Dynamic Prediction Model Based on Process Neural Networks and Its Application

A dynamic prediction method based on process neural networks is proposed for the process forecasting and prediction problem of dynamic system.Both the inputs and outputs of the process neural networks can be time-varying functions and their spatial-temporal aggregation operation and activation can reflect the space aggregation function of the time-varying input signals and the stage time cumulation effect in the input process at the same time.Dynamic prediction model based on process neural networks can meet nonlinear recognition and process predition of the dynamic system,and has better adaptability to dynamic forecasting and prediction problem in mechanism.The paper gives a learning algorithm based on function basis expansion integrated with gradient descent,and proves the effectiveness of the model and algorithm with the example of power load forecasting.