因此,论文应用小脑模型关节控制器(CMAC)神经网络和在线支持向量机(Online SVM)对CSPS系统停止在线优化。论文首先应用CMAC神经网络逼近Q学习中具有连续行动值白勺Q值函数,并给出相应白勺在线Q学习。
基于8个网页-相关网页
One is OSVM-Q, online SVM is set for each exploration state. The other is OSVM-Q-1, only one online SVM is set for all state-action of CSPS system.
另一种是只设置一个在线支持向量机，用来逼近CSPS系统的所有状态-行动对的Q值函数的OS VM - Q - 1算法。
And its learning procedure includes two parts: offline training of SVM and online training of fuzzy scale factors.
学习过程分为离线学习支持向量机和在线整定模糊比例因子两部分。
An advantage of the incremental algorithm is that it can be used to train SVM model online, which largely extends the application area of SVM.
增量型的支持向量机训练算法的一个重要特点是可以用于实时在线训练支持向量机的模型，这将大大扩展支持向量机的应用范围。
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