Gaussian process and its application to soft-sensor modeling;
高斯过程及其在软测量建模中的应用
Time series prediction of foundation pit displacement using Gaussian process method;
基坑位移时间序列预测的高斯过程方法
The law of iterated logarithm with finite partial sum for the Gaussian process;
高斯过程下的有限项部分和重对数律
Gaussian processes(GP) are probabilistic kernel machines and moderately simple to implement and use without loss of performance compared with other kernel methods.
高斯过程一种具有概率意义的核学习机,在不损失性能的条件下,与其它核方法相比,有着其容易实现的优点。
The asymptotic distributions of M(α) uniform for α∈S p-1 are thereby derived, which are supremums of the Gaussian processes on S p-1 .
文中对一类稳健的平均绝对离差Ｍ（α）进行了讨论，得到了它的渐近表示式，并由此推出Ｍ（α）关于α一致地渐近分布为高斯过程的上界。
We propose a novel modeling approach using Gaussian processes(GP).
提出了一种基于高斯过程的软测量建模方法,高斯过程是一种有着概率意义的核学习机,在不牺牲性能的条件下,与人工神经网络和支持向量机相比具有实现简单的特点,理论分析和仿真研究表明了高斯过程在软测量建模中的优越性。
We mainly use the discrete stationary Gauss processes as the input and output signals of two-dimensional linear system to estimate the impulse transfer function,and we discuss some properties about the estimation of the impulse transfer function.
利用离散的平稳高斯过程族作为二维线性系统的输入和输出来对其脉冲传递函数进行了估计,并讨论了脉冲传递函数估计的渐近性质和极限定理。
With Gauss process and EI method calibration individuals can be selected effectively.
在该方法中,利用高斯过程所提供的预测标准差,通过引入EI方法,较好地解决了算法中校正个体的选择问题。
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
Forecasting Amount of Gas Emission Using Gaussian Process Machine Learning Model
瓦斯涌出量预测的高斯过程机器学习模型
Asymptotic Distribution of Maxima and Point Process of Locally Stationary Gaussian Process;
局部平稳高斯过程的最大值与点过程的渐近分布
On Limiting Distribution of Partial Sum and Maximum, and Point Process of Dependent Gaussian Process;
相依高斯过程的部分和与最大值、点过程的极限分布
Poincaré Inequality and Log-Sobolev Inequality for Stationary Gaussian Processes;
关于平衡高斯过程的Poincaré不等式和log-Sobolev不等式
HAUSDORFF DIMENSI ON OF GRAPH SETS AND WEEK VARI ATION FOR d -DIMENSI ON STATIONARY GAUSSIAN PROCESSES;
d维平稳高斯过程图集的Hausdorff维数及弱变差
Forecast of rock burst intensity based on Gaussian process machine learning
基于高斯过程机器学习的冲击地压危险性预测
Reinforcement Learning for Continuous Spaces Based on Gaussian Process Classifier
基于高斯过程分类器的连续空间强化学习
Application of Gaussian process machine learning to slope stability evaluation
高斯过程机器学习在边坡稳定性评价中的应用
Non-Gaussian Process Monitoring Based on NGPP-SVDD
基于NGPP-SVDD的非高斯过程监控及其应用研究
Approximate Implementation of Logarithm of Matrix Determinant in Gaussian Process Regression and Numerical Experiments
高斯过程回归中的logdet近似算法及数值实验
A spatial Gaussian process method for hyperspectral remote sensing imagery classification
用于高光谱遥感图像分类的空间约束高斯过程方法
Multi-model Modeling method Based on Affinity Propagation Clustering and Gaussian Processes
一种基于仿射传播聚类和高斯过程的多模型建模方法
Study on Gaussian process machine learning method for identifying the distribution structure of reservoir water temperature
水库水温分布结构识别的高斯过程机器学习方法
Non-Gaussian process monitoring and fault reconstruction and diagnosis based on SVDD
基于支持向量数据描述的非高斯过程故障重构与诊断
stationary gaussian markovian process
平稳高斯马尔可夫过程
Gas Comprehensive Management in High Gas Coal Seam in Didao Shenghe Coal Mine
高瓦斯煤层采掘过程中的瓦斯综合治理
Formation of Goss texture in HiB silicon steels analyzed by EBSD technique
HiB钢中高斯织构形成过程的EBSD分析
Randomness in this case is modelled as a Gaussian white noise process.
模式中之随机性乃视为一高斯白噪音过程。