(2)系统研究了最小二乘支持向量机(least squares support vector machine，LSSVM)的原理和训练算法，且针对LSSVM超参数选择问题，研究了选择LSSVM回归模型超参数的G-LSSVM...
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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.
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.
并且采用了最小二乘支持向量机， 用等式约束取代了支持向量机中的不等式约束， 降低了运算量， 提高了学习效率。
The LS-SVM classifier is adopted, which replaces inequality constraints in SVM by equality constraints. So the computation consumption is reduced and the learning performance is improved.
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