基于径向基网络的液冷服人体实验非线性模型辨识  

Nonlinear Model Identification Based on RBF Neural Network in Human Experiment of Liquid Cooling Garment

作　　者：马峰[1] 李潭秋[1] 

机构地区：[1]航天医学工程研究所,北京100094

出　　处：《航天医学与医学工程》2005年第6期432-436,共5页Space Medicine & Medical Engineering

基　　金：国家863项目(2002AA743030)资助

摘　　要：目的建立液冷服人体实验的非线性数学模型,研究人体状态参数与液冷服入口水温的关系。方法根据人体热学特性和以前的实验数据,运用径向基(RBF)神经网络辨识建模,考察了网络对该实验系统建模的适应性。结果RBF液冷服人体网络对人体状态和液冷服相关数据有很好的辨识能力,逼近速度快。结论RBF网络适合本文仿真实验,有利于今后实时的自适应控制。Objective To establish a nonlinear mathematical model for human experiment of liquid cooling garment （LCG）, and to study the relationship between human status parameters and the inlet temperature of LCG. Method Based on thermodynamic characteristic of human body and previous experimental data, a model was set up by radial basis function （RBF） neural network. And the applicability of the neural network was investigated, Result RBF neural network had good identifiable ability and fast impending speed to the simulation experiment. Conclusion RBF neural network is very suitable to the experiment and beneficial for the future real-time adaptive control.

关 键 词：舱外航天服 液冷服 径向基神经网络 模型辨识 温度调节 

分 类 号：R852.81[医药卫生—航空、航天与航海医学]