This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extracti
Analysis and validation of neural network approach for extraction of small-signal model parameters of microwave transistors
✍ Scribed by Marinković, Zlatica; Ivković, Nenad; Pronić-Rančić, Olivera; Marković, Vera; Caddemi, Alina
- Book ID
- 118179205
- Publisher
- Elsevier Science
- Year
- 2013
- Tongue
- English
- Weight
- 926 KB
- Volume
- 53
- Category
- Article
- ISSN
- 0026-2714
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
An extraction technique to determine the small-signal HBT equi¨alent circuit is presented. Some of the extrinsic element ¨alues are extracted by using an analytical approach, while the remaining ones are calculated adopting a statistical method. All of the model elements are uniquely determined. Sat
We propose a first-order global modeling approach of Monolithic Microwave Integrated Circuits (MMIC) by modeling the active device with a neural network based on a full hydrodynamic model. This neural network describes the nonlinearities of the equivalent circuit parameters of an MESFET implemented
rower than the achieved ; 25% impedance bandwidth. According to the measurements, good dual linear polarization characteristics can be realized between the two resonance frequencies.