This book presents research focused on the design of fractal antennas using bio-inspired computing techniques. The authors present designs for fractal antennas having desirable features like size reduction characteristics, enhanced gain, and improved bandwidths. The research is summarized in six chapters which highlight the important issues related to fractal antenna design and the mentioned computing techniques. Chapters demonstrate several applied concepts and techniques used in the process such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The work aims to provide cost-effective and efficient solutions to the demand for compact antennas due to the increasing demand for reduced sizes of components in modern wireless communication devices.
A key feature of the book includes an extensive literature survey to understand the concept of fractal antennas, their features, and design approaches. Another key feature is the systematic approach to antenna design. The book explains how the IE3D software is used to simulate various fractal antennas, and how the results can be used to select a design. This is followed by ANN model development and testing for optimization, and an exploration of ANN ensemble models for the design of fractal antennas.
The bio-inspired computing techniques based on GA, PSO, and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the given computing techniques is also explained to demonstrate how to select the best algorithm for a given bio-inspired design. Finally, the book explains how to evaluate antenna designs.
This book is a valuable resource for students (from UG to PG levels) and research scholars undertaking learning modules or projects on microstrip and patch antenna design in communications or electronics engineering courses.
Table of Contents
Chapter 1 Introduction
- Antennas for Communication Applications
- Antennas for Medical Applications
- Limitations of Existing Antenna Systems
- Fractal Antennas
- Design and Analysis of Fractal Antennas: Recent Deve- Lopment
- Conclusion
- References
Chapter 2 Bio-Inspired Computing Techniques and Their Applications in Antennas
- Introduction
- Bio-Inspired Computing Techniques
- Artificial Neural Network (Ann)
- Multi-Layer Perceptron Neural Networks (Mlpnn)
- Radial Basis Function Neural Networks (Rbfnn)
- General Regression Neural Networks (Grnn)
- Ann Ensemble
- Genetic Algorithm (Ga)
- Particle Swarm Optimization (Pso) Algorithm
- Bacterial Foraging Optimization (Bfo) Algorithm
- Hybrid Bio-Inspired Computing Techniques
- Bio-Inspired Computing Techniques in Antennas
- Ann Applications in Antennas
- Microstrip Antenna Design Using Anns
- Resonant Frequency Calculation Using Anns
- Anns for Other Antenna Parameter Calculations
- Other Antenna Related Applications of Anns
- Applications of Bio-Inspired Optimization Techniques in Antennas
- Limitations of the Existing Bio-Inspired Computing Techniques
- Applications of the Bio-Inspired Optimization Techni- Ques in Fractal Antennas
- Conclusion
- Disclosure
- References
Chapter 3 Fractal Antennas
- Introduction
- Selected Typical Fractal Antennas and Their Features
- Sierpinski Gasket Monopole Fractal (Sgmf) Antenna
- Sierpinski Carpet Fractal Antenna
- Koch Curve Antenna
- Hexagonal Fractal Antenna
- Crown Square Fractal Antenna
- Rectangular Sierpinski Carpet Based Fractal Antenna
- Sierpinski-Koch Hybrid Fractal Antenna
- Other Mathematical Fractal Geometries-Based Antenna
- Fractal Antennas Developed in the Present Research Work
- Miniaturized Crown Rectangular Fractal (Crf) Antenna
- Tapered Crf Antenna
- Miniaturized Crown Circular Fractal (Ccf) Antenna
- Conclusion
- Acknowledgment
- Disclosure
- References
Chapter 4 Development of Ann Models for the Design of Fractal Antennas
- Introduction
- Development of Ann Models for Fractal Antennas
- Ann Model for Analysis of Sgmf Antenna
- Parameter Estimation of Crf Antenna Using Ann Models
- Ann Ensemble Models for Fractal Antennas
- Ann Ensemble Model for Tapered Crf Antenna
- Ann Ensemble Model for Ccf Antenna
- Conclusion
- Disclosure
- References
Chapter 5 Development of Hybrid Bio-Inspired Computing Algorithms for Design of Fractal Antennas
- Introduction
- Design of Sgmf Antenna Using Bio-Inspired Computing Techniques
- Parameters of the Proposed Optimization Models for Sgmf Antenna
- Results of the Proposed Optimization Models for Sgmf Antenna
- Ga-Ann Hybrid Model for Sgmf Antenna Design
- Bfo-Ann Ensemble Hybrid Algorithm to Design Tapered Crf Antenna for Ism Band Applications
- Experimental Results of Tapered Crf Antenna
- Pso-Ann Ensemble Hybrid Model to Design Ccf Antenna for Wlan Applications
- Experimental Results of Ccf Antenna
- Conclusion
- Annexure
5.1: Bfo-Ann Ensemble Hybrid Algorithm Coding Design Steps
- Part -1 Ann Ensemble Coding Steps
- Part -2 Bfo - Ann Ensemble Hybrid Algorithm Coding Steps
- Disclosure
- References
Chapter 6 Conclusion and Future Scope
- Conclusion
- Future Scope
- Glossary
- List of Abbreviations
- Subject Index
Author
- Balwinder S. Dhaliwal
- Shyam Sundar Pattnail
- Suman Pattnaik
