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A neuro-space mapping (Neuro-SM) for modeling heterojunction bipolar transistor (HBT) is presented, which can automatically modify the input signals of the given model by neural network. The novel Neuro-SM formulations for DC and small-signal simulation are proposed to obtain the mapping network. Simulation results show that the errors between Neuro-SM models and the accurate data are less than 1%,...
This paper proposes a new technique to train neural network (NN); with the result, we can solve some real‐world application problems such as microwave components modeling and optimization. Its major advance is achieved in avoiding the testing error falling into local minimum. After the generalization, the ability of three‐layer and four‐layer NN is also checked; our investigations show that four‐layer...
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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