控制理论(社会学)
卡尔曼滤波器
非线性系统
维纳滤波器
数学
估计理论
扩展卡尔曼滤波器
块(置换群论)
自回归滑动平均模型
噪音(视频)
滤波器(信号处理)
算法
计算机科学
自回归模型
人工智能
统计
物理
几何学
图像(数学)
控制(管理)
量子力学
计算机视觉
作者
Feng Li,Shengyi Qian,Naibao He,Bo Li
摘要
Abstract This paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.
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