故障排除模型估计
关于故障排除模型
During validation, models can exhibit undesirable characteristics or a poor fit to the validation data.
Use the tips in these sections to help improve your model performance. Some features, such as low signal-to-noise ratio, varying system properties, or nonstationary disturbances, can produce data for which a good model fit is not possible.
Model Order Is Too High or Too Low
A poor fit in the Model Output plot can be the result of an incorrect model order. System identification is largely a trial-and-error process when selecting model structure and model order. Ideally, you want the lowest-order model that adequately captures the system dynamics. High-order models are more expensive to compute and result in greater parameter uncertainty.
首先估计模型顺序如上所述Preliminary Step – Estimating Model Orders and Input Delays。使用建议的顺序作为起点，以不同的模型结构估算最低序列。每次估计后，监视模型输出和剩余分析图，然后调整您的设置以进行下一个估计。
When a low-order model fits the validation data poorly, estimate a higher-order model to see if the fit improves. For example, if the Model Output plot shows that a fourth-order model gives poor results, estimate an eighth-order model. When a higher-order model improves the fit, you can conclude that higher-order linear models are potentially sufficient for your application.
使用独立的数据集来验证您的模型。如果您使用相同的数据集进行估计和验证，则随着增加型号顺序并冒着过度拟合的风险，拟合总是会改善。但是，如果您使用独立的数据集来验证模型，则拟合最终会恶化，如果模型订单太高。
Substantial Noise in the System
系统中的大量噪音可能会导致模型差。当：
A state-space model produces a better fit than an ARX model. While a state-space structure has sufficient flexibility to model noise, an ARX structure is unable to independently model noise and system dynamics. The following ARX model equation shows thatAcouples the dynamics and the noise terms by appearing in the denominator of both:
A residual analysis plot shows significant autocorrelation of residuals at nonzero lags. For more information about residual analysis, see the topics on theResidual Analysis页。
To model noise more carefully, use either an ARMAX or the Box-Jenkins model structure, both of which model the noise and dynamics terms using different polynomials.
Unstable Models
不稳定的线性模型
您可以测试是否linear model不稳定是通过检查模型的极点零图，这在杆和零块。The stability threshold for pole values differs for discrete-time and continuous-time models, as follows:
For stable continuous-time models, the real part of the pole is less than 0.
For stable discrete-time models, the magnitude of the pole is less than 1.
Note
估计数据中的线性趋势可能导致确定的线性模型不稳定。但是，逐渐降低模型并不能保证稳定性。
如果您的模型不稳定，但是您认为系统稳定，则可以。
估计期间的力稳定性 - 设置
Focus估算值对保证稳定模型的值。此设置可能导致模型质量降低。允许某些不稳定 - 设置稳定阈值高级估计选项以允许错误余量：
对于连续时间模型，设置
Advanced.SostabilityThreshold.S。The model is considered stable if the pole on the far right is to the left ofs。对于离散时间模型，设置
Advanced.SostabilityThreshold.Z。如果所有电线杆都在半径为半径的圆内，则该模型被认为稳定z这是以原点为中心的。
For more information aboutFocus和Advanced.SostalibalyThreshold，请参阅各种命令以创建估算选项集，例如tfestOptions,ssestOptions， 和procestOptions。
不稳定的非线性模型
测试是否非线性模型is unstable, plot the simulated model output on top of the validation data. If the simulated output diverges from measured output, the model is unstable. However, agreement between model output and measured output does not guarantee stability.
When an Unstable Model Is OK
在某些情况下，不稳定的模型仍然有用。例如，如果您的系统没有控制器不稳定，则可以将模型用于控制设计。在这种情况下，您可以将不稳定模型导入Simulink万博1manbetx®or Control System Toolbox™ products.
缺少输入变量
If modeling noise and trying different model structures and orders still results in a poor fit, try adding more inputs that can affect the output. Inputs do not need to be control signals. Any measurable signal can be considered an input, including measurable disturbances.
在输入数据中包括其他测量信号，然后再次估算模型。
System Nonlinearities
如果线性模型对验证数据显示较差，请考虑系统中是否存在非线性效应。
您可以执行一个年代的非线性模型imple transformation on the input signals to make the problem linear in the new variables. For example, in a heating process with electrical power as the driving stimulus, you can multiply voltage and current measurements to create a power input signal.
如果您的问题足够复杂，并且您对系统没有物理洞察力，请尝试将非线性黑框模型拟合到数据中，请参阅About Identified Nonlinear Models。
非线性估计器的拟合度差
对于非线性ARX和Hammerstein-Wiener模型，当非线性估计器具有不正确的复杂性时，模型输出图不会显示出良好的拟合。
Specify the complexity of piece-wise-linear, wavelet, sigmoid, and custom networks using the单位数量非线性估计器属性。较高数量的单元表示更复杂的非线性估计器。使用神经网络时，使用网络对象的参数指定复杂性。有关更多信息，请参见深度学习工具箱™文档。
To select the appropriate nonlinearity estimator complexity, first validate the output of a low-complexity model. Next, increase the model complexity and validate the output again. The model fit degrades when the nonlinearity estimator becomes too complex. This degradation in performance is only visible if you use independent estimation and validation data sets
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