基于贝叶斯证据框架下的最小二乘小波支持向量机，设计了一种新型模拟电路故障诊断方法。
Based on least squares wavelet support vector machines (LS-WSVM) within the Bayesian evidence framework, a systematic method for fault diagnosis of analog circuits was proposed.
在有限元模拟基础上，采用正交设计与最小二乘小波支持向量机对充液拉深过程参数优化进行了研究。
Orthogonal design and least squares wavelet support vector machine are integrated to optimize the technological parameters of hydro-mechanical deep drawing process using FEM.
考虑到电梯交通流本身所存在的非线性、复杂性和随机性，提出了一种基于小波支持向量机的电梯交通流预测模型。
Considering the nonlinearity, complexity and randomicity of elevator traffic flow, the prediction model of elevator traffic flow based on wavelet support vector machines was proposed.
采用小波多分辩率分析方法提取基因表达的特征，利用支持向量机和BP神经网络方法进行分类。
The features of gene expression are extracted by the wavelet multi-resolution analysis, the features are classified by the support vector machines and BP neural network methods.
本文主要研究目前较为流行的基于统计学习理论的分类方法——支持向量机方法(SVM)，以及小波变换提取特征的方法，将其用于人脸检测。
This paper mainly make research on classify methods based on statistical theory, support vector machine (SVM), and feature extraction method-wavelet transform, and using them in human face detection.
针对木材干燥系统强耦合非线性的特性，提出了一种基于小波最小二乘支持向量机的预测控制方法。
Aiming at the strongly coupling nonlinear characteristics of the timber desiccation system, the predictive control method based on wavelet least square support vector machine is proposed.
文中使用小波变换来对人脸的高维图像矢量进行压缩，并设计了一个支持向量机分类器系统来识别人脸。
This article utilizes the wavelet transform to compress the high dimensional face image vectors, then devises an SVM classify system to recognition the face.
该模型具有小波变换的良好时、频域分辨能力和支持向量机的非线性学习能力；
The model combines good time domain, frequency domain resolving ability of wavelet transformation and nonlinear learning ability of SVM.
同时利用粒子群算法优化小波最小二乘支持向量机的参数，避免了人为选择参数的盲目性，从而提高了模型的预测精度。
The adaptive particle swarm optimization is used to optimize the parameters of SVM so as to avoid artificial arbitrariness and enhance the forecast accuracy.
同时利用粒子群算法优化小波最小二乘支持向量机的参数，避免了人为选择参数的盲目性，从而提高了模型的预测精度。
The adaptive particle swarm optimization is used to optimize the parameters of SVM so as to avoid artificial arbitrariness and enhance the forecast accuracy.
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