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Proceedings Paper

Reliable neural modeling of pHEMT from a smaller number of measurement data
Author(s): Mojtaba Joodaki; Guenter Kompa
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Paper Abstract

A systematic approach is presented to achieve a reliable neural model for microwave active devices with different numbers of training data. The method is implemented for a small-signal bias depended modeling of pHEMT in tow different environments, on a standard test-fixture and in the New Generation Quasi-Monolithic Integration Technology (NGQMIT), with different numbers of training data. The errors for different numbers of training data have been compared to each other and show that by using this method a reliable model is achievable even though the number of training data is considerably small. The method aims at constructing a model, which can satisfy the criteria of minimum training error, maximum smoothness (to avoid the problem of over-fitting), and simplest network structure.

Paper Details

Date Published: 6 December 2002
PDF: 9 pages
Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); doi: 10.1117/12.453548
Show Author Affiliations
Mojtaba Joodaki, Univ. of Kassel (Germany)
Guenter Kompa, Univ. of Kassel (Germany)


Published in SPIE Proceedings Vol. 4787:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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