A Survey of Adaptive Filter Algorithms Used for Noise Cancellation Application

  • Authors

    • Sofiya Kandi
    • Soumya S. Patil
    • Rajashekar B. Shettar
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24531
  • noise, LMS, neural networks, machine learning, signal processing, interval arithmetic.

  • Noise component is difficult to predict and controlling completely is impossible. There is lot of research going on in handling noise, where speed, faster convergence, computational complexities, accuracy have been a major concern. In this paper the survey of all approaches are presented in minimizing noise with optimum mean square error (MSE). A new concept of adaptive filter using interval arithmetic is proposed. Using this concept, the rounding and truncation errors are taken care.  In interval arithmetic the bound of input and output is calculated. Finding optimum weight is of major concern in adaptive filter.  This optimization problem can be solved using interval analysis methods. This makes it applicable in many of the modern day technologies like pattern recognition, machine learning and share market analysis where accuracy is a challenging factor. With the combination neural networks and interval analysis the adaptive filter can be efficiently designed. As interval analysis has two values, the time of execution is increased.

     

     

     

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    Kandi, S., S. Patil, S., & B. Shettar, R. (2018). A Survey of Adaptive Filter Algorithms Used for Noise Cancellation Application. International Journal of Engineering & Technology, 7(4.41), 215-219. https://doi.org/10.14419/ijet.v7i4.41.24531