某控制系统模型可信度验证分析

随着建模仿真技术日益广泛的应用,仿真模型结果的可信度备受关注。因此,必须对仿真模型进行可信度分析。有些模型的真实参考数据不能满足时间序列一致性的要求,即真实数据与仿真数据的采样点间隔不一致、起始点不一致等,对于这种情况,传统的可信度分析方法就不适用。为了对上述情况的仿真模型进行可信度分析,提出一种基于函数型数据相关的仿真模型验证方法。该方法将函数型数据相关分析的思想引入模型验证中,不要求输出数据具有时间序列一致性。通过对某舵机模型应用上述方法进行可信度分析表明,上述方法可以完成在不具备时间序列一致性情况下的模型验证。

Consistency of kernel density estimators for causal processes

Using the blocking techniques and m-dependent methods,the asymptotic behavior of kernel density estimators for a class of stationary processes,which includes some nonlinear time series models,is investigated.First,the pointwise and uniformly weak convergence rates of the deviation of kernel density estimator with respect to its mean(and the true density function)are derived.Secondly,the corresponding strong convergence rates are investigated.It is showed,under mild conditions on the kernel functions and bandwidths,that the optimal rates for the i.i.d.density models are also optimal for these processes.