Identification of secondary path in ANC using diagonal recurrent neural networks with EKF algorithm

Riyanto T. Bambang*, Redi R. Yacoub, Kenko Uchida

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Citations (Scopus)


    This paper presents theoretical and experimental modeling of secondary path of an active noise control system in free space by using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks is developed based on Extended Kalman Filter and is referred to as Diagonal Recurrent Extended Kalman Filter algorithm. The neural network structure and its algorithm are applied to handle nonlinearity of the secondary path. To put the neural identification task within the context of ANC, a new control algorithm based on DREKF is also presented. The real-time experiment, however, is performed only for identification task. Experimental results using a floating point DSP show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the identification system performance.

    Original languageEnglish
    Title of host publication2004 5th Asian Control Conference
    Number of pages9
    Publication statusPublished - 2004
    Event2004 5th Asian Control Conference - Melbourne
    Duration: 2004 Jul 202004 Jul 23


    Other2004 5th Asian Control Conference


    • ANC
    • Diagonal recurrent neural networks
    • DSP
    • Extended Kalman Filter
    • Identification
    • Secondary path nonlinearity

    ASJC Scopus subject areas

    • Engineering(all)


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