The Aitken Accelerated Gradient Algorithm for a Class of Dual‐Rate Volterra Nonlinear Systems Utilizing the Self‐Organizing Map Technique
Junwei Wang, Weili Xiong, Feng Ding
Abstract
ABSTRACT This article focuses on the parameter estimation issues for dual‐rate Volterra fractional‐order autoregressive moving average models. In the case of dual‐rate sampling, we derive a dual‐rate identification model of the system and implement intersample output estimation with the help of an auxiliary model method. Then, combined with the self‐organizing map technique, we propose an Aitken multi‐innovation gradient‐based iterative algorithm. The system parameters are estimated using the Aitken multi‐innovation gradient‐based iterative algorithm, whereas the differential orders are determined using self‐organizing map method. Moreover, the computational cost of the proposed algorithm is analyzed using the floating point operation. Finally, the convergence analysis and simulation examples show the effectiveness of the proposed algorithm.