Simulation-driven design optimization and modeling for microwave engineering / edited by Slawomir Koziel, Xin-She Yang, Qi-Jun Zhang.

Format:

Book

Contributor:

Koziel, Slawomir.

Yang, Xin-She.

Zhang, Q. J.

Series:

Gale eBooks

Language:

English

Subjects (All):

Microwave devices--Mathematical models.

Microwave devices.

Microwave circuits--Mathematical models.

Microwave circuits.

Physical Description:

1 online resource (xxiii, 501 pages) : illustrations

Place of Publication:

London : Imperial College Press, 2013.

Language Note:

English

Summary:

Computer-aided full-wave electromagnetic (EM) analysis has been used in microwave engineering for the past decade. Initially, its main application area was design verification. Today, EM-simulation-driven optimization and design closure become increasingly important due to the complexity of microwave structures and increasing demands for accuracy. In many situations, theoretical models of microwave structures can only be used to yield the initial designs that need to be further fine-tuned to meet given performance requirements. In addition, EM-based design is a must for a growing number of mic

Contents:

List of Contributors; Preface; Acknowledgments; Contents; 1. Introduction to Optimization and Gradient-Based Methods Xin-She Yang and Slawomir Koziel; 1.1 Introduction; 1.2 Main Challenges in Optimization; 1.2.1 Efficiency of an Algorithm; 1.2.2 The Right Algorithms?; 1.2.3 Efficiency of a Numerical Solver; 1.3 Gradient-Based Methods; 1.3.1 Newton's Method; 1.3.2 Steepest-Descent Method; 1.3.3 Line Search; 1.3.4 Conjugate Gradient Method; 1.3.5 BFGS Method; 1.3.6 Trust-Region Method; 1.4 Quadratic Programming; 1.4.1 Quadratic Programming; 1.4.2 Sequential Quadratic Programming; References

2. Derivative-Free Methods and Metaheuristics Xin-She Yang and Slawomir Koziel2.1 Derivative-Free Methods; 2.1.1 Pattern Search; 2.1.2 Nelder-Mead's Simplex Method; 2.1.3 Surrogate-Based Methods; 2.1.4 Contemporary Derivative-Free Methods; 2.2 Metaheuristics; 2.2.1 Ant Algorithms; 2.2.2 Bee Algorithms; 2.2.3 Bat Algorithm; 2.2.4 Simulated Annealling; 2.2.5 Genetic Algorithms; 2.2.6 Differential Evolution; 2.2.7 Particle Swarm Optimization; 2.2.8 Harmony Search; 2.2.9 Firefly Algorithm; 2.2.10 Cuckoo Search; 2.2.11 Other Algorithms; References

3. Surrogate-Based Optimization Slawomir Koziel, Leifur Leifsson, and Xin-She Yang3.1 Introduction; 3.2 Surrogate-Based Optimization Concept; 3.2.1 Direct Optimization; 3.2.2 Surrogate-Based Optimization; 3.3 Surrogate-Based Optimization Techniques; 3.3.1 Approximation Model Management Optimization; 3.3.2 Space Mapping; 3.3.3 Manifold Mapping; 3.3.4 Surrogate Management Framework; 3.3.5 Other Techniques; 3.3.6 Exploration vs. Exploitation; 3.4 Approximation-Based Surrogate Models; 3.4.1 Surrogate Construction Overview; 3.4.2 Design of Experiments; 3.4.3 Polynomial Regression

3.4.4 Radial Basis Functions3.4.5 Kriging; 3.4.6 Neural Networks; 3.4.7 Support Vector; 3.4.8 Other Regression Techniques; 3.4.9 Model Validation; 3.5 Physics-Based Surrogate Models; 3.5.1 The Modeling Concept; 3.5.2 Objective Function Correction; 3.5.3 Space Mapping; 3.5.4 Other Techniques; 3.6 Conclusion; References; 4. Space Mapping Slawomir Koziel, Stanislav Ogurtsov, Qingsha S. Cheng, and John W. Bandler; 4.1 Space Mapping: Concept and Historical Overview; 4.2 Space Mapping Formulation and Algorithms; 4.2.1 Space Mapping Concept; 4.2.2 Aggressive Space Mapping

4.2.3 Parametric Space Mapping: Input, Implicit, Output, and Others4.2.4 Space Mapping Illustration; 4.2.4.1. Wedge-cutting problem solved using input space mapping; 4.2.4.2. Wedge-cutting problem solved using implicit space mapping; 4.2.5 Practical Issues and Open Problems; 4.2.6 Trust-Region Space Mapping; 4.3 Application Example; 4.4 Conclusions; Acknowledgement; References; 5. Tuning Space Mapping Qingsha S. Cheng, John W. Bandler, and Slawomir Koziel; 5.1 Tuning Technology; 5.2 EM-Simulator-Based Tuning; 5.3 Introduction to Tuning Space Mapping; 5.4 General Tuning Space Mapping Algorithm

5.5 Types of Tuning Model

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9781299462175

1299462170

9781848169173

1848169175

OCLC:

839386975