Interval type-2 fuzzy weighted support vector machine learning for energy efficient biped walking
作者:Liyang Wang, Zhi Liu, C. L. Philip Chen, Yun Zhang
摘要
An interval type-2 fuzzy weighted support vector machine (IT2FW-SVM) is proposed to address the problem of high energy consumption for biped walking robots. Different from the traditional machine learning method of ‘copy learning’, the proposed IT2FW-SVM obtains lower energy cost and larger zero moment point (ZMP) stability margin using a novel strategy of ‘selective learning’, which is similar to human selections based on experience. To handle the uncertainty of the experience, the learning weights in the IT2FW-SVM are deduced using an interval type-2 fuzzy logic system (IT2FLS), which is an extension of the previous weighted SVM. Simulation studies show that the existing biped walking which generates the original walking samples is improved remarkably in terms of both energy efficiency and biped dynamic balance using the proposed IT2FW-SVM.
论文关键词:Biped robot, Energy cost, Fuzzy logic system, Support vector machine
论文评审过程:
论文官网地址:https://doi.org/10.1007/s10489-013-0472-2