Process Neural Network Algorithm Based on Piecewise Linear Interpolation Function

Process neural networks (PNN) can only receive time-varying continuous functions, can not receive discrete samples. To solve this problem, a training algorithm of PNN based on piecewise linear interpolation function is proposed. First the discrete data of both sample functions and weight functions are transformed to piecewise linear functions, and then the integrals of product of two linear functions at a given sampling interval are computed. As a result of aggregation, these integrals are submitted to process neurons of PNN hide layer. Finally, the networks output is obtained in output layer. Some advantages of piecewise linear interpolation function, e.g. continuity, calculability, lower exponential and less parameters, simplifies aggregation operation of PNN in both space and time. The experimental results are illustrated the availability of the proposed method.