Efficient scattering parameter modeling of a microwave transistor using Generalized Regression Neural Network

Abstract

In this paper, a simple, accurate, fast and reliable black-box modeling is presented for the Scattering (S-) parameters of a microwave transistor from the reduced amount of the discrete data using General Regression Neural Network (GRNN). GRNN is a probability- based Neural Network and has been used in the generalization applications in the cases of the existence of the poor data bases. In this work, the GRNN-based modeling is implemented to the microwave transistor BFP640 with the separate interpolation and extrapolation applications and the comparative results are given. It can be concluded that the superior extrapolation ability of a GRNN can be used in generalization of the reduced amount of scattering parameter data accurately to the entire operation domain of device, thus in S- parameter modeling of a microwave transistor can be achieved.

Keywords

Extrapolation Artificial neural network Interpolation (computer graphics) Generalization Scattering parameters Computer science Microwave Transistor Electronic engineering Regression analysis Algorithm Regression Overfitting Data modeling Artificial intelligence Machine learning Mathematics Statistics Electrical engineering Engineering Mathematical analysis Telecommunications

Subject Areas

Advanced Photonic Communication Systems ·Electrical and Electronic Engineering ·Physical Sciences

Photonic and Optical Devices ·Electrical and Electronic Engineering ·Physical Sciences

Microwave Engineering and Waveguides ·Electrical and Electronic Engineering ·Physical Sciences

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Efficient scattering parameter modeling of a microwave transistor using Generalized Regression Neural Network

Abstract

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