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System identification and modelling based on a double modified multi-valued neural network

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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. The 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 approach in order to elaborate the data to present to the net.

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Authors and Affiliations

  1. Department of Information Engineering, University of Florence, Florence, Italy

    F. Grasso, A. Luchetta, S. Manetti & M. C. Piccirilli

Authors
  1. F. Grasso
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  2. A. Luchetta
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  3. S. Manetti
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  4. M. C. Piccirilli
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Correspondence to A. Luchetta.

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Grasso, F., Luchetta, A., Manetti, S. et al. System identification and modelling based on a double modified multi-valued neural network. Analog Integr Circ Sig Process 78, 165–176 (2014). https://doi.org/10.1007/s10470-013-0211-y

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  • DOI: https://doi.org/10.1007/s10470-013-0211-y

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