Development of Neural Networks for Noise Reduction

Lubna Badri
Faculty of Engineering, Philadelphia University, Jordan

Abstract: This paper describes the development of neural network models for noise reduction. The networks used to enhance the performance of modeling captured signals by reducing the effect of noise. Both recurrent and multi-layer Backpropagation neural networks models are examined and compared with different training algorithms. The paper presented is  to illustrate the effect of training algorithms and  network architecture on neural network performance for a given application.

Keywords: Noise reduction, recurrent neural networks, multi-layer backpropagation.