✦ LIBER ✦

Microwave noise modeling for PHEMT using artificial neural network technique

✍ Scribed by Xiuping Li; Jianjun Gao; Qi-Jun Zhang

Publisher: John Wiley and Sons
Year: 2009
Tongue: English
Weight: 528 KB
Volume: 19
Category: Article
ISSN: 1096-4290
DOI: 10.1002/mmce.20339

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Artificial neural network applications i

Artificial neural network applications in improved noise wave modeling of microwave FETs

✍ Zlatica D. Marinković; Olivera Pronić-Rančić; Vera Marković 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 489 KB

## Abstract An application of artificial neural networks (ANNs) for accuracy improving of the microwave FETs (MESFET/HEMT, dual‐gate MESFET) noise modeling is presented in this paper. The proposed model is based on a basic transistor noise wave model, whose noise wave temperatures are assumed to be

Neural networks for microwave modeling:

Neural networks for microwave modeling: Model development issues and nonlinear modeling techniques

✍ Vijaya Kumar Devabhaktuni; Mustapha C. E. Yagoub; Yonghua Fang; Jianjun Xu; Qi-J 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 280 KB 👁 2 views

Printed dipole antenna design using arti

Printed dipole antenna design using artificial neural network modeling for RFID application

✍ Xiuping Li; Jianjun Gao; Georg Boeck 📂 Article 📅 2006 🏛 John Wiley and Sons 🌐 English ⚖ 197 KB 👁 1 views

An approach to printed dipole antenna design using the artificial neural network (ANN) modeling technique is presented in this article. Three important antennalayout dimensions are used to capture critical input/output relationships in the ANN model. Once fully developed, the ANN model has been show

Modeling of microwave devices with artif

Modeling of microwave devices with artificial neural networks using segmentation and finite elements

✍ Juan M. Cid; Jesús García; Juan Zapata 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 157 KB 👁 1 views

## Abstract The CAD of microwave devices involves extensively computing their electromagnetic response. Using segmentation and finite elements, a neural model for an arbitrary device is developed efficiently, even if the number of design parameters is high. This is made clear with an example of a t

A coupled FDTD-artificial neural network

A coupled FDTD-artificial neural network technique for large-signal analysis of microwave circuits

✍ S. Goasguen; S. M. El-Ghazaly 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 302 KB

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

Rainfall-runoff models using artificial

Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction

✍ Dae-Il Jeong; Young-Oh Kim 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 865 KB

## Abstract Previous ensemble streamflow prediction (ESP) studies in Korea reported that modelling error significantly affects the accuracy of the ESP probabilistic winter and spring (i.e. dry season) forecasts, and thus suggested that improving the existing rainfall‐runoff model, TANK, would be cr