人工肺-嗅觉系统集成与混合气体识别方法

针对仿人机器人的嗅觉及多种混合气体识别问题,提出一种人工肺-嗅觉系统(HALOS-I)及基于主动呼吸的气体识别方法.该系统硬件主要集成了微型真空泵、酒精/硫化氢/氨气/烟雾/甲烷5种气体传感器、单片机以及信号采集与处理电路;分别用K-均值聚类分析法、遗传算法结合神经网络(GA+BP)、三级级联神经网络(GA+3BP)进行了5种单一气体及4种混合气体的识别实验,结果表明:GA+BP算法仅对5种单一气体识别率达到90%以上,加入混合气体后识别率较低;GA+3BP算法除对硫化氢和烟雾的混合气体识别率为70%以外,对其余8种气体识别率均在90%以上,表明GA与多级级联BP神经网络相结合方法对多种单一及混合气体具有较高的识别率.

Integration of artificial lung-olfactory sense system and identification method of gas mixtures

For olfactory sense and gas mixtures identification of humanoid robots, an artificial lung & olfactory sense system (HAL&OS-I) and its identification method through active breathing are proposed and researched. The integrated hardware of the system mainly consists of micro vacuum pump, five gas sensors for alcohol/hydrogen sulfide/ammonia/smoke/methane separately, and the single chip microcomputer along with the circuit boards for signal sampling and processing. Gas identification experiments of five pure gases and four gas mixtures were conducted by using K-mean cluster analysis method, genetic algorithm combined with neural network (GA+BP), cascade neural network (GA+3BP) separately. The experimental results show that the identification rate of five pure gases by the GA+BP algorithm is above 90%, but the identification rate is relatively low when the gas mixtures are included. Gas identification rate of all gases by the GA+3BP algorithm is more than 90% except the smog and hydrogen sulfide mixture gas of which the identification rate is 70%. It is revealed that the GA+nBP algorithm has higher identification rates for multiple pure and gas mixtures.