【作者】 刘超；

【导师】 陈小平；

【作者基本信息】 苏州大学 ， 信号与信息处理， 2008， 硕士

【摘要】 随着现代工业及科学技术的迅速发展,设备的安全性、可靠性和有效性变得越来越重要和突出,传统的故障诊断技术已不能满足需求,而人工神经网络(Artificial Neural Network,ANN)的迅速发展为故障诊断领域开辟了一条新的途径。但是针对具体问题来设计神经网络是一个很复杂的问题,网络的结构(隐含层数和各层神经元个数)、各层的激活函数和学习训练方法等往往需要根据设计者的经验和多次实验来确定,这样导致得到的神经网络并不是最优的,影响了故障诊断的性能。免疫算法(Immune Algorithm, IA)作为一种抽取和反映生物机体免疫系统特点的优化算法。它通过对抗体交叉、变异的进化操作和基于抗体浓度的调节操作,使抗体不断优化,从而找到最佳抗体。本文运用ANN的方法对水质监测系统进行故障诊断,并且引入免疫算法来优化神经网络,更好的满足实际应用需要,提高故障诊断水平。本文对水质监测系统的水质参数测量原理进行分析,建立了水质故障诊断的动态模型。选用反向传播(Back Propagation,BP)网络进行水质监测系统的故障诊断。提出用免疫算法优化BP网络,将网络结构、激活函数和训练方法等编码作为个体,进行免疫操作,得到最优或次优解,克服了网络结构、激活函数和训练方法的确定没有可循规则的弊端。以PC机和PCI9112数据采集卡为硬件基础,虚拟仪器Labwindows/CVI为软件开发平台,采用在Labwindows/CVI环境下调用Matlab程序的方式实现免疫算法优化BP网络的软件设计,完成水质监测系统故障诊断的界面,功能包括神经网络离线训练学习和在线故障诊断。最后对免疫算法优化的神经网络和经验得到的神经网络进行性能比较和分析。结果表明,在收敛速度和最小均方误差方面均优于经验得到的神经网络。更多还原

【Abstract】 With the rapid development of modern industry and technology, the security, reliability and validity become more and more important. While traditional fault diagnosis can not meet the requirement, a new method is found in the field of fault diagnosis when Artificial Neural Network (ANN) is prompt progressing. But it is very complex to design neural network for the specifical problem. The network structure, activation function and training method are usually acquired according to the designer’s experience and many tries, it makes that the solution is not optimum and reduces the performance.Immune Algorithm (IA) is a kind of optimization algorithms which comes from immune system’s characteristic of biologic system. It makes antibody optimize continually by using cross and mutation, adjusting based on antibody concentration, and then gets the optimum antibody. So in this paper, the ANN, which is optimized by IA, is used in the fault diagnosis of water-quality monitor to get better for practical application and raise the level of fault diagnosis.The paper studies the measuring principle of water-quality parameters in the monitor system, and establishes the dynamic model of fault diagnosis about water-quality. Back Propagation (BP) network is used in the fault diagnosis of water-quality monitor. A method is presented to optimize BP network by IA, in which the network structure, activation function and training method are encoded as an individual, in the purpose of optimum solution. The problem has been availably solved that there isn’t a guided rules to specify the network structure, activation function and training method. Hardware in the project is based on computer and PCI 9112 DAQ card, and software is based on virtual instrument Labwindows/CVI. The software design is finished for optimal BP via IA in Labwindows/ CVI by calling ANN function of Matlab. The interface of water-quality monitoring system is accomplished with the function of ANN training off line and fault diagnosis on line. Finally, the performance is compared and analyzed between optimized network by IA and experiential network. The results show that the convergence rate and the mean squared-error are both better than the experiential network.更多还原