Neural fuzzy digital filtering: Multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)

Translated title of the contribution: Neural fuzzy digital filtering: Multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Multivariate identifier filters (multiple inputs and multiple outputs- MIMO) are adaptive digital systems having a loop in accordance with an objective function to adjust matrix parameter convergence to observable reference system dynamics. One way of complying with this condition is to use fuzzy logic inference mechanisms which interpret and select the best matrix parameter from a knowledge base. Such selection mechanisms with neural networks can provide a response from the best operational level for each change in state (Shannon, 1948). This paper considers the MIMO digital filter model using neuro fuzzy digital filtering to find an adaptive parameter matrix which is integrated into the Kalman filter by the transition matrix. The filter uses the neural network as back propagation into the fuzzy mechanism to do this, interpreting its variables and its respective levels and selecting the best values for automatically adjusting transition matrix values. The Matlab simulation describes the neural fuzzy digital filter giving an approximation of exponential convergence seen in functional error.

Translated title of the contributionNeural fuzzy digital filtering: Multivariate identifier filters involving multiple inputs and multiple outputs (MIMO)
Original languageEnglish
Pages (from-to)184-192
Number of pages9
JournalIngenieria e Investigacion
Volume31
Issue number1
StatePublished - 2011

Keywords

  • Adaptive digital system
  • Digital filter
  • Fuzzy control
  • MIMO
  • Neural network

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