Multi-Innovation Nesterov accelerated gradient parameter identification method for autoregressive exogenous models

Abstract

This paper proposed a multi-innovation Nesterov accelerated gradient (MNAG) parameter identification method for the autoregressive exogenous (ARX) model. First, a momentum acceleration term is added stochastic gradient descent (SGD) algorithm to increase the convergence rate of the SGD. Second, the parameter updating process is expanded from a single batch of current information iteration to the multiple batches of both previous and current information iteration, which extended the algorithm from single-innovation Nesterov accelerated gradient (NAG) to multi-innovation NAG parameter identification method. That enhances the algorithm’s anti-noise and anti-abnormal data abilities, and its data utilization rate. Then, the convergence of the MNAG parameter identification method is proven. The effectiveness of the MNAG parameter identification method is verified by numerical simulation and the rotational speed system of a ring-pendulum double-sided polisher.