy Logic.- D.V. Filatova: Application of Dynamic Factor Model for the Training Process Prediction.- P. Golabek, W. Kosinski, M. Weigl: Adaptation of Learning Rate via Adaptation of Weight Vector in Modified M-Delta Networks.- Z. Gontar: Electronic Distribution Planning - Lessons in Simulated Annealing.- Z.S. Hippe: Computational Intelligence - An Example of Searching Regularities.- A. Jozwik, K. Strzecha: Neuronlike Net, Standard k-NN Classifier and Net of k-NN Classifiers. An Experimental Comparison Study.- J. Kabzinski, P. Wozniak: Adaptive Plus Fuzzy/Neural Controllers for MIMO Nonlinear Plants.- J. Kazimierczak: On Solving Some Artificial Intelligence Problems by Using Evolvable Hardware.- E. Kacki,B.A. Ostrowska, J. Stempczynska: Diagnostic Algorithm Based on Expert Matrix Obtained by Monte-Carlo Method.- S.S. Lam, X. Cai: Distance Measures of Fuzzy Numbers.- S.J. Langdell, J.C. Mason: Exploiting the Relevance of Input Variables to Improve Classification of Biomedical Data.- H.S. Lopes: A Medical Diagnostic System Optimization Using Parallel Genetic Algorithms.- Z. Lubosny: Identification of Dynamic Object by Neural Network.- A. Materka, M. Strzelecki: Observation Domain Partitioning by Means of ANNs in Real-Time Parameter Estimation Systems.- W. Miszalski, Z. Swiatnicki, R. Wantoch-Rekowski: The Air Force Development Using Experts' Knowledge and Common Sense.- S.K. Mitra, C.A. Murthy, M.K. Kundu: Digital Image Magnification Using Fractal Operators and Genetic Algorithm.- J.M. Molina-Lopez, P. Isasi-Vinuela, A. Sanchis de Miguel: Expert System-Neural Network: A Hybrid System for Predictive Control.- K.W. Przytula, D. Thompson: Analysis of Trained Neural Networks.- J. Ratynska: An Inference Model and Natural Language Processing.- A. Riid, E. Rustern: Fuzzy Modeling and Controlling of Fed-Batch Fermentation.- D. Rutkowska, R. Norwicki, L. Rutkowski: Singleton and Non-Singleton Fuzzy Systems with Nonparametric Defuzzification.- P. Strumillo, W. kaminski, J. Skrzypski: Modelling and Interpolation of Seasonal Air Temperature Changes in Central Europe.- Z. Swiatnicki, R. Semklo: The Multilayer Perceptron for Object Recognition Based on Radar Signals.- Z. Swiatnicki, R. Wantoch-Rekowski: The Stars Recognition with Neural Network Based on Special Representation of Celestial Constellation.- M. Swiercz: Modular Neural Networks for Modeling of a Nonlinear Dynamic System. A Case Study.- G.E. Tsekouras, G.V. Bafas, S.K. Stoyanov: Multivariable

Soft computing for multicustomer due-date bargaining

Abstract: The due-date bargainer is a useful tool to support negotiation on due dates between a manufacturer and its customers. To improve the computational performance of an earlier version of the due-date bargainer, we present a new soft computing approach. It uses a genetic algorithm to find the best priority sequence of customer orders for resource allocation, and fuzzy logic operations to allocate the resources and determine the order completion times, following the priority sequence of orders. To extend the due-date bargainer to accommodate bargaining with several customers at the same time, we propose a method to distribute the total penalty using marginal penalties for the individual bargainers. A demonstration software package implementing the improved due-date bargainer has been developed. It is targeted at apparel manufacturing enterprises. Experiments using realistic resource data and randomly generated orders have achieved satisfactory results.

Ila-2: an inductive learning algorithm for knowledge discovery

Abstract: In this paper we describe the ILA-2 rule induction algorithm, which is the improved version of a novel inductive learning algorithm ILA . We first outline the basic algorithm ILA, and then present how the algorithm is improved using a new evaluation metric that handles uncertainty in the data. By using a new soft computing metric, users can reflect their preferences through a penalty factor to control the performance of the algorithm. Inductive learning algorithm has also a faster pass criteria feature which reduces the processing time without sacrificing much from the accuracy that is not available in basic ILA. We experimentally show that the performance of ILA-2 is comparable to that of well-known inductive learning algorithms, namely, CN2, OC1, ID3, and C4.5.