Abstract
Automated model generation (AMG) of neural network models has been proved to be an effective method for improving neural modeling efficiency. In this paper, an advanced parallel AMG method incorporating interpolation approaches and parallel computation formulation for multi-processor environment is presented to further speedup the neural model development. Modeling of a microwave filter is presented as an example to demonstrate the advantages of the method.
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