CAD of Stacked Patch Antennas Through Multipurpose Admittance Matrices From FEM and Neural Networks

In this work, a novel computer-aided design methodology for probe-fed, cavity-backed, stacked microstrip patch antennas is proposed. The methodology incorporates the rigor of a numerical technique, such as finite element methods, which, in turn, makes use of a newly developed procedure (multipurpose admittance matrices) to carry out a full-wave analysis in a given structure in spite of certain physical shapes and dimensions not yet being established. With the aid of this technique, we form a training set for a neural network, whose output is the desired response of the antenna according to the value of design parameters. Last, taking advantage of this neural network, we perform a global optimization through a genetic algorithm or simulated annealing to obtain a final design. The proposed methodology is validated through a real design whose numerical results are compared with measurements with good agreement

A Novel Design Approach to X-Band Minkowski Reflectarray Antennas using the Full-Wave EM Simulation-based Complete Neural Model with a Hybrid GA-NM Algorithm

In this work, a novel multi-objective design optimization procedure is presented for the Minkowski Reflectarray RAs using a complete 3-D CST Microwave Studio MWS-based Multilayer Perceptron Neural Network MLP NN model including the substrate constant εr with a hybrid Genetic GA and Nelder-Mead NM algorithm. The MLP NN model provides an accurate and fast model and establishes the reflection phase of a unit Minkowski RA element as a continuous function within the input domain including the substrate 1 ≤ εr ≤ 6; 0.5mm ≤ h ≤ 3mm in the frequency between 8GHz ≤ f ≤ 12GHz. This design procedure enables a designer to obtain not only the most optimum Minkowski RA design all throughout the X- band, at the same time the optimum Minkowski RAs on the selected substrates. Moreover a design of a fully optimized X-band 15×15 Minkowski RA antenna is given as a worked example with together the tolerance analysis and its performance is also compared with those of the optimized RAs on the selected traditional substrates. Finally it may be concluded that the presented robust and systematic multi-objective design procedure is conveniently applied to the Microstrip Reflectarray RAs constructed from the advanced patches

Neural Network Characterization of Reflectarray Antennas

An efficient artificial neural network (ANN) approach for the modeling of reflectarray elementary components is introduced to improve the numerical efficiency of the different phases of the antenna design and optimization procedure, without loss in accuracy. The comparison between the results of the analysis of the entire reflectarray designed using the simplified ANN model or adopting a full-wave characterization of the unit cell finally proves the effectiveness of the proposed model

Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network

The microstrip patch antenna can also be designed using an artificial neural network (ANN) modeling technique where size of the antenna is major limitation especially in mobile and wireless applications. In this chapter, analysis and synthesis problems for designing of microstrip patch antennas were discussed using the artificial neural network technique. An analysis problem refers to calculation of resonant frequency of microstrip patch antenna whereas a synthesis problem refers to calculation of dimensions of patch antenna. Both problems are reciprocal of each other. Results are implemented using graphical user interface (GUI) tools of MATLAB programming language. Back‐propagation training algorithm of artificial neural network is used to train the network for minimization of error and computation time. Therefore, the geometric dimensions of patch are obtained with high accuracy in less computation time as compared to simulation software

Application of adaptive neuro-fuzzy inference system technique in design of rectangular microstrip patch antennas

The recent explosion in information technology and wireless communications has created many opportunities for enhancing the performance of existing signal transmission and processing systems and has provided a strong motivation for developing novel devices and systems. An indispensable element of any wireless communication system is the antenna. microstrip patch antenna (MPA) is well suited for wireless communication due to its light weight, low volume and low profile planar configuration which can be easily conformed to the host surface. In this paper, an adaptive neuro‐fuzzy inference systems (ANFIS) technique is used in design of MPA. This artificial Intelligence (AI) technique is used in determining the parameters used in the design of a rectangular microstrip patch antenna. The ANFIS has the advantages of expert knowledge of fuzzy inference system (FIS) and the learning capability of artificial neural network (ANN). By determining the patch dimensions and the feed point of a rectangular microstrip antenna, this paper shows that ANFIS produces good results that are in agreement with Antenna Magus simulation results.Key words: Artificial intelligence (AI), microstrip patch antennas (MPAs), adaptive neuro‐fuzzy inference system (ANFIS

Investigations for the Prediction of Resonant Frequency of Microstrip Patch Antenna Using RBF Neural Network

In this paper, an application of radial basis function (RBF) neural network is presented for the prediction of resonant frequency of microstrip patch antenna. The training and testing data for artificial neural network (ANN) model are generated with the help of method of moment based IE3D simulation software. ANN model predicted accurate results for resonant frequency of microstrip patch antenna. Simulated and ANN model results are compared with the experimental results which are available in the literature and found in good agreement

Demonstrating Antenna Miniaturisation for Radiolocation Applications using Double Elliptical Patches

Double Elliptical Micro-strip Patch Antenna (DEMPA) is developed out of Double Elliptical Patch (DEP) which is a recently proposed shape of patch. The use of DEP results in higher flexibility in design of patch antenna and thus promotes antenna miniaturisation. The present work is an attempt to demonstrate the miniaturisation of radiolocation antenna through the concept of Design Flexibility (DF). In this paper, optimised neural network model for synthesis of DEMPA has been developed for radiolocation applications for which the earmarked frequency band is 8.50 GHz – 10.50 GHz. With the help of synthesis model, for an arbitrary operational frequency of 9.85 GHz, radiolocation antennas with effective patch area ranging from 142 mm2 to 66 mm2 were designed by using DEPs. In this case, the percentage reduction in effective patch area was found to be 53.52%. It shows that double elliptical patches can be employed to develop miniaturised radiolocation antennas. One prototype antenna was fabricated and tested to demonstrate the efficacy of the methodology adopted. The fabricated antenna had resonance at 10.15 GHz with a reflection coefficient of -20.73dB and bandwidth of 3.106 GHz (from 7.458 GHz to 10.564 GHz). Its Fractional Bandwidth was 34.469%. Positive and reasonably good gain was maintained over the entire working band. At resonance, the peak gain was 4.22 dB.The measured characteristics of antenna were in close agreement with the simulated results. The methodology presented in this paper can also be applied to frequency bands for other wireless applications

Neurocomputational Models for Parameter Estimation of Circular Microstrip Patch Antennas

AbstractNeurocomputational models eliminates the complex, lengthy and time consuming mathematical procedures for design, analysis and calculating performance parameters of Microstrip antenna. No single ANN based model has been proposed till date for calculating all parameters of circular microstrip antennas simultaneously. This paper presents a Neuro-Computational (NC) approach for estimation of all performance parameters such as Return Loss (RL), Voltage Standing Wave Ratio (VSWR), resonant frequency (fr), Band-Width (BW), Gain(G), Directivity(D) and antenna efficiency(η) of Circular Microstrip Patch Antenna (CMPA) simultaneously. The difficulty in calculating the parameters of these antennas lies due to the involvement of a large number of physical parameters including their associated optimal values. It is indeed very difficult to formulate an exact numerical solution merely on practical observations based empirical studies. In order to circumvent this problem, an alternative solution is achieved using artificial neural network (ANN). Feed-Forward Back-Propagation Artificial Neural Network (FFBP-ANN) trained with Levenberg-Marquardt algorithm is used for estimation of different performance parameters of CMPA. The results of NC estimation are in very agreement with simulated, measured and theoretical results

ANN Synthesis Model of Single-Feed Corner-Truncated Circularly Polarized Microstrip Antenna with an Air Gap for Wideband Applications

A computer-aided design model based on the artificial neural network (ANN) is proposed to directly obtain patch physical dimensions of the single-feed corner-truncated circularly polarized microstrip antenna (CPMA) with an air gap for wideband applications. To take account of the effect of the air gap, an equivalent relative permittivity is introduced and adopted to calculate the resonant frequency and Q-factor of square microstrip antennas for obtaining the training data sets. ANN architectures using multilayered perceptrons (MLPs) and radial basis function networks (RBFNs) are compared. Also, six learning algorithms are used to train the MLPs for comparison. It is found that MLPs trained with the Levenberg-Marquardt (LM) algorithm are better than RBFNs for the synthesis of the CPMA. An accurate model is achieved by using an MLP with three hidden layers. The model is validated by the electromagnetic simulation and measurements. It is enormously useful to antenna engineers for facilitating the design of the single-feed CPMA with an air gap