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This paper presents a general method combining microwave empirical/equivalent model with artificial neural networks for efficient modeling of microwave components. The method, called Generalized Knowledge-Based Neural Network (GKBNN), unifies several existing methods and provides increased model accuracy and extrapolation capability, even if the training data is limited. It is applied to microwave...
Artificial neural network (ANN) and space mapping are recognized as two major recent advances in microwave CAD. ANNs can be trained to learn EM and physics behaviour from component data, and trained ANNs can be used in high-level circuit design. Space mapping has proved to be a breakthrough in engineering optimization allowing expensive EM optimization to be performed effectively with the help of...
This paper presents a novel approach aimed at enhancing the speed and accuracy of harmonic balance (HB) simulation of nonlinear circuits represented by dynamic neural network (DNN) models. A set of constraint functions are proposed using the boundary information extracted from the time-domain training data for developing DNN models. Using these constraints, an expanded HB formulation of DNN is presented...
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