title: Energy-efficient SVM learning control system for biped walking robots.
abstract: 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.
authors.forename: Liyang
authors.initials: L
authors.lastname: Wang
authors.forename: Zhi
authors.initials: Z
authors.lastname: Liu
authors.forename: Chun Lung Philip
authors.initials: CL
authors.lastname: Chen
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhang
authors.forename: Sukhan
authors.initials: S
authors.lastname: Lee
authors.forename: Xin
authors.initials: X
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012371:Robotics; D060388:Support Vector Machine; D016138:Walking