Active noise control in free space using recurrent neural networks with EKF algorithm

R. T. Bambang, Kenko Uchida, R. R. Yacoub

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)


    This paper presents theoretical and experimental investigation of active noise control (ANC) in free space using recurrent neural networks. A learning algorithm for diagonal recurrent neural networks based on extended Kalman filter is developed and is referred to as diagonal recurrent extended Kalman filter (DREKF) algorithm. Based on DREKF, new control algorithm suited for ANC is developed to handle nonlinearity inherently arising in this application. Real-time experiment using floating point digital signal processor is carried out for both identification and control tasks required in ANC. The results show that the number of neurons in neural network can be reduced by introducing the diagonal recurrent elements, without deteriorating the system performance, and that DREKF produces better performance than linear adaptive controller in compensating the secondary path nonlinearity.

    Original languageEnglish
    Pages (from-to)267-276
    Number of pages10
    JournalControl and Intelligent Systems
    Issue number3
    Publication statusPublished - 2008


    • Active noise control
    • Diagonal recurrent neural networks
    • Digital signal processor
    • Extended Kalman filter
    • Free space
    • Nonlinearity

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Control and Systems Engineering


    Dive into the research topics of 'Active noise control in free space using recurrent neural networks with EKF algorithm'. Together they form a unique fingerprint.

    Cite this