On the properties of periodic perceptrons

This paper presents a summary of research regarding a modified perceptron model in which processing elements utilize a periodic activation function. Empirical results for a number of benchmark tests provide some indication of the “in practice” power of networks containing periodic processors for pattern classification tasks. In addition, new results are reported in which the internal structure of the network is shown to be interpretable, and indeed provides a basis for rule extraction. Together these results give some indication of the behaviour that can be expected when applying networks containing periodic perceptrons to pattern classification tasks, and provides dimensions along which such processing elements may be distinguished from standard sigmoid devices