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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.

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