电子鼻运用SSNN算法进行化学品分类识别研究
sampling spiking neural network electronic nose on a tiny-chip
电子鼻运用ssnn算法进行化学品分类识别研究
hoda s. abdel-aty-zohdy
microelectronics & bio-inspired systemd design
department of electrical and computer engineering
oakland university, rochester, mi, usa
jacob n. allen
microelectronics & bio-inspired systemd design
department of electrical and computer engineering
oakland university , rochester, mi, usa
abstract
chemicals classification using a new sampling spiking neural network (ssnn) approach is presented in this paper with experimental measurements using the cyranose 320 sensor array. the network is unique in its minimal yet powerful design which implements on chip learning and parallel monitoring to detect
binary odor patterns with high noise environment. the ssnn architecture is further implemented on a 0.5 um cmos technology tiny-chip designed to work in conjunction with a 256k external sram memory. it handles the routing of spike signal among 32,000 synapses and 255 neurons. at the same time, it tracks and records learning statistics. the chip can be used in parallel with other ssnn co processors for very large systems. experimental measurements of our ssnn e-nose classifier, compared to other e-nose systems proved superior in capability, size, and correctness.
本文提出了一种新的神经网络(ssnn)方法对化学品进行分类识别，并利用cyranose 320电子鼻进行了实验测量。该网络的*之处在于其小但功能强大的设计，实现了片上学习和并行监控以检测高噪声环境下的二元气味模式。ssnn架构进一步实现在0.5umcmos技术的微型芯片上，该芯片设计为与256k外部sram存储器协同工作。它处理32000个突触和255个神经元之间的尖峰信号路由。同时，对学习统计数据进行跟踪记录。对于非常大的系统，该芯片可以与其他ssnn协处理器并行使用。与其他电子鼻系统相比，我们的ssnn电子鼻分类器的实验测量结果在性能、尺寸和正确性方面都表现出了优越性。
激光在线气体分析仪在脱硝微量氨检测中的实际使用效果
Fluke 709H精密回路校准仪技术参数
换气老化试验箱维护保养
数控铣方机对刀前的准备工作
SG立式管道泵结构解释与故障排除
电子鼻运用SSNN算法进行化学品分类识别研究
LED发光字安装如何更好地防水？
便携式超声波流量计用途包括但不限于以下几个方面
油罐需要的注意些什么？
电动提升机散热风扇如何清理
化学制药废水处理方案
要想烘干菊花没有黑头？诸城明超食品机械帮您解决
胶囊颗粒检重秤，高速药品分选检重机厂家
怎样区分电动堆高车和电动叉车
高效生物除臭设备净化效果好，除臭效率高
单联电炉直销要求
滚动包装机怎么有贵有便宜呢，区别到底在哪？
AT600型美国panametrics夹式流量计安装方便
“冷热水长轴深井泵：特点、应用领域与性能参数“
紫外线鼠迹检测仪
电子鼻运用ssnn算法进行化学品分类识别研究
hoda s. abdel-aty-zohdy
microelectronics & bio-inspired systemd design
department of electrical and computer engineering
oakland university, rochester, mi, usa
jacob n. allen
microelectronics & bio-inspired systemd design
department of electrical and computer engineering
oakland university , rochester, mi, usa
abstract
chemicals classification using a new sampling spiking neural network (ssnn) approach is presented in this paper with experimental measurements using the cyranose 320 sensor array. the network is unique in its minimal yet powerful design which implements on chip learning and parallel monitoring to detect
binary odor patterns with high noise environment. the ssnn architecture is further implemented on a 0.5 um cmos technology tiny-chip designed to work in conjunction with a 256k external sram memory. it handles the routing of spike signal among 32,000 synapses and 255 neurons. at the same time, it tracks and records learning statistics. the chip can be used in parallel with other ssnn co processors for very large systems. experimental measurements of our ssnn e-nose classifier, compared to other e-nose systems proved superior in capability, size, and correctness.
本文提出了一种新的神经网络(ssnn)方法对化学品进行分类识别，并利用cyranose 320电子鼻进行了实验测量。该网络的*之处在于其小但功能强大的设计，实现了片上学习和并行监控以检测高噪声环境下的二元气味模式。ssnn架构进一步实现在0.5umcmos技术的微型芯片上，该芯片设计为与256k外部sram存储器协同工作。它处理32000个突触和255个神经元之间的尖峰信号路由。同时，对学习统计数据进行跟踪记录。对于非常大的系统，该芯片可以与其他ssnn协处理器并行使用。与其他电子鼻系统相比，我们的ssnn电子鼻分类器的实验测量结果在性能、尺寸和正确性方面都表现出了优越性。
激光在线气体分析仪在脱硝微量氨检测中的实际使用效果
Fluke 709H精密回路校准仪技术参数
换气老化试验箱维护保养
数控铣方机对刀前的准备工作
SG立式管道泵结构解释与故障排除
电子鼻运用SSNN算法进行化学品分类识别研究
LED发光字安装如何更好地防水？
便携式超声波流量计用途包括但不限于以下几个方面
油罐需要的注意些什么？
电动提升机散热风扇如何清理
化学制药废水处理方案
要想烘干菊花没有黑头？诸城明超食品机械帮您解决
胶囊颗粒检重秤，高速药品分选检重机厂家
怎样区分电动堆高车和电动叉车
高效生物除臭设备净化效果好，除臭效率高
单联电炉直销要求
滚动包装机怎么有贵有便宜呢，区别到底在哪？
AT600型美国panametrics夹式流量计安装方便
“冷热水长轴深井泵：特点、应用领域与性能参数“
紫外线鼠迹检测仪