On training artificial neural networks to identify periodic functions

Training an artificial neural network (ANN) to represent some type of plant, system, or general algebraic function is relatively straightforward and many methods exist. However, most of these methods and ANN architectures do not take into account any a priori knowledge that is often known for the problem of interest. In this paper, a priori knowledge is utilized to encourage the output of the ANN to be a periodic function of the input. Methods are investigated and compared with the general gradient-based (backpropagation) algorithm. Results show improved convergence characteristics and, in some cases, the guarantee of output periodicity with respect to the input. Generalizations can be made to higher dimensional spaces, but these may or may not be reasonable in terms of computational time and effort