Comparative Filtering Performance of Neural Networks

  • Authors:
  • V. R. Mankar;A. A. Ghatol

  • Affiliations:
  • -;-

  • Venue:
  • ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
  • Year:
  • 2007

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Abstract

Filtering of signals is of primary importance in signal processing. The design of filters to perform signal estimation is a problem that freeze up in the design of communication systems, control systems, in geophysics & in many other applications & disciplines. Optimum filters are used for linear prediction. In this paper, neural networks have been trained to predict a signal using the past signal samples. It is found that neural networks such as multiplayer perceptron, general feed forward, modular neural network, etc., comprising of three hidden layers with a linear transfer function elegantly filters various signals under consideration. Keywords Linear Estimation, Newlind, Feed-forward Neural Network, RLS, MSE, MLP, RBF.