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A novel synthesis artificial neural network (SYNTHESIS-ANN) is combined with the finite-difference time-domain method. Practical applications are illustrated through the optimization of a dipole antenna input impedance. The ANN architecture utilizes a hetero-associative memory, which exploits a fault tolerant number representation of a neural network for input and output data. In addition, the number representation reveals significant insight into a new method of fault tolerant computing. A new randomization process for the synthesis of antenna geometrical parameters is presented. Additional work is required to investigate the potential of this new paradigm.
Published in:
Antennas and Propagation, IEEE Transactions on
(Volume:53
,
Issue:
7
)
Date of Publication: July 2005
- Page(s):
- 2231 - 2236
- ISSN :
- 0018-926X
- INSPEC Accession Number:
- 8507652
- Digital Object Identifier :
- 10.1109/TAP.2005.850706
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 05 July 2005
- Issue Date :
- July 2005
- Sponsored by :
- IEEE Antennas and Propagation Society
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