Application of a genetic algorithm in an artificial neural network to calculate the resonant frequency of a tunable single-shorting-post rectangular-patch antenna
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. (C) 2004 Wiley Periodicals, Inc.
genetic algorithm artificial neural networks resonant frequency microstrip antenna shorting post