Preface

The demand of the compact antennas is increasing continuously due to the requirement for reduced-size wireless communication devices. The use of fractal geometry for the design of small-size antennas is a modern trend. As closed-form expressions do not exist for fractal antennas, alternative methods of designing fractal antennas are needed. The use of bio-inspired computing techniques like Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bacterial Foraging Optimization (BFO) is very appropriate in such cases. In the presented research work, these techniques have been used for parameter estimation and design optimization of fractal patch antennas. Therefore, the presented research works confines the fractal antennas & bio-inspired computing techniques to provide cost-effective & efficient solutions. An extensive literature survey is carried out to understand the concept of fractal antennas, their features and design approaches. Also, a number of research papers are reviewed on the applications of bio-inspired computing techniques for antenna design, especially fractal antenna design. The extracts of the literature survey presented in the book highlight these important issues.

Many fractal antenna geometries suitable for medical and communication applications have been proposed in the presented research work. The IE3D software has been used to simulate various fractal antennas, and the simulation results are obtained to analyze the performance of the selected antennas. The desired features are assessed from the S₁₁ plots, gain plots and radiation patterns which are validated with experimental and analytical findings. The multilayer perceptron neural network, radial basis function neural network, and generalized regression neural network models are developed to estimate various parameters of the proposed fractal antennas. The performance of various ANN models has also been compared in order to find optimally suitable models. The use of ANN ensemble models for the design of fractal antennas is also explored, and it has been found that the ANN ensemble approach is better than the traditional ANN model approach. The different methods of developing ANN ensemble models are also presented. 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 various bio-inspired computing techniques is also carried out to select the best algorithm. The use of ANN models as an objective function of optimization algorithms is also enumerated to design the presented fractal antennas. It has been observed that the developed bio-inspired computing techniques provide accurate solutions with a very small computational cost. The performance of the designed antennas is validated by fabricating prototypes and then performing experimental testing. The simulated results are compared with the experimental results, and good matching of simulated and experimental results is observed in almost all cases. The obtained results are also compared with the previously published results to validate the presented designs. The research work has resulted in the design of the fractal antennas having many desirable features like size reduction characteristics, enhanced gain, and improved bandwidths

This book contains six chapters which present the outcomes of the above-described research work.