Neural input space mapping optimization based on nonlinear two-layer perceptrons with optimized nonlinearity
Loading...
Date
2010-09
Authors
Gutiérrez-Ayala, Vladimir
Rayas-Sánchez, José E.
Journal Title
Journal ISSN
Volume Title
Publisher
Int. J. RF and Microwave CAE
Abstract
Description
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.
Keywords
Computer Aided Design (CAD), Circuit Design, Electromagnetic Based Optimization, Three-Layer Perceptron, Two-Layer Perceptron, Microstrip Filters, Surrogate Modeling, Neural Space Mapping (NSM), Artificial Neural Networks (ANN)
Citation
V. Gutiérrez-Ayala and J.E. Rayas-Sánchez, “Neural input space mapping optimization based on nonlinear two-layer perceptrons with optimized nonlinearity,” Int. J. RF and Microwave CAE, vol. 20, pp. 512-526, Sep. 2010.