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Neural input space mapping optimization based on nonlinear two‐layer perceptrons with optimized nonlinearity
Author(s) -
GutiérrezAyala Vladimir,
RayasSánchez José E.
Publication year - 2010
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.20457
Subject(s) - space mapping , perceptron , stopband , computer science , nonlinear system , artificial neural network , algorithm , filter (signal processing) , microstrip , band pass filter , electronic engineering , artificial intelligence , engineering , physics , quantum mechanics , computer vision
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.