Showing papers on "Soft computing published in 2007"

Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches

Abstract: This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The introductory part of the book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. Subsequently, advanced robust observer structures are presented. Parameter estimation based techniques are discussed as well. A particular attention is drawn to experimental design for fault diagnosis. The book also presents a number of robust soft computing approaches utilizing evolutionary algorithms and neural networks. All approaches described in this book are illustrated by practical applications.

Continuous Preference Logic for System Evaluation

Abstract: In this paper, we investigate mathematical models that are suitable for modeling decisions in the area of system evaluation, comparison, and selection. Our interest is focused on soft computing models that can be directly related to observable properties of human reasoning, and have a record of use in system evaluation practice. We analyze various approaches to defining andness and orness, and use a generalized conjunction/disjunction (GCD) to build compound preference logic functions and logic models for system evaluation. We also present applications of the continuous preference logic in decision models based on the LSP method.

Foundations of Fuzzy Logic and Soft Computing

Abstract: Relation Between Interval and Fuzzy Techniques.- Estimating Variance Under Interval and Fuzzy Uncertainty: Case of Hierarchical Estimation.- Testing Stochastic Arithmetic and CESTAC Method Via Polynomial Computation.- Friction Model by Using Fuzzy Differential Equations.- From Interval Computations to Constraint-Related Set Computations: Towards Faster Estimation of Statistics and ODEs Under Interval, p-Box, and Fuzzy Uncertainty.- Non-commutative System of Fuzzy Interval Logic Generated by the Checklist Paradigm Measure m 3 Containing Early Zadeh Implication.- Points with Type-2 Operations.- Intuitionistic Fuzzy Sets and Their Applications.- Atanassov's Intuitionistic Fuzzy Sets as a Classification Model.- Classification with Nominal Data Using Intuitionistic Fuzzy Sets.- Intuitionistic Fuzzy Histograms of an Image.- Image Threshold Using A-IFSs Based on Bounded Histograms.- The Role of Entropy in Intuitionistic Fuzzy Contrast Enhancement.- Representation of Rough Sets Based on Intuitionistic Fuzzy Special Sets.- The Application of Fuzzy Logic and Soft Computing in Flexible Querying.- Towards Vague Query Answering in Logic Programming for Logic-Based Information Retrieval.- On Browsing Domain Ontologies for Information Base Content.- Fuzzy Tree Mining: Go Soft on Your Nodes.- Numerical Properties of Fuzzy Regions: Surface Area.- Qualification of Fuzzy Statements Under Fuzzy Certainty.- Weighted Conjunctive and Disjunctive Aggregation of Possibilistic Truth Values.- Bipolar Queries Using Various Interpretations of Logical Connectives.- A Hierarchical Approach to Object Comparison.- FuzzyXPath: Using Fuzzy Logic an IR Features to Approximately Query XML Documents.- Philosophical and Human-Scientific Aspects of Soft Computing.- Designing Representative Bodies When the Voter Preferences Are Fuzzy.- Possibility Based Modal Semantics for Graded Modifiers.- New Perspective for Structural Learning Method of Neural Networks.- Search Engine and Information Processing and Retrieval.- Web Usage Mining Via Fuzzy Logic Techniques.- Deduction Engine Design for PNL-Based Question Answering System.- Granular Computing and Modeling the Human Thoughts in Web Documents.- Perception Based Data Mining and Decision Making.- Extracting Fuzzy Linguistic Summaries Based on Including Degree Theory and FCA.- Linguistic Summarization of Time Series by Using the Choquet Integral.- Visualization of Possibilistic Potentials.- Joint Model-Based and Data-Based Learning: The Fuzzy Logic Approach.- Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation.- Fuzzy Backpropagation Neural Networks for Nonstationary Data Prediction.- Fuzzy Model Based Iterative Learning Control for Phenol Biodegradation.- Fuzzy Modelling Methodologies for Large Database.- Fuzzy Possibilistic Optimization.- On Possibilistic/Fuzzy Optimization.- The Use of Interval-Valued Probability Measures in Optimization Under Uncertainty for Problems Containing a Mixture of Fuzzy, Possibilisitic, and Interval Uncertainty.- On Selecting an Algorithm for Fuzzy Optimization.- A Risk-Minimizing Model Under Uncertainty in Portfolio.- Fuzzy Trees.- Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset.- Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees.- Fuzzy Logic Theory.- Semi-Boolean and Hyper-Archimedean BL-Algebras.- A Fuzzy Hahn-Banach Theorem.- The Algebraic Properties of Linguistic Value "Truth" and Its Reasoning.- Fuzzy Subgroups with Meet Operation in the Connection of Mos Intuitionistic Fuzzy Sets as a Classification Model.- Classification with Nominal Data Using Intuitionistic Fuzzy Sets.- Intuitionistic Fuzzy Histograms of an Image.- Image Threshold Using A-IFSs Based on Bounded Histograms.- The Role of Entropy in Intuitionistic Fuzzy Contrast Enhancement.- Representation of Rough Sets Based on Intuitionistic Fuzzy Special Sets.- The Application of Fuzzy Logic and Soft Computing in Flexible Querying.- Towards Vague Query Answering in Logic Programming for Logic-Based Information Retrieval.- On Browsing Domain Ontologies for Information Base Content.- Fuzzy Tree Mining: Go Soft on Your Nodes.- Numerical Properties of Fuzzy Regions: Surface Area.- Qualification of Fuzzy Statements Under Fuzzy Certainty.- Weighted Conjunctive and Disjunctive Aggregation of Possibilistic Truth Values.- Bipolar Queries Using Various Interpretations of Logical Connectives.- A Hierarchical Approach to Object Comparison.- FuzzyXPath: Using Fuzzy Logic an IR Features to Approximately Query XML Documents.- Philosophical and Human-Scientific Aspects of Soft Computing.- Designing Representative Bodies When the Voter Preferences Are Fuzzy.- Possibility Based Modal Semantics for Graded Modifiers.- New Perspective for Structural Learning Method of Neural Networks.- Search Engine and Information Processing and Retrieval.- Web Usage Mining Via Fuzzy Logic Techniques.- Deduction Engine Design for PNL-Based Question Answering System.- Granular Computing and Modeling the Human Thoughts in Web Documents.- Perception Based Data Mining and Decision Making.- Extracting Fuzzy Linguistic Summaries Based on Including Degree Theory and FCA.- Linguistic Summarization of Time Series by Using the Choquet Integral.- Visualization of Possibilistic Potentials.- Joint Model-Based and Data-Based Learning: The Fuzzy Logic Approach.- Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation.- Fuzzy Backpropagation Neural Networks for Nonstationary Data Prediction.- Fuzzy Model Based Iterative Learning Control for Phenol Biodegradation.- Fuzzy Modelling Methodologies for Large Database.- Fuzzy Possibilistic Optimization.- On Possibilistic/Fuzzy Optimization.- The Use of Interval-Valued Probability Measures in Optimization Under Uncertainty for Problems Containing a Mixture of Fuzzy, Possibilisitic, and Interval Uncertainty.- On Selecting an Algorithm for Fuzzy Optimization.- A Risk-Minimizing Model Under Uncertainty in Portfolio.- Fuzzy Trees.- Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset.- Fuzziness and Performance: An Empirical Study with Linguistic Decision Trees.- Fuzzy Logic Theory.- Semi-Boolean and Hyper-Archimedean BL-Algebras.- A Fuzzy Hahn-Banach Theorem.- The Algebraic Properties of Linguistic Value "Truth" and Its Reasoning.- Fuzzy Subgroups with Meet Operation in the Connection of Mobius Transformations.- A Method for Automatic Membership Function Estimation Based on Fuzzy Measures.- Counting Finite Residuated Lattices.- On Proofs and Rule of Multiplication in Fuzzy Attribute Logic.- Graded Fuzzy Rules.- On External Measures for Validation of Fuzzy Partitions.- Coherence Index of Radial Conjunctive Fuzzy Systems.- Topology in Fuzzy Class Theory: Basic Notions.- Features of Mathematical Theories in Formal Fuzzy Logic.- A New Method to Compare Dynamical Systems.- Advances in the Geometrical Study of Rotation-Invariant T-Norms.- Fuzzy Reversed Posynomial Geometric Programming and Its Dual Form.- Posynomial Fuzzy Relation Geometric Programming.- Type-2 Fuzzy Logic.- A Vector Similarity Measure for Type-1 Fuzzy Sets.- On Approximate Representation of Type-2 Fuzzy Sets Using Triangulated Irregular Network.- Hybrid Control for an Autonomous Wheeled Mobile Robot Under Perturbed Torques.- Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition.- Fuzzy Logic Applications.- A Fuzzy Model for Olive Oil Sensory Evaluation.- An Interval-Based Index Structure for Structure Elucidation in Chemical Databases.- Fuzzy Cognitive Layer in RoboCupSoccer.- An Approach to Theory of Fuzzy Discrete Signals.- Using Gradual Numbers for Solving Fuzzy-Valued Combinatorial Optimization Problems.- Fuzzy Classifier with Probabilistic IF-THEN Rules.- Fuzzy Adaptive Search Method for Parallel Genetic Algorithm Tuned by Evolution Degree Based on Diversity Measure.- Fuzzy Controller for Robot Manipulators.- Collaboration Between Hyperheuristics to Solve Strip-Packing Problems.- Neural Networks and Control.- Discrete-Time Recurrent High Order Neural Observer for Induction Motors.- Strict Generalization in Multilayered Perceptron Networks.- Fault Tolerant Control of a Three Tank Benchmark Using Weighted Predictive Control.- Synchronization in Arrays of Chaotic Neural Networks.- Intelligent Agents and Knowledge Ant Colony.- On Fuzzy Projection-Based Utility Decomposition in Compound Multi-agent Negotiations.- Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating.- Ant Colony Optimization Applied to Feature Selection in Fuzzy Classifiers.- Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems.- Beam-ACO Distributed Optimization Applied to Supply-Chain Management.- A Cultural Algorithm with Operator Parameters Control for Solving Timetabling Problems.- On Control for Agents Formation.

Modeling and control of non-linear systems using soft computing techniques

Abstract: This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, ...) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.

Applications of Fuzzy Sets Theory: 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings ... / Lecture Notes in Artificial Intelligence)

Abstract: This volume constitutes the refereed proceedings of the 7th International Workshop on Fuzzy Logic and Applications held in Camogli, Genoa, Italy in July 2007. The 84 revised full papers presented together with 3 keynote speeches were carefully reviewed and selected from 147 submissions. The papers are organized in topical sections on fuzzy set theory, fuzzy information access and retrieval, fuzzy machine learning, fuzzy architectures and systems; and special sessions on intuitionistic fuzzy sets and soft computing in image processing. WILF 2007 hosts four special sessions, namely the Fourth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIDD 2007), the Third International Workshop on Cross-Language Information Processing (CLIP 2007); Intuitionistic Fuzzy Sets: Recent Advances (IFS), and Soft Computing in Image Processing (CLIPS). These special sessions extend and deepen the main topics of WILF.

Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques

Abstract: Early work on case-based reasoning (CBR) reported in the literature shows the importance of soft computing techniques applied to different stages of the classical four-step CBR life cycle. This correspondence proposes a reduction technique based on rough sets theory capable of minimizing the case memory by analyzing the contribution of each case feature. Inspired by the application of the minimum description length principle, the method uses the granularity of the original data to compute the relevance of each attribute. The rough feature weighting and selection method is applied as a preprocessing step prior to the generation of a fuzzy rule system, which is employed in the revision phase of the proposed CBR system. Experiments using real oceanographic data show that the rough sets reduction method maintains the accuracy of the employed fuzzy rules, while reducing the computational effort needed in its generation and increasing the explanatory strength of the fuzzy rules

Soft Computing for Knowledge Discovery and Data Mining

Abstract: Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook.This bookwas written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results. Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.

Intelligent systems in the automotive industry: applications and trends

Abstract: There is a common misconception that the automobile industry is slow to adapt new technologies, such as artificial intelligence (AI) and soft computing. The reality is that many new technologies are deployed and brought to the public through the vehicles that they drive. This paper provides an overview and a sampling of many of the ways that the automotive industry has utilized AI, soft computing and other intelligent system technologies in such diverse domains like manufacturing, diagnostics, on-board systems, warranty analysis and design.

Rough Computing: Theories, Technologies and Applications

Abstract: Rough set theory is a new soft computing tool which deals with vagueness and uncertainty It has attracted the attention of researchers and practitioners worldwide, and has been successfully applied to many fields such as knowledge discovery, decision support, pattern recognition, and machine learning Rough Computing: Theories, Technologies and Applications offers the most comprehensive coverage of key rough computing research, surveying a full range of topics from granular computing to pansystems theory With its unique coverage of the defining issues of the field, this commanding research collection provides libraries with a single, authoritative reference to this highly advanced technological topic

Analysis and Design of Intelligent Systems Using Soft Computing Techniques

Abstract: The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. P. Melin, O. Castillo, E.G. Ramírez, W. Pedrycz (Eds.) Analysis and Design of Intelligent Systems Using Soft Computing Techniques

Effective learning system techniques for human-robot interaction in service environment

Abstract: HRI (Human-Robot Interaction) is often frequent and intense in assistive service environment and it is known that realizing human-friendly interaction is a very difficult task because of human presence as a subsystem of the interaction process. After briefly discussing typical HRI models and characteristics of human, we point out that learning aspect would play an important role for designing the interaction process of the human-in-the loop system. We then show that the soft computing toolbox approach, especially with fuzzy set-based learning techniques, can be effectively adopted for modeling human behavior patterns as well as for processing human bio-signals including facial expressions, hand/ body gestures, EMG and so forth. Two project works are briefly described to illustrate how the fuzzy logic-based learning techniques and the soft computing toolbox approach are successfully applied for human-friendly HRI systems. Next, we observe that probabilistic fuzzy rules can handle inconsistent data patterns originated from human, and show that combination of fuzzy logic, fuzzy clustering, and probabilistic reasoning in a single frame leads to an algorithm of iterative fuzzy clustering with supervision. Further, we discuss a possibility of using the algorithm for inductively constructing probabilistic fuzzy rule base in a learning system of a smart home. Finally, we propose a life-long learning system architecture for the HRI type of human-in-the-loop systems.

Analysis of Biological Data: A Soft Computing Approach

Abstract: Basic Principles and Features of Soft Computing and Bioinformatics: Bioinformatics: Tasks and Challenges Soft Computing: Tools and Techniques Biological Sequence Analysis: Combining Hidden Markov Model and Artificial Neural Networks for DNA Sequencing Multiple Sequence Alignment Using Tabu Search and Genetic Algorithms Molecular Structure Prediction and Drug Design: Protein Folding with Multi-Objective Evolutionary Algorithms and Neural Networks In Silico Drug Design Using Evolutionary Approach Clustering and Classification Tasks in Bioinformatics: Clustering of Microarray Data: Pattern Recognition and Soft Computing Approach Soft Computing Techniques for Protein Classification and other papers

Forging New Frontiers: Fuzzy Pioneers II

Abstract: The 2005 BISC International Special Event-BISCSE 05 'FORGING THE FRONTIERS' was held in the University of California, Berkeley, WHERE FUZZY LOGIC BEGAN, from November 3 through 6, 2005. The successful applications of fuzzy logic and it s rapid growth suggest that the impact of fuzzy logic will be felt increasingly in coming years. Fuzzy logic is likely to play an especially important role in science and engineering, but eventually its influence may extend much farther. In many ways, fuzzy logic represents a significant paradigm shift in the aims of computing - a shift which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity. The chapters of the book are evolved from presentations made by selected participants at the meeting and organized in two books. The papers include reports from the different front of soft computing in various industries and address the problems of different fields of research in fuzzy logic, fuzzy set and soft computing. The book provides a collection of forty four (44) articles in two volumes.

A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing

Abstract: The ink drop spread (IDS) method is a modeling technique developed by algorithmically mimicking the information-handling processes of the human brain. This method has been proposed as a new approach to soft computing. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergences. This paper investigates the modeling ability of the IDS method based on three typical benchmarks. Experimental results demonstrated that the IDS method can handle various modeling targets, ranging from logic operations to complex nonlinear systems, and that its modeling performance is satisfactory in comparison with that of feedforward neural networks.

A cumulative evidential stopping criterion for multiobjective optimization evolutionary algorithms

Abstract: In this work we present a novel and efficient algorithm independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorithms (MOEAs). The criterion, after each iteration of the optimization algorithm, gathers evidence of the improvement of the solutions obtained so far. A global (execution wise) evidence accumulation process inspired by recursive Bayesian estimation decides when the optimization should be stopped. Evidenceis collected using a novel relative improvement measure constructed on top of the Pareto dominance relations. The evidence gathered after each iteration is accumulated and updated following a rule based on a simplified version of a discrete Kalman filter. Our criterion is particularly useful in complex and/or high-dimensional problems where the traditional procedure of stopping after a predefined amount of iterations cannot be used and the waste of computational resources can induceto a detriment of the quality of the results. Although the criterion discussed here is meant for MOEAs,it can be easily adapted to other soft computing or numerical methods by substituting the local improvement metric with a suitable one.

Daily groundwater level fluctuation forecasting using soft computing technique

Abstract: The study presented here deals with forecasting daily groundwater level fluctuation (GLF) for monitoring of GLF pattern. The calculation model is based on the adaptive neuro-fuzzy inference system (ANFIS) and two algorithms of artificial neural networks (ANN) models, namely Levenberg- Marquardt (LM) and radial basis function (RBF). The objective in this study is to predict daily GLF for monitoring purposes. The first step was to investigate the effect of the number time lags as inputs for one- day-ahead prediction using the ANFIS algorithm. It was found that three input nodes containing three time- lag of well studied gave good prediction results. The second experiment was to predict the GLF one to seven steps ahead using the three input nodes. In this experiment, the three soft computing techniques were applied. The results indicate that the performances were decreasing by increasing the time step ahead, and in general there was no significant difference between the three techniques used. The best accuracy was for one-day-ahead prediction. The results obtained in this study suggest that GLF monitoring can be conducted by a forecasting model with considering time-lag as inputs. (Nature and Science. 2007;5(2):1-10).

Soft Computing Techniques Applied to Combinatorial Catalysis: A New Approach for the Discovery and Optimization of Catalytic Materials

Abstract: A soft computing technique based on the combination of Artificial Neural Networks (ANNs) and a Genetic Algorithm (GA) has been developed for the discovery and optimization of new materials when exploring a high-dimensional space. This technique allows the experimental design in the search of new solid materials with high catalytic performance when exploring simultaneously a large number of variables such as elemental composition, manufacture procedure variables, etc. This novel integrated architecture allows one to strongly increase the convergence performance when compared with the performance of conventional GAs. It is described how both artificial intelligence techniques are built to work together. Moreover, the influence of algorithm configuration and the different algorithm parameters in the final optimization performance have been evaluated. The proposed optimization architecture has been validated using two hypothetical functions, based on the modeled behavior of multi-component catalysts explored in the field of combinatorial catalysis.

Innovations in Hybrid Intelligent Systems

Abstract: Hybrid Artificial Intelligence Systems.- Hybrid Artificial Intelligence Systems.- Agents and Multiagent Systems.- Analysis of Emergent Properties in a Hybrid Bio-inspired Architecture for Cognitive Agents.- Using Semantic Causality Graphs to Validate MAS Models.- A Multiagent Framework to Animate Socially Intelligent Agents.- Context Aware Hybrid Agents on Automated Dynamic Environments.- Sensitive Stigmergic Agent Systems - A Hybrid Approach to Combinatorial Optimization.- Fuzzy Systems.- Agent-Based Social Modeling and Simulation with Fuzzy Sets.- Stage-Dependent Fuzzy-valued Loss Function in Two-Stage Binary Classifier.- A Feature Selection Method Using a Fuzzy Mutual Information Measure.- Interval Type-2 ANFIS.- A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms.- Artificial Neural Networks.- Development of Multi-output Neural Networks for Data Integration - A Case Study.- Combined Projection and Kernel Basis Functions for Classification in Evolutionary Neural Networks.- Modeling Heterogeneous Data Sets with Neural Networks.- A Computational Model of the Equivalence Class Formation Psychological Phenomenon.- Data Security Analysis Using Unsupervised Learning and Explanations.- Finding Optimal Model Parameters by Discrete Grid Search.- Clustering and Multiclassfier Systems.- A Hybrid Algorithm for Solving Clustering Problems.- Clustering Search Heuristic for the Capacitated p-Median Problem.- Experiments with Trained and Untrained Fusers.- Fusion of Visualization Induced SOM.- Robots.- Open Intelligent Robot Controller Based on Field-Bus and RTOS.- Evolutionary Controllers for Snake Robots Basic Movements.- Evolution of Neuro-controllers for Multi-link Robots.- Solving Linear Difference Equations by Means of Cellular Automata.- Metaheuristics and Optimization Models.- Automated Classification Tree Evolution Through Hybrid Metaheuristics.- Machine Learning to Analyze Migration Parameters in Parallel Genetic Algorithms.- Collaborative Evolutionary Swarm Optimization with a Gauss Chaotic Sequence Generator.- A New PSO Algorithm with Crossover Operator for Global Optimization Problems.- Solving Bin Packing Problem with a Hybridization of Hard Computing and Soft Computing.- Design of Artificial Neural Networks Based on Genetic Algorithms to Forecast Time Series.- Experimental Analysis for the Lennard-Jones Problem Solution.- Application of Genetic Algorithms to Strip Hot Rolling Scheduling.- Synergy of PSO and Bacterial Foraging Optimization - A Comparative Study on Numerical Benchmarks.- Artificial Vision.- Bayes-Based Relevance Feedback Method for CBIR.- A Novel Hierarchical Block Image Retrieval Scheme Based Invariant Features.- A New Unsupervised Hybrid Classifier for Natural Textures in Images.- Visual Texture Characterization of Recycled Paper Quality.- Case-Based Reasoning.- Combining Improved FYDPS Neural Networks and Case-Based Planning - A Case Study.- CBR Contributions to Argumentation in MAS.- Case-Base Maintenance in an Associative Memory Organized by a Self-Organization Map.- Hybrid Multi Agent-Neural Network Intrusion Detection with Mobile Visualization.- Learning Models.- Knowledge Extraction from Environmental Data Through a Cognitive Architecture.- A Model of Affective Entities for Effective Learning Environments.- Bioinformatics.- Image Restoration in Electron Cryotomography - Towards Cellular Ultrastructure at Molecular Level.- SeqTrim - A Validation and Trimming Tool for All Purpose Sequence Reads.- A Web Tool to Discover Full-Length Sequences - Full-Lengther.- Discovering the Intrinsic Dimensionality of BLOSUM Substitution Matrices Using Evolutionary MDS.- Autonomous FYDPS Neural Network-Based Planner Agent for Health Care in Geriatric Residences.- Structure-Preserving Noise Reduction in Biological Imaging.- Ensemble of Support Vector Machines to Improve the Cancer Class Prediction Based on the Gene Expression Profiles.- NATPRO-C13 - An Interactive Tool for the Structural Elucidation of Natural Compounds.- Application of Chemoinformatic Tools for the Analysis of Virtual Screening Studies of Tubulin Inhibitors.- Identification of Glaucoma Stages with Artificial Neural Networks Using Retinal Nerve Fibre Layer Analysis and Visual Field Parameters.- Dimensional Reduction in the Protein Secondary Structure Prediction - Nonlinear Method Improvements.- Other Applications.- Focused Crawling for Retrieving Chemical Information.- Optimal Portfolio Selection with Threshold in Stochastic Market.- Classification Based on Association Rules for Adaptive Web Systems.- Statistical Selection of Relevant Features to Classify Random, Scale Free and Exponential Networks.- Open Partner Grid Service Architecture in eBusiness.- An Architecture to Support Programming Algorithm Learning by Problem Solving.- Explain a Weblog Community.- Implementing Data Mining to Improve a Game Board Based on Cultural Algorithms.

Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks

Abstract: Mechanical properties of any material are extensively influenced by the parameters such as strain, strain rate, temperature, and its composition. The characteristics of any material such as ductility, strain hardening, strength, dynamic recovery, grain growth, and recrystallization are greatly affected by the influence of various process parameters. So, it is essential to have the knowledge of the constitutive relationships that relate different process variables to flow stress of the deforming material which estimates various parameters such as load, energy, and stress in the metal forming operations. A consistent effort has been gone into developing the constitutive equations for the detailed mathematical description of the flow curves and the aforementioned parameters for years now. Soft computing tools that concern computation of an imprecise environment and model very complex systems those are based on input-output relationship have gained significant attention in recent years. The intricacies of the mathematical modeling of the mechanical properties of the material, enticed the artificial research community to take this as a challenge. One such soft computing tool neural network is applied in this field to predict the behavior accurately. In this paper, a hybrid neural network model better known as RSONFN (Recurrent Self-Organizing Neural Network Model) is applied to predict the flow stress for carbon steels. The RSONFN is having the advantages of the well-established technologies of the artificial intelligence tools such as Fuzzy logic to capture long range data sets and neural networks. The RSONFN structure is a dynamic one as the numbers of its layers as well as the numbers of nodes in each layer of the network are not predetermined. Such an attribute differentiate it from the Multilayer perceptron which is having static structure. The results obtained by this network prove its superiority over other existing tools.

Novel Architecture for supporting medical decision making of different data types based on Fuzzy Cognitive Map Framework

Abstract: Medical problems involve different types of variables and data, which have to be processed, analyzed and synthesized in order to reach a decision and/or conclude to a diagnosis. Usually, information and data set are both symbolic and numeric but most of the well-known data analysis methods deal with only one kind of data. Even when fuzzy approaches are considered, which are not depended on the scales of variables, usually only numeric data is considered. The medical decision support methods usually are accessed in only one type of available data. Thus, sophisticated methods have been proposed such as integrated hybrid learning approaches to process symbolic and numeric data for the decision support tasks. Fuzzy cognitive maps (FCM) is an efficient modelling method, which is based on human knowledge and experience and it can handle with uncertainty and it is constructed by extracted knowledge in the form of fuzzy rules. The FCM model can be enhanced if a fuzzy rule base (IF-THEN rules) is available. This rule base could be derived by a number of machine learning and knowledge extraction methods. Here it is introduced a hybrid attempt to handle situations with different types of available medical and /or clinical data and with difficulty to handle them for decision support tasks using soft computing techniques.