人工嗅觉系统关键技术研究进展

回顾了人工嗅觉系统的发展历史,在介绍了人类嗅觉机理、人工嗅觉仿生系统的原理与组成的基础上,详细阐述了关键技术如气体传感器技术、信号预处理技术和模式识别技术等的研究现状,剖析总结了人工嗅觉系统存在的问题,并对未来发展进行了展望.     

基于自组织特征映射网络的气体识别方法研究

针对气体传感器的交叉敏特性,提出将气体传感器阵列与模式识别上结合形成人工嗅觉系统,用于气体的定性识别和定量检测。利用６个半导体传感器组成阵列,并结合自组织特征映射网络模式识别技术准确地识别了ＣＯ、Ｈ２、ＣＨ４等三种气体,说明人工嗅觉系统能够提高气体伟感器的选择性,自组织牧映射网络应用于气体识别是可行的。     

电子鼻在食品工业中的应用研究进展

电子鼻是一种新颖的模拟人的嗅觉来分析、识别和检测复杂嗅味和挥发性成分的仪器。电子鼻由气体传感器、信号处理和模式识别系统等组成。介绍了电子鼻的组成和工作原理,综述了电子鼻在食品原料、加工、质量监测、成份检验以及农产品贮藏中的应用研究进展,并指出了电子鼻目前存在的主要问题。     

卫星通信系统的计算机测控实现

卫星通信系统长期可靠稳定运行保证,在一定程度上取决于测控系统;简要介绍了卫星通信系统基本组成和测试原理,对比以前人工手动的系统测控方法,采用了基于VXI和VISA相结合的技术,实现了一种卫星通信系统的计算机自动测控方案;介绍了采用该方案的硬、软件组成结构和实现方法,以及实施后的试验分析结果显示及精度,效率优于人工测试方法,同时也为卫星通信系统稳定可靠运行提供了更好的保证;最后结合最新的技术在测控领域的发展和数据采集遥测遥控领域中的应用提出改进建议和方法.     

机器视觉系统组成研究

机器视觉检测系统解决了传统人工检测中的许多问题，在很多行业中得到广泛的应用。文章介绍了机器视觉系统的组成及一些著名的机器视觉产品生产厂家，并且介绍了一套完整的机器视觉系统的组成方案。     

人工免疫系统及其在控制系统中的应用

人工神经网络系统理论的研究已经取得了较大的进展, 然而对于人工免疫系统理论的研究还刚刚起步. 本文介绍了免疫系统的特点、免疫系统的组成, 比较了免疫系统和中枢神经系统的相似性和差异性, 论述了人工免疫网络系统及其在复杂系统建模和控制中的应用. 最后对进一步的研究工作作了简单的展望.     

人工嗅觉技术在酒类鉴别中的应用

简述了人工嗅觉酒类鉴别系统的原理、结构。给出了气体传感器阵列的组成方法,进行了气体传感器温度、湿度补偿。并运用主成分分析法和BP神经网络对试验结果进行了分析。鉴别实验研究表明,经温度、湿度补偿后该系统不仅能鉴别不同香型的白酒,还能鉴别同一香型的白酒。     

基于前向神经网络的呈香物质识别方法研究

介绍了所研制的一个人工嗅觉装置,详细叙述该装置对呈香物质的识别过程。实验结果表明,采用SuperSAB算法可以显著地提高人工嗅觉装置对香气物质的分析识别速度,为在线检测提供了条件,同时实验结果的准确性也较满意。     

气体传感器阵列测试算法研究

采用人工神经网络中的BP神经网络处理阵列式气体传感器信号,气体传感器阵列与前馈神经网络模式识别技术结合而成的人工嗅觉系统被用来进行混合气体的定量分析.利用计算机模拟方法对非线性气体传感器阵列进行模拟,并运用MATLAB神经网络工具箱设计了BP网络,最后,对模拟数据进行了比较.结果表明:神经网络法具有非线性逼近能力强、识别率较高等特点.     

立德粉生产合成过程的计算机控制

本文介绍了立德粉合成过程的计算机控制方案,阐述了计算机控制系统的硬件组成,特别介绍了该控制系统中的pH值的前馈-反馈控制系统和流量比值控制系统。与人工操作的对比说明了该计算机控制系统的有效性。     

嗅觉混沌神经网络的研究和应用

Freeman对生物嗅觉神经系统建立了一套新型的混沌神经网络模型-K系列模型,与传统的人工神经网络相比,该网络不仅能很好的模拟生物嗅觉神经系统产生的混沌信号,而且提出了一种全新的信息处理和模式识别的概念和理论,是一种更接近生物系统的神经网络模型. 我们对该混沌神经网络进行了模拟研究,同时应用该网络对简单的图像模式和手写体数字进行了模式识别的探索,实验结果验证了KIII网络的模式识别能力,为这种新方法的进一步发展和应用打下了基础.     

基于人工神经网络的苹果气体识别方法研究

将气体传感器阵列与人工神经网络模式识别技术相结合,建立了电子鼻系统,对水果变化过程进行监控。尝试对3种不同状态(好、碰伤、坏)的苹果气体进行定性识别。实验结果表明:结合主成分分析的人工神经网络方式为模式识别、分类提供了快速准确的辨识方法,对红富士苹果进行分类时正确率在83.33%以上。     

An evaluation of an olfactory sensor and recognition system using metal-oxide semiconductor gas sensor with variable heater controller

An artificial olfactory, which is called an electronic nose system (e-nose), is studied for realizing new human–machine interface. The system consists of sensor unit and a signal processing unit. There are some types of sensors for the sensor unit, metal oxide semiconductor gas sensors (MOGS) and quartz crystal microbalance gas sensors are useful in our study. Our system in this paper has MOGS. Many of MOGS utilize an effect of an oxidation–reduction reaction on the surface of the sensors. One of the features of the sensor, the character of the sensor is changed by temperature of the sensors. In this paper, we build a extend output sensor unit using this feature, and show experimental result of classification applying multilayer perceptron. In the experiment, we choose soy sauce as classification targets because we are considering applying the system for management of cooked foods.     

Autonomous learning with complex dynamics

Traditionally, associative memory models are based on point attractor dynamics, where a memory state corresponds to a stationary point in state space. However, biological neural systems seem to display a rich and complex dynamics whose function is still largely unknown. We use a neural network model of the olfactory cortex to investigate the functional significance of such dynamics, in particular with regard to learning and associative memory. the model uses simple network units, corresponding to populations of neurons connected according to the structure of the olfactory cortex. All essential dynamical properties of this system are reproduced by the model, especially oscillations at two separate frequency bands and aperiodic behavior similar to chaos. By introducing neuromodulatory control of gain and connection weight strengths, the dynamics can change dramatically, in accordance with the effects of acetylcholine, a neuromodulator known to be involved in attention and learning in animals. With computer simulations we show that these effects can be used for improving associative memory performance by reducing recall time and increasing fidelity. the system is able to learn and recall continuously as the input changes, mimicking a real world situation of an artificial or biological system in a changing environment. © 1995 John Wiley & Sons, Inc.     

A neural network model of the olfactory system of mice: computer simulation of the attention behavior of mice for some components in an odor

Recently, it was observed that mice could identify an odor by paying attention to only a few of its components. Further, it has been reported that each individual is attracted to different components of an odor. This behavior is referred to as “attention”; however, its mechanism has yet to be completely elucidated. In this paper, we first propose a novel artificial neural network model based on the biological structure of an olfactory system. Then a series of computer simulations of odorant discrimination are performed to evaluate the attention ability of the proposed model. Finally, we changed the connective weights between the neurons to simulate individual differences. The simulation results indicate that the inhibitory connections from the piriform cortex to the olfactory bulb may contribute to the individual differences observed in the behavioral experiment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

结合基因工程的生物电子鼻用于气味检测的研究

为了实现结合生物工程的仿生嗅觉传感器系统的多气味检测和目标气味的特异性识别和区分,使用生物工程化的大鼠嗅觉系统作为敏感元件,植入嗅球的多通道微丝电极作为换能器,采用无线数据采集系统收集和分析响应信号。最后,利用Matlab计算神经元平均放电率,对神经元的气味响应进行相关性分析和主成分分析,证实了该系统检测和区分不同气味的性能。创新性地提出将嗅觉受体基因整合、克隆到在体嗅觉细胞上,用于改造生物嗅觉系统,使之可以对目标气味分子产生敏感的响应信号。结果表明结合基因工程的生物电子鼻系统能够显著增强其检测特异性目标气味的性能,在环境监测、公共安全等领域中有很大应用潜力。     

人工系统中数据–因素–算力–算法作用及相互关系研究

目前的研究认为人工智能的核心是数据、算法和算力,但因素在形成人工智能系统过程中是必不可少的。论文针对人工系统中的数据、算力、算法和因素的各自作用,及其相互关系进行了探讨和论证。从人工系统的内涵出发,描述人工系统的发展过程,认为目前和今后的人工系统必将具有人工智能特征,而实现人工系统要充分考虑上述四方面的相互作用。研究结果表明:数据是人工系统辨识因素的基础,也是形成算法的基础;因素是人工系统控制自然系统的方法及算法所需变量;算法体现了因素与数据关系,可描述人工系统结构;算力是解算算法的能力,也需考虑数据和因素的特征。因此因素在人工系统建立过程中与数据、算法和算力具有相同的重要性。它们具有明显的作用关系,且普遍存在于各个学科,是形成各学科理论基础体系的关键。     

Computation of molecular information in mammalian olfactory systems

The olfactory system is unique in that the sensory input is in the form of molecular information carried in odour molecules and that a huge variety of compounds can function as odour molecules. The mammalian olfactory system has neuronal networks that can process and integrate the molecular information for discrimination of odour molecules and for perception of olfactory images of objects. Recent rapid advances have begun to provide new insights into the functional logic employed by the main olfactory system for odour discrimination, for odour classification and for olfactory perception of objects. This review discusses how the odour molecule information is encoded, transmitted, processed and decoded at distinct anatomical structures (sensory epithelium, olfactory bulb and olfactory cortex) of the olfactory nervous system. We also discuss the functional network in the accessory olfactory system with regard to the processing of pheromonal information.