介绍了几种新的基于核函数方法的软测量建模技术，并提出了针对复杂工业过程的混合核函数软测量建模方法。
Several new modeling technologies based on kernel function are introduced and the modeling method of soft measurement based on hybrid kernel function is stated for complicated industrial processes.
本文提出了基于支持向量机模型预测钢淬透性的方法，并分析了核函数的选择对支持向量机建模的影响。
In this paper, an SVM-based approach applied to predict steel quenching degree is presented, and the effects of selecting kernel function on SVM modeling are also analyzed.
基于离散时频分布的信号识别方法，将时频核设计问题转化为以信号自模糊函数为原始特征的特征选择问题，以实现特征降维和信号识别。
Actually the kernel design in the recognition method based on discrete time - frequency representation is a problem of feature selection from the ambiguity functions to reduce feature dimension.
针对间歇生产过程的配方缺少定量分析方法，难以用于过程建模和控制策略实施的问题，提出了一种基于类核函数的配方模糊聚类算法。
This paper presents a new fuzzy cluster analysis method for batch process recipe that has less quantitative analysis ways before and is difficult to be used for modeling or controlling system.
分析了基于记忆库混沌时间序列预测方法，引入一种改进核函数的支持向量机分类器。
Secondly the prediction technology of chaotic time series is studied based on memory-based predictor.
本文基于非参数核密度估计与核回归估计的基础上，介绍了合理选取核函数及窗宽的原则和方法。
This paper introduced the selection principle and method about a reasonable kernel function and bandwidth based on the nonparametric kernel density estimation and kernel regression estimation.
本文在研究了众多边缘检测方法的基础上，重点研究了最小二乘支持向量机（LS-SVM）的图像边缘检测技术，提出了一种基于混合核函数最小二乘支持向量机的图像边缘检测方法。
On the basis of studying on least-squares support vector machines (LS-SVM) of the image edge detection technology, Proposed a new method, which is based on mixed Kernel LS-SVM image edge detection.
实验结果表明使用我们构造的核函数的支持向量机可以取得比现有的基于序列的方法更好的结果。
Experimental results show that the constructed kernels used with an SVM classifier perform better than the existing sequence-based methods.
提出了基于核函数主元分析的齿轮故障诊断方法。
An approach to gear fault diagnosis is presented, which bases on kernel principal component analysis (KPCA).
该方法不仅考虑了样本点到类中心的距离，而且还考虑了样本间的密切度，结合这两种思想在特征空间中构造了一种新的基于动态核函数的模糊隶属度。
The fuzzy membership is defined not merely by the distance between a point and its class center, but also by two different points of the sample, which is depicted as the affinity between them.
基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用，运用支持向量机的关键是设计有效的核函数。
Kernel-based Support Vector Machine (SVM) is widely used in many fields (e. g. image classification) for its good generalization, in which the key factor is to design effective kernel functions.
基于核方法的支持向量机(SVM)以其良好的推广性在图像分类等领域已经得到广泛应用，运用支持向量机的关键是设计有效的核函数。
Kernel-based Support Vector Machine (SVM) is widely used in many fields (e. g. image classification) for its good generalization, in which the key factor is to design effective kernel functions.
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