1) non-Gaussian linear process
非高斯线性过程
2) non Gaussian process
非高斯过程
1.
This paper begins with the theory of identification of non Gaussian process in additive colored Gaussian noise, then analyses and reviews the cumulant methods to identify non Gaussian and non minimum phase ARMA model, which have been developed during recent years.
从利用高阶累积量对加性高斯有色噪声中非高斯过程辨识的基本理论出发 ,对近年来基于高阶统计量方法辨识非高斯、非最小相位 ARMA模型的算法进行了分析和综述 ,阐明了借助高阶统计量方法可以克服传统的基于2阶统计量方法在解决此类问题中的缺陷 ,有效地解决非高斯、非最小相位系统的辨识问
3) nonlinear process
非线性过程
1.
On-line nonlinear process monitoring based on sparse kernel principal component analysis;
基于稀疏核主元分析的在线非线性过程监控
2.
Through the simulation of a nonlinear process——pH controlling process, we can see the good stability and dynamic response of the algorithm.
通过对一个控制 p H值的非线性过程的仿真研究 ,表明该算法具有良好的稳定性和动态响应特
3.
Based on kernel ICA (KICA) model,the main contributions are as follows: firstly,a method to sort the rows of demixing matrix was introduced and the number of independent components was determined; secondly,the monitoring indices were extended into high-dimensional space by "kernel trick",and so a nonlinear process monitoring method was proposed.
在利用核ICA(KICA)建立过程非线性模型的基础上,根据核技巧,给出了一种高维空间分离矩阵的排序和独立元个数的选择方法,并将监控指标扩展到高维空间,从而提出一种基于KICA的非线性过程监控方法,解决了ICA对非线性过程监控效果不理想的缺点。
5) nonlinear processes
非线性过程
1.
Experimental studies of the characteristics of hot electron and nonlinear processes produced from different targets materials;
不同靶材超热电子和非线性过程特性的实验研究
2.
DKPLS based fault detection for nonlinear processes
基于DKPLS的非线性过程故障检测
6) nonlinear and non-Gaussian
非线性非高斯
1.
The particle filter is an effective algorithm for the state recursive estimation in nonlinear and non-Gaussian dynamic systems by utilizing Monte Carlo simulation.
粒子滤波是指利用Monte Carlo仿真方法处理递推估计问题的非线性滤波算法,这种方法不受模型线性和Gauss假设的约束,是一种处理非线性非高斯动态系统状态估计的有效算法。
2.
The Unscented Particle Filter (UPF) was considered as one of the most effective state estimation method for nonlinear and non-Gaussian system.
最后,将自适应UPF算法与粒子滤波、标准UPF算法进行了仿真比较,仿真结果表明在保持高精度估计能力的同时,自适应UPF算法比标准UPF算法具有更好的实时性,是解决非线性非高斯系统状态估计问题的一种有效方法。
补充资料：非自衡的非振荡过程
分子式：
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性质： 有些过程在输入阶跃作用下，被控变量会一直上升或下降，直到极限值。
CAS号：
性质： 有些过程在输入阶跃作用下，被控变量会一直上升或下降，直到极限值。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条