Muutke küpsiste eelistusi

E-raamat: Genetic Algorithms in Electromagnetics [Wiley Online]

(The Pennsylvania State University), (The Pennsylvania State University)
  • Formaat: 320 pages, Tables: 0 B&W, 0 Color
  • Sari: IEEE Press
  • Ilmumisaeg: 26-Apr-2007
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 047010628X
  • ISBN-13: 9780470106280
Teised raamatud teemal:
  • Wiley Online
  • Hind: 157,54 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Genetic Algorithms in ElectromagneticsSuurem pilt
  • Formaat: 320 pages, Tables: 0 B&W, 0 Color
  • Sari: IEEE Press
  • Ilmumisaeg: 26-Apr-2007
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 047010628X
  • ISBN-13: 9780470106280
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/047010628X
For readers who have already learned how to formulate the objective function to solve particular problems in electromechanics, Haupt and Werner (electrical engineering, both Pennsylvania State U.) explain methods for optimizing the objective function--the computer algorithm, analytical model, or experimental result that describes the performance of an electromagnetic system. As they main tool, they introduce genetic algorithms, which they say have proved to be tenacious in finding optimal results when tradition approaches fail. MATLAB code is appended for kids brave enough to try it at home. Annotation ©2007 Book News, Inc., Portland, OR (booknews.com)

A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems

Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail.

Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature:

  • Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands
  • Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems
  • Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles
  • Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole"
  • How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas
  • Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials
  • Ideas on operator and parameter selection for a GA
  • Detailed explanations of particle swarm optimization and multiple objective optimization
  • An appendix of MATLAB code for experimentation
Preface.
Acknowledgments.
1. Introduction to Optimization in Electromagnetics.
1.1 Optimizing a Function of One Variable.
1.1.1 Exhaustive Search.
1.1.2 Random Search.
1.1.3 Golden Search.
1.1.4 Newton’s Method.
1.1.5 Quadratic Interpolation.
1.2 Optimizing a Function of Multiple Variables.
1.2.1 Random Search.
1.2.2 Line Search.
1.2.3 Nelder–Mead Downhill Simplex Algorithm.
1.3 Comparing Local Numerical Optimization Algorithms.
1.4 Simulated Annealing.
1.5 Genetic Algorithm.
2. Anatomy of a Genetic Algorithm.
2.1 Creating an Initial Population.
2.2 Evaluating Fitness.
2.3 Natural Selection.
2.4 Mate Selection.
2.4.1 Roulette Wheel Selection.
2.4.2 Tournament Selection.
2.5 Generating Offspring.
2.6 Mutation.
2.7 Terminating the Run.
3. Step-by-Step Examples.
3.1 Placing Nulls.
3.2 Thinned Arrays.
4. Optimizing Antenna Arrays.
4.1 Optimizing Array Amplitude Tapers.
4.2 Optimizing Array Phase Tapers.
4.2.1 Optimum Quantized Low-Sidelobe Phase Tapers.
4.2.2 Phase-Only Array Synthesis Using Adaptive GAs.
4.3 Optimizing Arrays with Complex Weighting.
4.3.1 Shaped-Beam Synthesis.
4.3.2 Creating a Plane Wave in the Near Field.
4.4 Optimizing Array Element Spacing.
4.4.1 Thinned Arrays.
4.4.2 Interleaved Thinned Linear Arrays.
4.4.3 Array Element Perturbation.
4.4.4 Aperiodic Fractile Arrays.
4.4.5 Fractal–Random and Polyfractal Arrays.
4.4.6 Aperiodic Refl ectarrays.
4.5 Optimizing Conformal Arrays.
4.6 Optimizing Reconfi gurable Apertures.
4.6.1 Planar Reconfi gurable Cylindrical Wire Antenna Design.
4.6.2 Planar Reconfi gurable Ribbon Antenna Design.
4.6.3 Design of Volumetric Reconfi gurable Antennas.
4.6.4 Simulation Results—Planar Reconfi gurable Cylindrical Wire Antenna.
4.6.5 Simulation Results—Volumetric Reconfi gurable Cylindrical Wire Antenna.
4.6.6 Simulation Results—Planar Reconfi gurable Ribbon Antenna.
5. Smart Antennas Using a GA.
5.1 Amplitude and Phase Adaptive Nulling.
5.2 Phase-Only Adaptive Nulling.
5.3 Adaptive Reflector.
5.4 Adaptive Crossed Dipoles.
6. Genetic Algorithm Optimization of Wire Antennas.
6.1 Introduction.
6.2 GA Design of Electrically Loaded Wire Antennas.
6.3 GA Design of Three-Dimensional Crooked-Wire Antennas.
6.4 GA Design of Planar Crooked-Wire and Meander-Line Antennas.
6.5 GA Design of Yagi–Uda Antennas.
7. Optimization of Aperture Antennas.
7.1 Refl ector Antennas.
7.2 Horn Antennas.
7.3 Microstrip Antennas.
8. Optimization of Scattering.
8.1 Scattering from an Array of Strips.
8.2 Scattering from Frequency-Selective Surfaces.
8.2.1 Optimization of FSS Filters.
8.2.2 Optimization of Reconfi gurable FSSs.
8.2.3 Optimization of EBGs.
8.3 Scattering from Absorbers.
8.3.1 Conical or Wedge Absorber Optimization.
8.3.2 Multilayer Dielectric Broadband Absorber Optimization.
8.3.3 Ultrathin Narrowband Absorber Optimization.
9. GA Extensions.
9.1 Selecting Population Size and Mutation Rate.
9.2 Particle Swarm Optimization (PSO).
9.3 Multiple-Objective Optimization.
9.3.1 Introduction.
9.3.2 Strength Pareto Evolutionary Algorithm—Strength Value Calculation.
9.3.3 Strength Pareto Evolutionary Algorithm—Pareto Set Clustering.
9.3.4 Strength Pareto Evolutionary Algorithm—Implementation.
9.3.5 SPEA-Optimized Planar Arrays.
9.3.6 SPEA-Optimized Planar Polyfractal Arrays.
Appendix: MATLAB(r) Code.
Bibliography.
Index.


Randy L. Haupt, PhD, is a senior scientist in the Applied Research Laboratory at The Pennsylvania State University. Douglas H. Werner, PhD, is a professor of electrical engineering, a senior scientist in the Applied Research Laboratory, and a faculty member of the Materials Research Institute at The Pennsylvania State University.
Püsilink: https://www.kriso.ee/db/9780470106280_pe.html
Märksõnad: