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.
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.
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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