Data Filtering-Based Maximum Likelihood Gradient-Based Iterative Algorithm for Input Nonlinear Box–Jenkins Systems with Saturation Nonlinearity

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Yamin Fan, Ximei Liu, Meihang Li
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Abstract

Saturation nonlinearity exists widely in various practical control systems. Modeling and parameter estimation of systems with saturation nonlinearity are of great importance for analyzing their characteristics and controller design. This paper focuses on the identification issue of the input nonlinear Box–Jenkins systems with saturation nonlinearity. The input saturation nonlinearity is presented as a linear parametric expression through the application of a switching function, then the identification model of the system is derived by using the key term separation technique. Based on this model and the data filtering technique, the filtering identification model of the system is given by changing the system structure without changing the relationship between the input and output, which can reduce the interference of the colored noise and improve the identification accuracy. Then a data filtering-based maximum likelihood gradient-based iterative algorithm is proposed to estimate the unknown parameters. The maximum likelihood gradient-based iterative algorithm is provided for comparison. The feasibility and superiority of the proposed approach are emphasized by a simulation example.

Abstract Image

针对具有饱和非线性的输入非线性盒-詹金斯系统的基于数据过滤的最大似然梯度迭代算法
饱和非线性广泛存在于各种实际控制系统中。对具有饱和非线性的系统进行建模和参数估计,对分析其特性和控制器设计具有重要意义。本文主要研究具有饱和非线性的输入非线性 Box-Jenkins 系统的识别问题。通过应用开关函数,将输入饱和非线性表述为线性参数表达式,然后利用关键项分离技术推导出系统的识别模型。在此模型和数据滤波技术的基础上,通过改变系统结构而不改变输入和输出之间的关系,给出了系统的滤波识别模型,从而减少了彩色噪声的干扰,提高了识别精度。然后提出一种基于数据滤波的最大似然梯度迭代算法来估计未知参数。并对基于最大似然梯度的迭代算法进行了比较。通过一个仿真实例强调了所提方法的可行性和优越性。
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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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