IEEE Trans Neural Netw Learn Syst. 2013 May;24(5):831-7. doi: 10.1109/TNNLS.2013.2242486.
An energy-efficient support vector machine (EE-SVM) learning control system considering the energy cost of each training sample of biped dynamic is proposed to realize energy-efficient biped walking. Energy costs of the biped walking samples are calculated. Then the samples are weighed with the inverses of the energy costs. An EE-SVM objective function with energy-related slack variables is proposed, which follows the principle that the sample with the lowest energy consumption is treated as the most important one in the training. That means the samples with lower energy consumption contribute more to the EE-SVM regression function learning, which highly increases the energy efficiency of the biped walking. Simulation results demonstrate the effectiveness of the proposed method.
提出了一种考虑双足动态每个训练样本能量成本的节能支持向量机（EE-SVM）学习控制系统，以实现节能双足行走。计算双足行走样本的能量成本。然后，用能量成本的倒数对样本进行加权。提出了一个具有能量相关松弛变量的 EE-SVM 目标函数，该函数遵循的原则是，能耗最低的样本在训练中被视为最重要的样本。也就是说，能耗较低的样本对 EE-SVM 回归函数学习的贡献更大，这极大地提高了双足行走的能效。仿真结果验证了该方法的有效性。
