Frequency Of Rectangular Microstrip Antenna Research Articles

Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on AdaBoost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the AdaBoost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.

Velocity Modulated Bacterial Foraging Optimization Technique (VMBFO)

In this paper, a Velocity Modulated Bacterial Foraging Optimization Technique is proposed. The Bacterial Foraging algorithm is hybridized with Particle Swarm Optimization technique to reduce the convergence time while maintaining high accuracy. The hybridized optimization technique is tested on various benchmark functions like Sphere, Rosenbrock, Generalized Rastrigin, Griewank and Ackley. In addition to these, the proposed algorithm is also tested to calculate the resonant frequency of Rectangular Microstrip Antenna. The results are compared with the existing algorithms and that of experimental results and are found to be in good agreement.

COMPARISON OF MAMDANI AND SUGENO FUZZY INFERENCE SYSTEM MODELS FOR RESONANT FREQUENCY CALCULATION OF RECTANGULAR MICROSTRIP ANTENNAS

Models based on fuzzy inference systems (FISs) for calculating the resonant frequency of rectangular microstrip antennas (MSAs) with thin and thick substrates are presented. Two types of FIS models, Mamdani FIS model and Sugeno FIS model, are used to compute the resonant frequency. The parameters of FIS models are determined by using various optimization algorithms. The resonant frequency results predicted by FIS models are in very good agreement with the experimental results available in the literature. When the performances of FIS models are compared with each other, the best result is obtained from the Sugeno FIS model trained by the least- squares algorithm.

Bacterial foraging optimization technique to calculate resonant frequency of rectangular microstrip antenna

Bacterial foraging optimization technique to calculate resonant frequency of rectangular microstrip antenna

Effects of superstrates on the resonant frequency of rectangular microstrip antennas

AbstractThis article presents the extended expression to calculate the environmental effect on resonant frequency of multilayer rectangular microstrip antennas. The expression has been investigated for effective dielectric constant εeff and resonant frequency fr. The effective dielectric constant εeff of Present Method has been compared with full wave numerical technique with and without loading. These are in good agreement within 1% accuracy. It has been observed that % change in fr due to cover and substrate thickness is around 3.2% and 20%, respectively, as their thickness is varying 19 times of its initial value. This variation is drastic for h3/h2 = 0.01. The maximum % variation in fr for one substrate and two superstrate is around 5% as h4 varies from 1 mm to 5 mm. In case of unwanted air (h3), the % variation in fr of present method in comparison to experimental value is less than 1.0% for small air‐gap and 0.4% for large air‐gap for various spaced dielectric (εr4), which reveals that the effect of superstrate in fr is small than substrate above the ground plane. The resonant frequency in suspended substrate microstrip antenna with and without superstrate is 21.8% and 28.15%, respectively, as h1/h2 varies from 0.065 to 1.94. The resonant frequency of all the possible configuration is in good agreement with the experimental results. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 2946–2950, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22954

Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas

A new method based on adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of rectangular microstrip antennas (MSAs) with thin and thick substrates is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The resonant frequency results obtained by using ANFIS are in excellent agreement with the experimental results reported elsewhere.

A closed‐form expression for the resonant frequency of rectangular microstrip antennas

AbstractA new and accurate expression for the edge extension in accurately predicting the resonant frequency of electrically thin and thick rectangular microstrip antennas is presented. First, a closed‐form model for the edge extension is constructed by utilizing a differential evolution algorithm, the resonant frequency of rectangular microstrip antennas is then calculated by means of this edge extension expression. The calculations were carried out on 46 antennas with a range of different dimensions built on various substrates. The resonant frequency results presented in this letter show the better agreement with the experimental results as compared with those reported by the several researchers. A comparison between calculated and experimental resonant frequencies demonstrates that the edge extension model provides less than 0.7% errors on average. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 1848–1852, 2007; Published online in Wiley Inter‐Science (www.interscience.wiley.com). DOI 10.1002/mop.22572

An Empirical Expression for the Edge Extension in Calculating Resonant Frequency of Rectangular Microstrip Antennas with Thin and Thick Substrates

This paper presents a simple empirical model for the edge extension in accurately calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas. Based on the experimental results available in literature, a closed-form formula for edge extension is constructed by utilizing the simplex search method; then the resonant frequency of rectangular microstrip antennas is calculated by using this edge extension expression. A comparison between the calculated and experimental resonant frequency values demonstrates that the edge extension expression provides less than 0.51% errors on average. The resonant frequency results presented in this work also show the better agreement with the experimental results as compared to those predicted by several researchers.

Artificial Neural Networks for Resonant Frequency Calculation of Rectangular Microstrip Antennas with Thin and Thick Substrates

Neural models based on multilayered perceptrons for computing the resonant frequency of rectangular microstrip antennas with thin and thick substrates are presented. Eleven learning algorithms, Levenberg-Marquardt, conjugate gradient of Fletcher-Reeves, conjugate gradient of Powell-Beale, bayesian regularization, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, resilient backpropagation, conjugate of Polak-Ribiere, backpropagation with adaptive learning rate, one-step secant, and backpropagation with momentum, are used to train the multilayered perceptrons. The resonant frequency results obtained by using neural models are in very good agreement with the experimental results available in the literature. When the performances of neural models are compared with each other, the best result is obtained from the multilayered perceptrons trained by Levenberg-Marquardt algorithm.

Comments on "Resonant frequencies of rectangular microstrip antennas with flush and spaced dielectric superstrates"

The author comments that the work of Bernhard and Tousignant (see IEEE Trans. Antennas Propagat., vol.47, p.302-308, Feb. 1999) is important for implementations in computer-aided design (CAD). The authors have successfully tied up many loose ends in the previously reported work in this area. However, it appears they have not been attentive to include the effect of the dielectric thickness for the substrate with relative permittivity /spl epsi//sub 3/.

Resonance frequency of rectangular microstrip antenna on thick substrate

A modified cavity model to compute the resonance frequency of a rectangular patch antenna on a thick polytetrafluoroethylene substrate (0.037λg – 0.229λg) with maximum deviation of 2% against experimental results is presented. This accuracy is achieved by accounting for the effect of anisotropy and total losses on the resonance frequency. All other models, including the method of moments (MOM)-based Ensemble, compute resonance frequency with error between 4 and 36%.

Resonant Frequency of Rectangular Microstrip Antenna in Plasma

The effective and the dynamic dielectric constants of a plasma immersed microstrip line are determined. They are subsequently used to calculate the resonant frequency of a plasma embedded rectangular microstrip antenna. Results for different test antennas are provided for simulated plasma environment. The effect of substrate dielectric constants and sheath thickness on resonant frequency are studied.

Resonant frequencies of rectangular microstrip antennas with flush and spaced dielectric superstrates

This paper presents a predictive model for the resonant frequencies of rectangular microstrip antennas with flush and spaced superstrates. This closed-form model is suitable for CAD and is directly applicable for the integration of microstrip antennas beneath plastic covers or protective dielectric superstrates in portable wireless equipment. The model utilizes conformal mapping and the concept of equivalent capacitance to determine the effective permittivity of a covered microstrip structure. A comparison between calculated and measured resonant frequencies demonstrates that the model provides less than 1% errors for structures with low relative permittivities (/spl epsi//sub r/<3) and slightly higher errors for structures that contain higher permittivity materials. Generally, the model predicts the resonant frequency of a spaced superstrate structure as accurately as possible within the tolerance range of the structure's electrical and physical parameters.

Superstrate effect on the resonant frequency of microstrip antennas

AbstractA computer code based on the full‐wave analysis and the spectral domain approach was developed to predict the effect of superstates on the resonant frequency of rectangular microstrip antennas. The formulation is based on the method of moments using Galerkin technique with entire‐domain basis functions. The resonant frequency is calculated by using an optimization routine that searches for the stationary condition of the characteristic equation. Numerical solutions are obtained for some test cases and are verified by comparison with experimental results.