Adaptive Gradient-Based Iterative Algorithm for Multivariable Controlled Autoregressive Moving Average Systems Using the Data Filtering Technique

作者：Pan, Jian^*; Ma, Hao; Jiang, Xiao; Ding, Wenfang; Ding, Feng

来源：Complexity, 2018, 2018: 9598307.

DOI：10.1155/2018/9598307

• PARAMETER-ESTIMATION ALGORITHM
• STATE-SPACE SYSTEM
• NONLINEAR-SYSTEMS
• IDENTIFICATION ALGORITHM
• DYNAMICAL-SYSTEMS
• MODEL
• NOISE
• DELAY
• CONVERGENCE
• OPTIMIZATION

The identification problem of multivariable controlled autoregressive systems with measurement noise in the form of the moving average process is considered in this paper. The key is to filter the input-output data using the data filtering technique and to decompose the identification model into two subidentification models. By using the negative gradient search, an adaptive data filtering-based gradient iterative (F-GI) algorithm and an F-GI with finite measurement data are proposed for identifying the parameters of multivariable controlled autoregressive moving average systems. In the numerical example, we illustrate the effectiveness of the proposed identification methods.

• 出版日期2018
• 单位湖北工业大学; 江南大学; 青岛科技大学