Neural Input Space Mapping Optimization Based on Nonlinear Two-Layer Perceptrons With Optimized Non linearity

作者：Gutierrez Ayala Vladimir^*; Rayas Sanchez Jose E

来源：International Journal of RF and Microwave Computer-Aided Engineering, 2010, 20(5): 512-526.

DOI：10.1002/mmce.20457

• circuit design
• electromagnetics-based optimization
• artificial neural networks. CAD
• three-layer perceptron. two-layer perceptron. space mapping
• neural space mapping
• microstrip filters
• surrogate modeling

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.

• 出版日期2010-9