A novel identification technique for the extraction of lumped circuit models of general distributed or stray devices is presented. The approach is based on two multi-valued neuron neural networks used in a joined architecture able to extract hidden parameters, whose convergence allows the validation of the approximated lumped model and the extraction of the correct values. The inputs of the neural network are the geometrical parameters of a given structure, while the outputs represent the estimation of the lumped circuit parameters. The method uses a Frequency Response Analysis (FRA) approach in order to elaborate the data to present to the net.
Identification of systems for modelling and diagnosis based on a double multi-valued neural network / F. Grasso; A. Luchetta; S. Manetti; M. C. Piccirilli. - STAMPA. - (2012), pp. 277-280. (Intervento presentato al convegno International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2012 tenutosi a Siviglia, Spagna nel settembre 2012) [10.1109/SMACD.2012.6339393].
Identification of systems for modelling and diagnosis based on a double multi-valued neural network
GRASSO, FRANCESCO;LUCHETTA, ANTONIO;MANETTI, STEFANO;PICCIRILLI, MARIA CRISTINA
2012
Abstract
A novel identification technique for the extraction of lumped circuit models of general distributed or stray devices is presented. The approach is based on two multi-valued neuron neural networks used in a joined architecture able to extract hidden parameters, whose convergence allows the validation of the approximated lumped model and the extraction of the correct values. The inputs of the neural network are the geometrical parameters of a given structure, while the outputs represent the estimation of the lumped circuit parameters. The method uses a Frequency Response Analysis (FRA) approach in order to elaborate the data to present to the net.File | Dimensione | Formato | |
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