Saad / Avineri / Dahal Soft Computing in Industrial Applications

Recent and Emerging Methods and Techniques
1. Auflage 2007
ISBN: 978-3-540-70706-6
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 330 Seiten

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 11 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.

Autoren/Hrsg.

Saad, Ashraf

Avineri, Erel

Dahal, Keshav

Sarfraz, Muhammad

Roy, Rajkumar

Inhaltsverzeichnis

1;Preface;6
2;Mesage from the WSC11 General Chair and Program Chair;7
3;WSC11 Organization and International Program Commitee;8
4;Contents;10
5;Hybrid Dynamic Systems in an Industry Design Application;14
6;Soft Computing in Computer Graphics, Imaging and Vision;30
6.1;Object Recognition Using Particle Swarm Optimization on Fourier Descriptors;31
6.2;Gestix: A Doctor-Computer Sterile Gesture Interface for Dynamic Environments;42
6.3;Differential Evolution for the Registration of Remotely Sensed Images;52
6.4;Geodesic Distance Based Fuzzy Clustering;62
7;Control Systems;72
7.1;Stability Analysis of the Simplest Takagi-Sugeno Fuzzy Control System Using Popov Criterion;73
7.2;Identification of an Experimental Process by B-Spline Neural Network Using Improved Differential Evolution Training;82
7.3;Applying Particle Swarm Optimization to Adaptive Controller;92
7.4;B-Spline Neural Network Using an Artificial Immune Network Applied to Identification of a Ball- and- Tube Prototype;102
8;Pattern Recognition;112
8.1;Pattern Recognition for Industrial Security Using the Fuzzy Sugeno Integral and Modular Neural Networks;113
8.2;Application of a GA/Bayesian Filter-Wrapper Feature Selection Method to Classification of Clinical Depression from Speech Data;123
8.3;Comparison of PSO-Based Optimized Feature Computation for Automated Configuration of Multi- sensor Systems;130
8.4;Evaluation of Objective Features for Classification of Clinical Depression in Speech by Genetic Programming;140
8.5;A Computationally Efficient SUPANOVA: Spline Kernel Based Machine Learning Tool;152
9;Classification;164
9.1;Multiobjective Genetic Programming Feature Extraction with Optimized Dimensionality;165
9.2;A Cooperative Learning Model for the Fuzzy ARTMAP- Dynamic Decay Adjustment Network with the Genetic Algorithm;175
9.3;A Modified Fuzzy Min-Max Neural Network and Its Application to Fault Classification;185
9.4;AFC-ECG: An Adaptive Fuzzy ECG Classifier;195
9.5;A Self-organizing Fuzzy Neural Networks;206
10;Soft Computing for Modeling, Optimization and Information Processing;217
10.1;A Particle Swarm Approach to Quadratic Assignment Problems;218
10.2;Population-Based Incremental Learning for Multiobjective Optimisation;228
10.3;Combining of Differential Evolution and Implicit Filtering Algorithm Applied to Electromagnetic Design Optimization;238
10.4;A Layered Matrix Cascade Genetic Algorithm and Particle Swarm Optimization Approach to Thermal Power Generation Scheduling;246
10.5;Differential Evolution for Binary Encoding;256
11;Soft Computing in Civil Engineering and Other Applications;268
11.1;Prioritization of Pavement Stretches Using Fuzzy MCDM Approach – A Case Study;269
11.2;A Memetic Algorithm for Water Distribution Network Design;283
11.3;Neural Network Models for Air Quality Prediction: A Comparative Study;294
11.4;Recessive Trait Cross over Approach of GAs Population Inheritance for Evolutionary Optimization;310
11.5;Automated Prediction of Solar Flares Using Neural Networks and Sunspots Associations;320
12;Keyword Index;329
13;Author Index;331