Neural Networks in Antennas and Microwaves: A Practical Approach

Neural networks are electronic systems which can be trained to remember behavior of a modeled structure in given operational points, and which can be used to approximate behavior of the structure out of the training points. These approximation abilities of neural nets are demonstrated on modeling a frequency-selective surface, a microstrip transmission line and a microstrip dipole. Attention is turned to the accuracy and to the efficiency of neural models. The association of neural models and genetic algorithms, which can provide a global design tool, is discussed

Multi-Objective Self-Organizing Migrating Algorithm: Sensitivity on Controlling Parameters

In this paper, we investigate the sensitivity of a novel Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) on setting its control parameters. Usually, efficiency and accuracy of searching for a solution depends on the settings of a used stochastic algorithm, because multi-objective optimization problems are highly non-linear. In the paper, the sensitivity analysis is performed exploiting a large number of benchmark problems having different properties (the number of optimized parameters, the shape of a Pareto front, etc.). The quality of solutions revealed by MOSOMA is evaluated in terms of a generational distance, a spread and a hyper-volume error. Recommendations for proper settings of the algorithm are derived: These recommendations should help a user to set the algorithm for any multi-objective task without prior knowledge about the solved problem

Multi-Objective Synthesis of Filtering Dipole Array Based on Tuning-Space Mapping

In the paper, we apply tuning-space mapping to multi-objective synthesis of a filtering antenna. The antenna is going to be implemented as a planar dipole array with serial feeding. Thanks to the multi-objective approach, we can deal with conflicting requirements on gain, impedance matching, side-lobe level, and main-lobe direction. MOSOMA algorithm is applied to compute Pareto front of optimal solutions by changing lengths of dipoles and parameters of transmission lines connecting them into a serial array. Exploitation of tuning space mapping significantly reduces CPU-time demands of the multi-objective synthesis: a coarse optimization evaluates objectives using a wire model of the filtering array (4NEC2, method of moments), and a fine optimization exploits a realistic planar model of the array (CST Microwave Studio, finite integration technique). The synthesized filtering antenna was manufactured, and its parameters were measured to be compared with objectives. The number of dipoles in the array is shown to influence the match of measured parameters and objectives

Mean-Adaptive Real-Coding Genetic Algorithm and its Applications to Electromagnetic Optimization (Part One)

In the paper, a novel instance of the real-coding steady-state genetic algorithm, called the Mean-adaptive real-coding genetic algorithm, is put forward. In this instance, three novel implementations of evolution operators are incorporated. Those are a recombination and two mutation operators. All of the evolution operators are designed with the aim of possessing a big explorative power. Moreover, one of the mutation operators exhibits self-adaptive behavior and the other exhibits adaptive behavior, thereby allowing the algorithm to self-control its own mutability as the search advances. This algorithm also takes advantage of population-elitist selection, acting as a replacement policy, being adopted from evolution strategies. The purpose of this paper (i.e., the first part) is to provide theoretical foundations of a robust and advanced instance of the real-coding genetic algorithm having the big potential of being successfully applied to electromagnetic optimization

Use of the Analog Neural Networks in the Adaptive Antenna Control Systems

In the paper, original control system of adaptive antennas, which is based on Kalman filter, is presented and compared with earlier approaches to this problem. The designed control circuit eliminates some disadvantages of the control circuits based on the classical Kalman neural network and the Wang one, and enables a real time processing of quickly changing signals processed by adaptive antennas. Especially, the dependence of the convergence rate on ratio of eigenvalues and the risk of instability are significantly reduced

Broadband Analysis of Microwave Structures by Enhanced Finite-Element Methods

The paper deals with the broadband modeling of microwave structures by finite-element methods. The attention is turned to original enhancements of accuracy, efficiency and stability of finite-element codes. The partial improvements are based on novel approximations both in the spatial domain and in the time one, in the adoption of complex frequency hopping, fast frequency sweep and envelope finite-element techniques. In the paper, a possible hybridization of approaches is discussed. Proposed finite-element schemes are applied to the analysis of canonical longitudinally homogeneous transmission lines in order to demonstrate their advantages

Modeling Microwave Structures in Time Domain Using Laguerre Polynomials

The paper is focused on time domain modeling of microwave structures by the method of moments. Two alternative schemes with weighted Laguerre polynomials are presented. Thanks to their properties, these schemes are free of late time oscillations. Further, the paper is aimed to effective and accurate evaluation of Green\'s functions integrals within these schemes. For this evaluation, a first- and second-order polynomial approximation is developed. The last part of the paper deals with modeling microstrip structures in the time domain. Conditions of impedance matching are derived, and the proposed approach is verified by modeling a microstrip filter

Influence of EBG Structures on the Far-Field Pattern of Patch Antennas

In this paper, the influence of the EBG structures on the far-field pattern of patch antennas is investigated. As a reference model, a conventional rectangular patch antenna on a high-permittivity substrate is used. The reference model is consequently equipped by an EBG substrate (instead of the conventional one), and by an EBG cover (so called EBG superstrate). The changes in the farfield radiation patterns are discussed. In the second part of the paper, the substrate is perturbed by two different EBG structures designed for the coverage of two operation bands

Global Evolutionary Algorithms in the Design of Electromagnetic Band Gap Structures with Suppressed Surface Waves Propagation

The paper is focused on the automated design and optimization of electromagnetic band gap structures suppressing the propagation of surface waves. For the optimization, we use different global evolutionary algorithms like the genetic algorithm with the single-point crossover (GAs) and the multi-point (GAm) one, the differential evolution (DE) and particle swarm optimization (PSO). The algorithms are mutually compared in terms of convergence velocity and accuracy. The developed technique is universal (applicable for any unit cell geometry). The method is based on the dispersion diagram calculation in CST Microwave Studio (CST MWS) and optimization in Matlab. A design example of a mushroom structure with simultaneous electromagnetic band gap properties (EBG) and the artificial magnetic conductor ones (AMC) in the required frequency band is presented