...(2009)04-1386-03 针对发酵过程发酵阶段具有模糊性的特点,提出了采用最小二乘支持向量机(least square support vector machines, LS-SVM)提取并简化模糊规则的推理优化控制方法.
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提出一种用最小二乘支持向量机(LS - SVM)构造函数链接型神经网络(FLANN)逆系统的传感器动态补偿新方法。
A dynamic compensating method for transducers is presented based on functional link artificial neural networks (FLANN) inverse system constructed by least squares-support vector machine (LS-SVM).
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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