On Interference of Signals and Generalization in Feedforward Neural Networks

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This paper studies how the generalization ability of neurons can be affected by mutual processing of different signals. This study is done on the basis of a feedforward artificial neural network. The mutual processing of signals can possibly be a good model of patterns in a set generalized by a neural network and in effect may improve generalization. In this paper it is discussed that the interference may also cause a highly random generalization. Adaptive activation functions are discussed as a way of reducing that type of generalization. A test of a feedforward neural network is performed that shows the discussed random generalization.