基于深度学习的模型识别系统为人类和类人机器人开发生物启发的概率控制模型的模块化结构
Deep Learning Based Model Identification System Exploits the Modular Structure of a Bio-Inspired Posture Control Model for Humans and Humanoids
摘要：
本文提出了一种基于卷积神经网络（CNN）的人体姿态控制系统辨识方法。所提出的控制模型的模块化结构启发了模块化识别过程的设计，在这个意义上，相同的神经网络用于识别控制不同自由度的模块的参数。通过这种方式，所呈现的由外部刺激引起的身体摇摆的示例一次提供多个训练样本
英文摘要：
This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
