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Knowledge-based neural network modeling techniques using space-mapping concept have been demonstrated in the existing literature as efficient methods to overcome the accuracy limitations of empirical/equivalent circuit models when matching new electromagnetic data. For different modeling problems, the mapping structures can be different. In this paper, we propose a unified automated model generation...
This paper proposes an advance and unified algorithm for automated knowledge-based neural network (NN) model generation. Automated model generation (AMG) method has been applied to generate knowledge-based NN model with a nonlinear input mapping structure. On the basis of the existing literature, the proposed unified method takes both input and output mapping, both linear and nonlinear mapping into...
This paper presents an advanced algorithm for automated model generation (AMG) using neural networks. AMG trains a neural network in a stage-by-stage manner to obtain a neural network of required accuracy with least amount of training data. In each stage, either the number of data or the size of the neural network is adjusted. The novelty of the proposed algorithm is to incorporate efficient interpolation...
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