Genetic algorithm with artificial neural networks as its fitness function to design rectangular microstrip antenna on thick substrate (original) (raw)
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IEEE Antennas and Propagation Magazine, 2005
This paper deals with the design of a multi-slot hole-coupled microstrip antenna on a substrate of 2 mm thickness that gives mulMrequency (wideband) characteristics. The Method of Moments (MoM)-based IE3D software was used to simulate the results for return loss, VSWR, the Smith chart, and the radiation patterns. A tunnel-based artificial neural network (ANN) was also developed to calculate the radiation patterns of the antenna. The radiation patterns were measured experimentally at 10.5 GHz and 12 GHz. The experimental results were in good agreement with the simulated results from IE3D and those of the artificial neural network. A new method of using a genetic algorithm (GA) in an artificial neural network is also discussed. This new method was used to calculate the resonant frequency of a single-shorting-post microstrip antenna. The resonant frequency calculated using the genetic-algorithm-coupled artificial neural network was compared with the analytical and experimental results. The results obtained were in very good agreement with the experimental results.
Calculation of optimized parameters of rectangular microstrip patch antenna using genetic algorithm
Microwave and Optical Technology Letters, 2003
In this paper, the genetic algorithm (GA) has been applied to calculate the optimized length and width of rectangular microstrip antennas. The inputs to the problem are the desired resonant frequency, dielectric constant, and thickness of the substrate; the outputs are the optimized length and width. The antennas considered are electrically thin. Method of moments (MoM)-based IE3D software from Zealand Inc., USA, and experimental results are used to validate the GA-based code. The results are in good agreement. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 431–433, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10940
Microstrip antenna optimization using genetic algorithms
2010
The design of a micro-strip patch antenna is proposed by optimizing its resonant frequency, Bandwidth of operation and Radiation resistance using Genetic Algorithm (GA). GA is based on the mechanics of natural genetics and natural selection and good at taking larger, potentially huge, search spaces and navigating them looking for optimal combinations of solutions which we might not find in life time .Optimizing radiation resistance operational bandwidth as high as 25.52 % and return loss -47.5dB is obtained without any complexity of design. The antenna can be used for various applications in fields of mobile communication, satellite communication, RFID, GPS, Radar communication etc.
A Heuristic Artificial Neural Network for Analyzing and synthesizing Rectangular Microstrip Antenna
2007
In this paper, both the synthesis and analysis of rectangular microstrip antenna models based on the artificial neural networks are presented to calculate accurately the resonant frequency of the rectangular microstrip antennas. Artificial neural networks are developed from neurophysiology by morphologically and computationally mimicking human brains The resonant frequency results obtained by using rectangular microstrip antenna characteristics and neural network models are very good agreement with the experimental results available in the literature. this paper presents a Multilayer Perceptron (MLP)modular neural network, training with the Resilient Back propagation algorithm which has been used for nonlinear device modeling in microwave band
Design and Analysis of Microstrip Patch Antennas Using Soft Computing
Microstrip patch antenna used to send onboard parameters of article to the ground while under operating conditions. The aim of the project is to design and fabricate an probe-fed Rectangular Microstrip Patch Antenna and r), substrate thickness (t) on the Radiation parameters of Bandwidth and Beam-width through the Neural network. This paper presents the general design of Microstrip antennas using artificial neural networks for rectangular patch geometry. The design consists of synthesis in the forward side and then analyzed as the reverse side of the problem. In this work, the neural network is employed as a tool in design of Microstrip antennas. The Neural network Training algorithms are used in simulation of results for training the samples to minimize the error and to obtain the geometric dimensions with high accuracy for selective band of frequencies.
Design and analysis of microstrip patch antenna using soft computing
INTERNATIONAL JOURNAL OF LATEST TRENDS IN ENGINEERING AND TECHNOLOGY
Microstrip patch antenna used to send onboard parameters of article to the ground while under operating conditions. The aim of the project is to design and fabricate an probe-fed Rectangular Microstrip Patch Antenna and r), substrate thickness (t) on the Radiation parameters of Bandwidth and Beam-width through the Neural network. This paper presents the general design of Microstrip antennas using artificial neural networks for rectangular patch geometry. The design consists of synthesis in the forward side and then analyzed as the reverse side of the problem. In this work, the neural network is employed as a tool in design of Microstrip antennas. The Neural network Training algorithms are used in simulation of results for training the samples to minimize the error and to obtain the geometric dimensions with high accuracy for selective band of frequencies.
Design of an aperture-coupled microstrip antenna using a hybrid neural network
IET Microwaves, Antennas & Propagation, 2012
In this paper, design of an aperture-coupled microstrip antenna (ACMSA) using differential evolution algorithm (DE) is described. The classical transmission line model for microstrip antenna is used to determine fitness function for DE and computed results are compared with the results obtained using particle swarm optimization (PSO) and binary coded genetic algorithm (GA). The aperture-coupled microstrip antenna is fabricated and measured results are compared with the results obtained using differential evolution algorithm.
Microstrip antenna is gathering a lot of interest in communication systems. Genetic algorithm is a popular optimization technique and has been introduced for design optimization of microstrip patch antenna. In this paper, genetic algorithm has been used for optimization of resonant frequency of coaxially fed rectangular microstrip antenna. The investigation is made at 3 different frequencies 3GHz, 5GHz and 10GHz respectively. Patch length, patch width & feed position are taken as optimization parameters. Return loss and radiation pattern for the optimized antenna are verified using IE3D software. Accuracy of the results encourages the use of genetic algorithm.
International Journal of Rf and Microwave Computer-aided Engineering, 2005
In this article, an efficient application of a genetic algorithm (GA) in an artificial neural network (ANN) to calculate the resonant frequency of a coaxially-fed tunable rectangular microstrip-patch antenna is presented. For a normal feed-forward back-propagation algorithm, with a compromise between time and accuracy, it is difficult to train the network to achieve an acceptable error tolerance. The selection of suitable parameters of ANNs in a feed-forward network leads to a high number of man-hours necessary to train a network efficiently. However, in the present method, the GA is used to reduce the man-hours while training a neural network using the feed forward-back-propagation algorithm. It is seen that the training time has also been reduced to a great extent while giving high accuracy. The results are in very good agreement with the experimental results. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2005.