Noise reduction by using autoassociative neural networks

Noise reduction has always been an important part of any control, acquisition or processing task. In order to increase the usage of some smaller and cheaper, but on the other hand less precise sensor solutions, it is necessary to incorporate some signal processing techniques for noise reduction. Nowadays soft computing techniques such as neural networks are widely used in many signal processing applications and provide very good results. In this paper, an approach to noise reduction by using autoassociative neural networks is described. The main idea is to use more precise, therefore more expensive sensor, for the network training, and afterward use this network for less precise and cheaper sensor signal processing. So, when the network is formed, it is possible to use less precise sensor and use the network for noise reduction. This would ensure noise reduction in the less precise sensor signal. With this signal processing tool, less precise sensors could be used in desired applications. When comparing the results obtained by using autoassociative neural networks with results obtained by using digital filters, the obvious advantage is that neural networks do not bring delay into the system like filters do. All described simulations and data processing are performed in Matlab and Simulink.