A novel identification technique for lumped models of general distributed circuits 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. The inputs of the neural network are 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.
Lumped Model Identification Based on a DoubleMulti-Valued Neural Network and Frequency ResponseAnalysis / A.Luchetta; S.Manetti. - ELETTRONICO. - (2012), pp. 2505-2508. (Intervento presentato al convegno IEEE-ISCAS'12 tenutosi a Seoul, Corea del Sud nel 20-23/05/2012).
Lumped Model Identification Based on a DoubleMulti-Valued Neural Network and Frequency ResponseAnalysis
LUCHETTA, ANTONIO;MANETTI, STEFANO
2012
Abstract
A novel identification technique for lumped models of general distributed circuits 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. The inputs of the neural network are 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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