非线性控制系统的支持向量机辨识建模研究

针对非线性控制系统辨识建模难的问题,系统研究了基于支持向量机的非线性控制系统的辨识建模理论和方法,然后利用回归支持向量机(Support Vector Regression,SVR)设计了一个非线性控制系统的辨识建模系统.仿真试验结果表明,SVR具有很高的建模精度和较强的泛化能力,从而验证了该辨识方法的有效性和先进性.     

非线性系统的支持向量机逆模型辨识及控制

针对非线性系统逆模型建立难的问题,提出了基于回归型支持向量机(support vetor regression,SVR)的非线性系统逆模型辨识建模的方法,在此基础上,提出了基于SVR的非线性系统逆模型控制的方法.仿真试验结果表明:采用SVR建立的非线性系统逆模型具有很高的建模精度和较强的泛化能力,基于SVR的逆模型控制...     

非线性系统的模糊建模及仿真

以一个非线性模型为研究对象、通过对自适应神经模糊推理系统（ANFIS）建模机理的研究,建立了非线性实例模糊模型,并且在不同的输入下进行仿真实验,结果表明利用ANFIS进行非线性系统建模和辨识是可行的,其辩识精度很高.     

基于BP神经网络的非线性系统辨识

工业系统大多具有大滞后、非线性的特点,难于控制.本文介绍了基于BP神经网络实现对非线性系统的辨识和仿真分析,并给出了实例.仿真结果表明,该方法可以对工程中常遇的复杂的、非线性较强的系统进行辨识,具有一定的适用性.     

基于神经元网络的动态系统辨识

本文利用神经元网络技术，构造出基于系统状态空间模型上的系统动态辨识器，该辨识器可用于线性或非线性系统的动态估计。文中讨论了辨识器的构成和实现，并给出了仿真结果。仿真结果表明该方法为非线性系统辨识提供了一条有效的途径。     

模糊神经元网络在非线性多变量燃烧系统解耦辨识中的应用

针对非线性多变量对象，提出了基于模糊神经网络的系统辨识，文章结合了模糊、神经网络的优点，既能象模糊逻辑那样表达近似与定性知识，又有神经网络很强的学习能力和非线性表述能力，而且模糊神经网络的物理意义也很清晰。仿真结果表明，该方法对于解耦辨识是有效的，证明了模糊神经网络具有很强的建模能力。     

迟滞对象的辨识研究

主要针对暖通空调系统中常见而又难以控制的时滞对象，研究了神经网络用于系统辨识中的工作原理，提出了基于神经网络的时滞线性系统和时滞非线性系统的辨识方法，同时，使用MATLAB的辨识工具箱对暖通空调中常见的惯性迟延系统对象进行了辨识试验并给出了仿真结果。从比较的结果看，对非线性的辨识，神经网络具有明显的优势。     

非线性动态电路／系统（C/S）辨识、分析计算与故障诊断的研究

采用Volterra级数解的递推算式与方波脉冲函数变换技术对一类非线性动态C/S的数学模型进行辨识,导出了相应的混合模型辨识公式和动态响应的求解方法。根据该法所得到的混合模型,对C/S施行了多重预置模型的故障诊断方法,并给出了辨识仿真和故障诊断实例。     

基于模糊C聚类的油封冷却系统鲁棒辨识研究

由于实际的复杂工业过程常常具有强非线性、不确定性、多变量以及工况变化频繁等特点,很难建立其精确的数学模型描述,使得传统控制难以达到理想的控制效果.根据目标函数选择模糊模型的结构,提出了一种非线性系统模糊建模新方法,以系统的输入和输出量试验数据为依据,确定其模糊规则,建立了系统的模糊模型.其次,将时域H_∞辨识方法应用于非线性系统的模糊建模中,使得由干扰到估计误差的最大能量增益达到最小.实例表明该方法具有一定得可行性.     

复杂过程的多模型建模方法研究

针对复杂工业过程的非线性、工况范围广的特点,本文提出了一种新的多模型建模方法.首先对系统按照工况划分准则进行双层K均值聚类,在此基础上采用LS-SVM算法建立各局部模型,并用粒子群算法对多模型权值进行辨识.此建模方法收敛速度快,对辨识过程有良好的全局适应性.并以典型非线性主汽温系统作为辨识对象,采用上述方法建立其系统的多模型,仿真结果验证了该方法的有效性.     

基于最小二乘模型的Bayes参数辨识方法

现实中的系统都具有一定的非线性,并且这种非线性在非线性通道补偿和非线性系统故障诊断等领域是不可忽略的。针对有白噪声干扰的输出误差非线性系统,将数学模型与基于最小二乘的Bayes算法相结合,用数学模型参数代替辨识模型信息向量中的未知项,用基于白噪声的最小二乘模型进行不可预测辨识,从而提出了基于最小二乘模型的Bayes参数辨识方法。介绍了Bayes基本原理及2种常用的方法,经过理论分析和MATLAB仿真研究证明,该方法原理简单、计算量小、速度快、抗干扰能力强,可以对较高精度非线性系统进行参数估计和在线辨识。     

基于支持向量回归的轴承故障定量诊断应用

针对轴承故障状态特征与故障大小之间存在非线性关系,提出利用支持向量回归机建立轴承故障大小与状态特征之间的定量诊断模型,并给出了基于支持向量回归的定量诊断策略和诊断流程。在获取轴承不同故障大小的特征量的基础上,建立了轴承故障定量诊断的支持向量回归模型,将其用于轴承故障的定量识别。结果表明,该方法能够有效地判断出故障的大小。进一步将该方法与人工神经网络方法比较,结果说明了支持向量回归方法在轴承故障定量诊断方面具有更好的适应性。     

The study of hybrid model identification,computation analysis and fault location for nonlinear dynamic circuits and systems

This paper presents the hybrid model identification for a class of nonlinear circuits and systems via a combination of the block-pulse function transform with the Volterra series. After discussing the method to establish the hybrid model and introducing the hybrid model identification, a set of relative formulas are derived for calculating the hybrid model and computing the Volterra series solution of nonlinear dynamic circuits and systems. In order to significantly reduce the computation cost for fault location, the paper presents a new fault diagnosis method based on multiple preset models that can be realized online. An example of identification simulation and fault diagnosis are given. Results show that the method has high accuracy and efficiency for fault location of nonlinear dynamic circuits and systems.     

A comparative study of adaptation algorithms for nonlinear system identification based on second order Volterra and bilinear polynomial filters

Nonlinear filtering techniques have recently become very popular in the field of signal processing. In this study we have considered the modeling of nonlinear systems using adaptive nonlinear Volterra filters and bilinear polynomial filters. The performance evaluation of these nonlinear filter models for the problem of nonlinear system identification has been carried out for several random input excitations and for measurement noise corrupted output signals. The coefficients of the two candidate filter models for are designed using several well known adaptive algorithms, such as least mean squares (LMS), recursive least squares (RLS), least mean p-norm (LMP), normalized LMP (NLMP), least mean absolute deviation (LMAD) and normalized LMAD (NLMAD) algorithms. Detailed simulation studies have been carried out for comparative analysis of Volterra model and bilinear polynomial filter, using these candidate adaptation algorithms, for system identification tasks and the superior solutions are determined.     

The study of hybrid model identification,computation analysis and fault location for nonlinear dynamic circuits and systems

This paper presents the hybrid model identification for a class of nonlinear circuits and systems via a combination of the block-pulse function transform with the Volterra series. After discussing the method to establish the hybrid model and introducing the hybrid model identification, a set of relative formulas are derived for calculating the hybrid model and computing the Volterra series solution of nonlinear dynamic circuits and systems. In order to significantly reduce the computation cost for fault location, the paper presents a new fault diagnosis method based on multiple preset models that can be realized online. An example of identification simulation and fault diagnosis are given. Results show that the method has high accuracy and efficiency for fault location of nonlinear dynamic circuits and systems. __________ Translated from Chinese Journal Of Scientific Instrument, 2005, 26(8) (in Chinese)     

Nonlinear modeling and identification of the electro-hydraulic control system of an excavator arm using BONL model

Electro-hydraulic control systems are nonlinear in nature and their mathematic models have unknown parameters. Existing research of modeling and identification of the electro-hydraulic control system is mainly based on theoretical state space model, and the parameters identification is hard due to its demand on internal states measurement. Moreover, there are also some hard-to-model nonlinearities in theoretical model, which needs to be overcome. Modeling and identification of the electro-hydraulic control system of an excavator arm based on block-oriented nonlinear（BONL） models is investigated. The nonlinear state space model of the system is built first, and field tests are carried out to reveal the nonlinear characteristics of the system. Based on the physic insight into the system, three BONL models are adopted to describe the highly nonlinear system. The Hammerstein model is composed of a two-segment polynomial nonlinearity followed by a linear dynamic subsystem. The Hammerstein-Wiener（H-W） model is represented by the Hammerstein model in cascade with another single polynomial nonlinearity. A novel Pseudo-Hammerstein-Wiener（P-H-W） model is developed by replacing the single polynomial of the H-W model by a non-smooth backlash function. The key term separation principle is applied to simplify the BONL models into linear-in-parameters struc~tres. Then, a modified recursive least square algorithm（MRLSA） with iterative estimation of internal variables is developed to identify the all the parameters simultaneously. The identification results demonstrate that the BONL models with two-segment polynomial nonlinearities are able to capture the system behavior, and the P-H-W model has the best prediction accuracy. Comparison experiments show that the velocity prediction error of the P-H-W model is reduced by 14%, 30% and 75% to the H-W model, Hammerstein model, and extended auto-regressive （ARX） model, respectively. This research is helpful in controller design, system monitoring and diagnosis.     

基于半物理仿真技术的机电伺服系统模型辨识研究

系统模型是进行系统性能分析与设计的基础。为了获得准确的系统数学模型,文章借助于dSPACE实时系统的半物理仿真环境和MATLAB系统辨识工具箱,提出了一种适用于机电伺服系统的模型辨识方法,并以机器人关节伺服系统为对象进行了系统模型辨识实验研究,通过对比离线仿真和半物理仿真结果验证了该方法的有效性。该研究对机电系统建模及控制系统设计具有参考价值。     

用于建模、优化、故障诊断的数据挖掘技术

建模、优化、故障诊断是流程工业ＣＩＭＳ技术中的关键技术。传统的建模、优化、故障诊断方法依赖于数学模型仿真或专家经验规则,对于强非线性和非高斯分布噪声的对象存在着知识获取瓶颈。而数据挖掘技术综合运用机器学习、计算智能（人工神经网、遗传算法）、模式识别、数理统计等技术,从大量数据中挖掘和发现有价值和隐含的知识。本文进一步研究了建模、优化、故障诊断的数据挖掘系统,以及规则挖掘、参变量优化、故障诊断建模的