DocumentCode :
2881120
Title :
Performance evaluation of Artificial Neural Networks in microstrip fractal antenna parameter estimation
Author :
Dhaliwal, B.S. ; Pattnaik, Shyam S.
Author_Institution :
Dept. of ECE, Guru Nanak Dev Eng. Coll., Ludhiana, India
fYear :
2012
fDate :
21-23 Nov. 2012
Firstpage :
135
Lastpage :
139
Abstract :
Artificial Neural Networks have been recently used for the design and analysis of fractal antennas. The performance of various types of networks has not been yet explored for these antennas. This paper evaluates the performance of three types of neural networks: Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Generalized Regression Neural Networks (GRNN) for parameter estimation of Microstrip Fractal Antenna. Depending on the values of mean percentage error and time taken for training of each type, it has been concluded that the GRNN has best performance among these three networks.
Keywords :
backpropagation; fractal antennas; microstrip antennas; parameter estimation; performance evaluation; radial basis function networks; regression analysis; telecommunication computing; BPNN; GRNN; RBFNN; artificial neural networks; backpropagation neural network; fractal antennas analysis; fractal antennas design; generalized regression neural networks; mean percentage error; microstrip fractal antenna parameter estimation; performance evaluation; radial basis function neural network; Artificial neural networks; Biological neural networks; Fractal antennas; Geometry; Microstrip; Microstrip antennas; Training; fractal antenna; neural networks; performance comparison; self-similar;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communication Systems (ICCS), 2012 IEEE International Conference on
Conference_Location :
Singapore
ISSN :
Pending
Print_ISBN :
978-1-4673-2052-8
Type :
conf
DOI :
10.1109/ICCS.2012.6406124
Filename :
6406124
Link To Document :
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