Discrete process neural networks and its application in the predication of sunspot number series

Considering that inputs of a process neural network (PNN) are generally time-varying functions while the inputs of many practical problems are discrete values of multiple series, in this paper, a process neural network with discrete inputs is presented to provide improved forecasting results for solving the complex time series prediction. The presented method first makes discrete input series carry out Walsh transformation, and submits the transformed series to the network for training. It can solve the problem of space-time aggregation operation of PNN. In order to examine the effectiveness of the presented method, the actual data of sunspots during 1749-2007 are employed. To predict the number of sunspots, the suitability of the developed model is examined in comparison with the other models to show its superiority and be an effective way of improving forecasting accuracy of networks.