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This letter presents a novel dynamic Neuro-space mapping (Neuro-SM) technique for nonlinear device modeling. This is an advance over the existing static Neuro-SM which aims to map a given approximate device model towards an accurate model. The proposed technique retains the ability of static Neuro-SM in modifying the effects of nonlinear resistors and current sources. The proposed technique can also...
In this paper, an advanced Neuro-Space Mapping (SM) modeling technique for nonlinear device modeling is proposed. By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. The novelty of our work is to introduce a Neuro-SM model combining separate mappings for voltage and current...
In this article, a new Neuro-Space mapping method is presented aimed at using neural networks to automatically enhance nonlinear device models, such as FET models. Compared with previously published space mapping methods, our proposed method produces better modeling accuracy and provides more effective combinations of mapping structure with existing coarse model. In our proposed models, separate mappings...
A gallium nitride Doherty power amplifier (GaN Doherty PA) was designed for 2.5 GHz WiMAX band, and a radial-basis function neural network (RBFNN) model is proposed for predicting this amplifier' nonlinear characteristics. Comparison of AM/AM, AM/PM, PAE and Pout curves between the RBFNN model and circuit simulation are given. After 125 epochs, the convergence of this RBFNN model becomes slower and...
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