Waszczyszyn Advances of Soft Computing in Engineering

2010
ISBN: 978-3-211-99768-0
Verlag: Springer Vienna
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 512, 336 Seiten

Reihe: CISM International Centre for Mechanical Sciences

The articles in this book present advanced soft methods related to genetic and evolutionary algorithms, immune systems, formulation of deterministic neural networks and Bayesian NN. Many attention is paid to hybrid systems for inverse analysis fusing soft methods and the finite element method. Numerical efficiency of these soft methods is illustrated on the analysis and design of complex engineering structures.

Autoren/Hrsg.

Waszczyszyn, Zenon

Inhaltsverzeichnis

1;PREFACE;6
2;CONTENTS;7
3;CHAPTER 1 Genetic Algorithms for Design;8
3.1;1 Introduction;8
3.2;2 The Canonical Genetic Algorithm;10
3.3;3 Using Genetic Algorithms for Design;25
3.4;4 Genetic Algorithms for Design;47
3.5;Bibliography;62
4;CHAPTER 2 Evolutionary and Immune Computations in Optimal Design and Inverse Problems*;64
4.1;1 Introduction;64
4.2;2 Parallel and distributed evolutionary algorithms;68
4.3;3 Geometry Modeling;70
4.4;4 The evolutionary computations in optimization of structures under dynamical loading;74
4.5;5 Evolutionary Optimization and Identification in Thermomechanical Problems;78
4.6;6 Distributed Evolutionary Algorithm in Optimization of Nonlinear Structures;87
4.7;7 Topological Optimization of 2-D and 3D Structures Using Evolutionary Computing;96
4.8;8 Evolutionary Multiobjective Optimization;118
4.9;9 Immune Optimization;126
4.10;Bibliography;136
5;CHAPTER 3 Applications of GA and GP to Industrial Design Optimization and Inverse Problems;140
5.1;1 Introduction;140
5.2;2 Weight Optimization of a Formula One Car Composite Component Using Genetic Algortithm;142
5.3;3 Application of Optimization Techniques to Structural Damage Recognition;148
5.4;4 The Use of Permutation GA for the Development of Uniform Designs of Experiments;158
5.5;5 Use of Genetic Programming Methodology for Metamodel Building;169
5.6;6 Use of Genetic Programming for Recognition of Damage in Steel Structures;179
5.7;7 Multicriteria Optimization of the Manufacturing Process for Roman Cement using Genetic Programming;181
5.8;8 Empirical Modelling of Shear Strength of Reinforced Deep Beams by Genetic Programming ( Ashour et al.2003);185
5.9;References;192
6;CHAPTER 4 Advances in Neural Networks in Computational Mechanics and Engineering;197
6.1;1 Introduction;197
6.2;2 Computing and Problem Solving in Nature;198
6.3;3 Mechanics, Computation and Computational Mechanics;201
6.4;4 Hard and Soft Computing Methods;203
6.5;5 Neural Networks as Soft Computing Tools;206
6.6;6 Neural Networks in Material Modeling;212
6.7;7. Inverse Problems in Engineering;230
6.8;References;243
7;CHAPTER 5 Selected Problems of Artificial Neural Networks Development;248
7.1;1 Introduction;248
7.2;2 Regression, Over-Fitting and Regularization;250
7.3;3 Some Problems of ANNs Design;259
7.4;4 Applications of Kalman Filtering to Learning of ANNs;268
7.5;5 Bayesian Neural Networks;275
7.6;6 Applications of ANNs to the Analysis of Selected Engineering Problems;303
7.7;Bibliography;322
7.8;Appendices Appendices Appendices Appendices Appendices Appendices;326
8;CHAPTER 6 Neural Networks: Some Successful Applications in Computational Mechanics;328
8.1;1 Introduction;328
8.2;2 Multi-layer Perceptrons;329
8.3;3 Fragility Analysis using Monte Carlo Simulation;332
8.4;4 NN-based Seismic Fragility Analysis;336
8.5;5 Metamodel Assisted Methodology for Validating the EC8 Approach;340
8.6;6 Conclusions;345
8.7;References;346