Discrete Process Neural Networks Algorithm with Application

To address the training of discrete process neural networks, two algorithms based on numerical integral are proposed. The cubic spline integral and the parabolic interpolation integral are directly applied to dealing with the aggregation of discrete samples and weights in time-domain, and the Levenberg-Marquard algorithm is employed to adjusting the networks parameters. The effectiveness of the proposed algorithms is testified by applying the networks to the caravan insurance policy prediction, the experimental results show that the performance of two algorithms is relatively close, and superior to the orthogonal basis expansion-based process neural networks.