Soft Computing in Industrial Applications: Recent and Emerging Methods and TechniquesAshraf Saad, Erel Avineri, Keshav Dahal, Muhammad Sarfraz, Rajkumar Roy Soft Computing admits approximate reasoning, imprecision, uncertainty and partial truth in order to mimic aspects of the remarkable human capability of making decisions in real-life and ambiguous environments. "Soft Computing in Industrial Applications" contains a collection of papers that were presented at the 11th On-line World Conference on Soft Computing in Industrial Applications, held in September-October 2006. This carefully edited book provides a comprehensive overview of the recent advances in the industrial applications of soft computing and covers a wide range of application areas, including data analysis and data mining, computer graphics, intelligent control, systems, pattern recognition, classifiers, as well as modeling optimization. The book is aimed at researchers and practitioners who are engaged in developing and applying intelligent systems principles to solving real-world problems. It is also suitable as wider reading for science and engineering postgraduate students. |
Contents
1 | |
17 | |
19 | |
A DoctorComputer Sterile Gesture Interface for Dynamic Environments | 30 |
Differential Evolution for the Registration of Remotely Sensed Images | 40 |
Geodesic Distance Based Fuzzy Clustering | 50 |
Part II Control Systems | 60 |
Stability Analysis of the Simplest TakagiSugeno Fuzzy Control System Using Popov Criterion | 63 |
A Cooperative Learning Model for the Fuzzy ARTMAPDynamic Decay Adjustment Network with the Genetic Algorithm | 169 |
A Modified Fuzzy MinMax Neural Network and Its Application to Fault Classification | 179 |
An Adaptive Fuzzy ECG Classifier | 189 |
A Selforganizing Fuzzy Neural Networks | 200 |
Part V Soft Computing for Modeling Optimization and Information Processing | 211 |
A Particle Swarm Approach to Quadratic Assignment Problems | 212 |
PopulationBased Incremental Learning for Multiobjective Optimisation | 223 |
Combining of Differential Evolution and Implicit Filtering Algorithm Applied to Electromagnetic Design Optimization | 233 |
Identification of an Experimental Process by BSpline Neural Network Using Improved Differential Evolution Training | 72 |
Applying Particle Swarm Optimization to Adaptive Controller | 82 |
BSpline Neural Network Using an Artificial Immune Network Applied to Identification of a BallandTube Prototype | 92 |
Part III Pattern Recognition | 102 |
Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks | 105 |
Application of a GABayesian FilterWrapper Feature Selection Method to Classification of Clinical Depression from Speech Data | 115 |
Comparison of PSOBased Optimized Feature Computation for Automated Configuration of Multisensor Systems | 122 |
Evaluation of Objective Features for Classification of Clinical Depression in Speech by Genetic Programming | 132 |
Spline Kernel Based Machine Learning Tool | 144 |
Part IV Classification | 156 |
Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality | 159 |
A Layered Matrix Cascade Genetic Algorithm and Particle Swarm Optimization Approach to Thermal Power Generation Scheduling | 241 |
Differential Evolution for Binary Encoding | 251 |
Part VI Soft Computing in Civil Engineering and Other Applications | 263 |
Prioritization of Pavement Stretches Using Fuzzy MCDM Approach A Case Study | 265 |
A Memetic Algorithm for Water Distribution Network Design | 279 |
A Comparative Study | 290 |
Recessive Trait Cross over Approach of GAs Population Inheritance for Evolutionary Optimization | 306 |
Automated Prediction of Solar Flares Using Neural Networks and Sunspots Associations | 316 |
Keyword Index | 325 |
Author Index | 327 |