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- Title
Neural input space mapping optimization based on nonlinear two-layer perceptrons with optimized nonlinearity.
- Authors
Gutiérrez-Ayala, Vladimir; Rayas-Sánchez, José E.
- Abstract
A neural space mapping optimization algorithm based on nonlinear two layer perceptrons (2LP) is described in this article. This work is an improved version of the Neural Space-Mapping (NSM) algorithm that uses three layer perceptrons (3LP) to implement a nonlinear input mapping function at each iteration. The new version uses a nonlinear 2LP whose nonlinearity is automatically regulated with classical optimization algorithms. Additionally, the new algorithm uses a different optimization method to train the SM-based neuromodel and a more efficient manner to predict the next iterate. With these improvements, we obtain a more efficient and faster algorithm. To verify the algorithm performance, we design some synthetic circuits, as well as a stopband microstrip filter with quarter-wave resonant opens stubs, a bandpass microstrip filter, and a microstrip notch filter with mitered bends. The last three cases use commercially available full-wave electromagnetic simulators. A rigorous comparison is made with the original NSM algorithm, showing the performance improvement achieved by our proposed new formulation. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.
- Subjects
ALGORITHMS; ARTIFICIAL neural networks; PERCEPTRONS; ELECTRIC circuits; STRIP transmission lines
- Publication
International Journal of RF & Microwave Computer-Aided Engineering, 2010, Vol 20, Issue 5, p512
- ISSN
1096-4290
- Publication type
Article
- DOI
10.1002/mmce.20457