A Staged Adaptive Identification Method with Fully Decoupled Structure for Nonlinear System Based on Volterra Model
一种基于Volterra模型的非线性系统具有全解耦结构的递阶式自适应辨识方法

The decoupled adaptive identification problem for nonlinear system based on Volterra series is discussed in allusion to the corrupted input and output observation data. According to the pseudo linear combination structure of Volterra series model, by applying the principle of the total least squares identification, a total fully decoupled identification idea is revealed. And then a staged adaptive identification algorithm with fully decoupled structure is built, and its framework diagram is shown. The advantage of the presented algorithm is that it possesses higher convergence speed and precision than the partly decoupled identification algorithm under a complete noise data environment. Finally simulation results indicate that the presented algorithm in this paper is efficient.