V. V. THAKARE ET AL.

336

0

0.02

0.04

0.06

0.08

0.1

0.12

12345678910

Number of hidden la

ers

Average Minimum MSE

FOR TRAINING DATAFOR TEST DATA

Figure 8. Graph showing variation of average minimum

MSE on Training and Test data set for different no. of the

hidden layers in the neural network.

0

0.02

0.04

0.06

0.08

0.1

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2610 1418

No. of neurons in the hidden la

er

Averag e M inimum MSE

FOR T RAI NING DAT AFOR TEST DATA

Figure 9. Graph showing variation of average minimum

MSE on Training and Test data set for different no. of hid-

den layers in the networ k.

Figure 10. Number of epochs to achieve minimum mean

square error level with RBF ANN.

for those 11 input combinations which are not included in

the set of training data and found satisfactory.

A neural network-based CAD model is developed for

the design of a rectangular patch antenna, which is robust

both from the angle of time of computation and accuracy.

A distinct advantage of neuro computing is that, after

proper training, a neural network completely bypasses the

repeated use of complex iterative processes for new cases

presented to it. The developed network structure can

predict the results for patch dimensions provided that the

values of εr, fr and h are in the domain of training values.

5. References

[1] R. K. Mishra and A. Patnaik, “Neural Network-Based

CAD Model for the Design of Square-Patch Antennas,”

IEEE Transactions on Antennas and Propagation, Vol.

46, No. 12, 1998, pp. 1890-1891. doi:10.1109/8.743842

[2] J. L. Narayan, K. Sri R. Krishna and L. P. Reddy, “De-

sign of Microstrip Antenna Using Artificial Neural Net-

works,” International Conference on Computational In-

telligence and Multimedia Applications, Vol. 1, 2007, pp.

332-334.

[3] N. Turker, F. Gunes and T. Yildirim, “Artificial Neural

Design of Microstrip Antennas,” Turkish Journal of

Electrical Engineering, Vol. 14, No. 3, 2006, pp. 445-

453.

[4] A. Patnaik, R. K. Mishra, G. K. Patra and S. K. Dash,

“An Artificial Neural Network Model for Effective Di-

electric Constant of Microstripline,” IEEE Transactions

on Antennas Propagation, Vol. 45, No. 11, 1997, p. 1697.

doi:10.1109/8.650084

[5] D. Karaboga, K. Giiney, S. Sagıroglu and M. Erler,

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IEEE Proceedings of Microwaves, Antennas and Propa-

gation, Vol. 146, No. 2, 1999, pp. 155-159.

doi:10.1049/ip-map:19990136

[6] S. Sagiroglu, K. Guney and M. Erler, “Computation of

Radiation Efficiency for a Resonant Rectangular Micro-

strip Patch Antenna using Back Propagation Multilayered

Perceptrons,” Journal of Electrical and Electronics, Vol.

3, 2003, pp. 663-671.

[7] Q. J. Zhang and K. C. Gupta, “Neural Networks for RF

and Microwave Design,” Artech House Publishers, Lon-

don, 2000.

[8] F. Peik, G. Coutts and R. R. Mansour, “Application of

Neural Networks in Microwave Circuit Modelling,” 1998

IEEE Canadian Conference of Electrical and Computer

Engineering, Waterloo, 24-28 May 1998, pp. 928-931.

[9] S. Devi, D. C. Panda and S. S. Pattnaik, “A Novel

Method of Using Artificial Neural Networks to Calculate

Input Impedance of Circular Microstrip Antenna,” An-

tennas and Propagation Society International Symposium,

Vol. 3, 2000, pp. 462-465.

[10] R. K. Mishra and A. Patnaik, “Design of Circular Micro-

strip Antenna Using Neural Networks,” IETE Journal of

Research, Vol. 44, No. 122, 1998, pp. 35-39.

[11] S. Sagiroglu and K. Guney and M. Erler, “Calculation of

Resonant Frequency for an Equilateral Triangular Micro-

strip Antenna Use with the of Artificial Neural Net-

works,” Microwave and Optical Technology Letters, Vol.

14, No. 3, 2003, pp. 89-93.

[12] R. K. Mishra and A. Patnaik, “ANN Techniques in Mi-

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