实现了基于数据挖掘理论和最小二乘支持向量机短时预测的多接口远程智能供水监管系统。
A short-term prediction system based on data mining preparation and least squares support vector machine was presented for a multi-port remote monitoring and management system.
提出了基于粒子群算法(PSO)和最小二乘支持向量机(LSSVM)的边坡稳定性评价方法。
A slope stability evaluation method based on particle swarm optimization (PSO) and least square support vector machine (LSSVM) is proposed.
现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
介绍了最小二乘支持向量机计算法和滑动时间窗的建立。
The establishment of beast square support vector machine method and sliding time window is introduced.
针对土石坝渗透参数和测压管水位间复杂的非线性关系，应用最小二乘支持向量机于土石坝渗透系数的反演。
In view of complex nonlinear relationship between dam seepage parameters and piezometric tube level, the least squares support vector machine is applied to the back analysis of seepage parameters.
将基于改进粒子群优化最小二乘支持向量机的预测模型引入滴头水力性能预测领域，对所建立的滴头水性能预测模型分别从理论和试验两方面进行验证。
A novel prediction model for hydraulic performance of trickle irrigation emitter based on modi? Fied PSO least square support vector machine is proposed and verified by experiment.
本文提出了基于时间窗的最小二乘支持向量机t - S模型在线辨识算法，包括结构辨识和参数辨识。
In the paper online identification based on time window and least square support vector machine (LSSVM) for T-S model is proposed, including structure and parameter identification.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
该方法首先通过在加权最小二乘 支持向量机的基础上加入对数据偏斜的处理，解决了元 信息分类时关键词特征稀疏和样本高度不均衡问题；
Since the feature of the meta-information classification keywords is sparse and the distributing of sample is unbalanced, this thesis considered the factor of data skew based on LS-VSM.
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