A Weight-Generating Approach of a Deep Neural Network for the Parameter Identification of Dynamic Systems

Abstract

The general learning process of deep learning is extremely time-consuming. Unlike the traditional learning process, a weight-generating approach to quickly generate the weight vectors of a deep neural network model is proposed, which can be used for parameter identification of a dynamic system. Based on the analysis of three trained deep neural network models, which are used to identify the parameters of three different dynamic systems, the statistical relationships between the weight vectors of each hidden layer and its inputs are revealed. Then, the statistical patterns of the weight vectors are imitated by exploiting the statistical patterns of the inputs and these relationships. Then, a weight-generating approach is designed to quickly generate the weight vectors of a deep neural network model. The effectiveness of the weight-generating approach is tested on the tasks of parameter identification for the three dynamic systems. The numerical results are provided to demonstrate the validity and high efficiency of the proposed weight-generating approach.