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A double integrated neural network for identification of geometrical features dependency in lumped models

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3 Author(s)
A. Luchetta ; Dept. of Electron. & Telecommun., Florence Univ., Italy ; S. Manetti ; L. Pellegrini

A novel identification technique for lumped models of general electronic circuits (i.e. MOSFET, BJT, monolithic integrated circuits and filters) is presented. The approach is based on a neural network having a supplementary layer and an adapted learning process, whose convergence allows the validation of the device model. The supplementary layer is another neural network trained off-line on the model under exam. The inputs of the network are geometrical parameters and the neural network output represents the lumped circuit parameter estimation.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004