Premium
Fuzzy neural‐based approaches for efficient RF/microwave transistor modeling
Author(s) -
Gaoua Said,
Ji Limin,
Cheng Ze,
Mohammadi Farah A.,
Yagoub Mustapha C. E.
Publication year - 2009
Publication title -
international journal of rf and microwave computer‐aided engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 39
eISSN - 1099-047X
pISSN - 1096-4290
DOI - 10.1002/mmce.20323
Subject(s) - microwave , artificial neural network , transistor , fuzzy logic , electronic engineering , computer science , radio frequency , flexibility (engineering) , cluster analysis , process (computing) , perceptron , engineering , electrical engineering , artificial intelligence , voltage , telecommunications , statistics , mathematics , operating system
In today's RF and microwave circuits, there is an ever‐increasing demand for higher level of system integration that leads to massive computational tasks during simulation, optimization, and statistical analyses, requiring efficient modeling methods so that the whole process can be achieved reliably. Since active devices such as transistors are the core of modern RF/microwave systems, the way they are modeled in terms of accuracy and flexibility will critically influence the system design, and thus, the overall system performance. In this article, the authors present neural‐ and fuzzy neural‐based computer‐aided design techniques that can efficiently characterize and model RF/microwave transistors such as field‐effect transistors and heterojunction bipolar transistors. The proposed techniques based on multilayer perceptrons neural networks and c‐means clustering algorithms are demonstrated through examples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.