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This paper presents the experimental results otjtained from an empirical analysis of the effect of noise on tlie training and testing performance of a simple image recognition neural network. The investigation makes use of a computer simulation program which implements the standard backpropagation learning algorithm for multilayer feedforward neural networks. The effect of varying the amount of noise, in bloth the training and test data, on the performance of the neiml network is examined. A number of trends are revealed and the resultant implications are discussed.

The Effect Of Noise On The Generalisation & Classification Capabilities Of Simple Image Recognition Artificial Neural Networks