Soft Computing in Industrial Applications: Recent and Emerging Methods and Techniques

Front Cover
Ashraf Saad, Erel Avineri, Keshav Dahal, Muhammad Sarfraz, Rajkumar Roy
Springer Science & Business Media, Aug 7, 2007 - Computers - 330 pages

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.

From inside the book

Contents

Hybrid Dynamic Systems in an Industry Design Application
1
Part I Soft Computing in Computer Graphics Imaging and Vision
17
Object Recognition Using Particle Swarm Optimization on Fourier Descriptors
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
Copyright

Other editions - View all

Common terms and phrases

Popular passages

Page 109 - Speaker identification is the process of determining from which of the registered speakers a given utterance comes.
Page 108 - NNs. The average load on each NN is reduced in comparison with a single NN that must learn the entire original task, and thus the combined model may be able to surpass the limitation of a single NN. The outputs of a certain number of local experts (Oj) are mediated by an integration unit.
Page 51 - ALGORITHMS the cluster. The prototypes are usually not known beforehand, and are sought by the clustering algorithms simultaneously with the partitioning of the data. The prototypes may be vectors of the same dimension as the data objects, but they can also be defined as "higher-level...
Page 124 - Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics.
Page 108 - Modular Neural Networks This section describes a particular class of "modular neural networks", which have a hierarchical organization comprising multiple neural networks; the architecture basically consists of two principal components: local experts and an integration unit, as illustrated in Figure 2.
Page 50 - Various definitions of a cluster can be formulated, depending on the objective of clustering. Generally, one may accept the view that a cluster is a group of objects that are more similar to one another than to members of other clusters. The term "similarity" should be understood as mathematical similarity, measured in some well-defined sense.
Page 151 - The dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other two; the latter are NOT linearly separable from each other.
Page 86 - If the current value is better than pbest, then set the pbest value equal to the current value and the pbest location equal to the current location in n-dimensional space.
Page 86 - If current value is better than gbest, then reset gbest to the current particle's array index and value. 5. Change the velocity and position of the particle according to equations (9) and (10) respectively [13,14]: .rand().(pi - V|) + c2.Rand().(pg - V|) (9) (10) 6.

Bibliographic information