神经网络-遗传算法优化反应烧结ZrO2-SiC材料制备工艺

应用人工神经网络对反应烧结ZrO2－SiC材料制备中工艺参数与原位SiC颗粒生成量的关系进行拟合和预测,并结合遗传算法优化出了最佳制备工艺．     

过程神经网络的训练及其应用

研究过程神经网络的学习算法及其在过程模式识别中的应用。针对权值基展开的过程神经网络讨论了权值基的选取规则和对采样曲线的标准化处理问题,给出了含一个隐层的过程神经网络的误差反传播学习算法。以聚合化学反应和渗流实验两个具体实例验证了算法的有效性,也说明了过程神经网络的广泛应用前景。     

Establishment of a Fault Prognosis Model Using Wavelet Neural Networks and Its Engineering Application

Fault diagnosis is confronted with two problems; how to “measure“ the growth of a fault and how to predict the remaining useful lifetime of such a failing component or machine. This paper attempts to solve these two problems by proposing a model of fault prognosis using wavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets, respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm.     

An exploratory study of a neural network approach for reliability data analysis

The results of this paper show that neural networks could be a very promising tool for reliability data analysis. Identifying the underlying distribution of a set of failure data and estimating its distribution parameters are necessary in reliability engineering studies. In general, either a chi-square or a non-parametric goodness-of-fit test is used in the distribution identification process which includes the pattern interpretation of the failure data histograms. However, those procedures can guarantee neither an accurate distribution identification nor a robust parameter estimation when small data samples are available. Basically, the graphical approach of distribution fitting is a pattern recognition problem and parameter estimation is a classification problem where neural networks have been proved to be a suitable tool. This paper presents an exploratory study of a neural network approach, validated by simulated experiments, for analysing small-sample reliability data. A counter-propagation network is used in classifying normal, uniform, exponential and Weibull distributions. A back-propagation network is used in the parameter estimation of a two-parameter Weibull distribution.     

基于RBF神经网络的水文地质参数识别

水文地质参数识别问题是水文地质学上的一个难题.针对传统水文地质参数识别方法的局限性,提出了水文地质参数识别的径向基函数(RBF)神经网络方法,并通过算例验证了它的可行性与有效性,实现了水文地质参数的自动识别,提高了计算效率,比BP神经网络具有更好的参数识别效果.     

六自由度运动平台控制系统的神经网络优化

阐述了六自由度运动平台的控制原理，并根据控制系统的特点，提出采用基于RBF和BP神经网络来改进常规PID控制器实现系统控制性能。在该控制系统结构中，提出了在RBF网络辨识Jacobian的基础上，将BP神经网络引入了平台控制系统中PID控制器的控制参数在线整定的算法，最后给出了在MATLAB下的具体仿真算法。     

采用BP神经网络进行混沌运动控制

利用4层BP神经网络对非线性非连续函数的无穷逼近特性，设计了控制方法，对非线性系统的混沌运动进行控制。在系统发生混沌运动时，对于不同类型的期望信号，可以将不同自由度的混沌运动分别控制到各自的目标信号上。目标函数可以是周期函数，非线性函数。对Lorenz方程进行了仿真计算，结果显示可以将混沌运动控制到锯齿波信号、正弦信号和直流信号上；对Rossler方程的仿真计算，可以将系统的混沌运动控制到方波信号与直流信号上，控制所用的时间很短。     

电液伺服系统的神经网络建模方法研究

针对电液伺服系统固有的流量-压力特性等非线性因素使得采用传递函数等传统方法难以获得电液伺服系统的精确模型的问题,详细研究了电液伺服系统的神经网络建模方法.研究了两种最常见的神经网络,即多层感知器神经网络和径向基函数神经网络,采用5种典型学习算法构造了3种多层感知器神经网络和2种径向基函数神经网络,并结合自动定深电液伺服系统的工程实例,详细分析了这5种神经网络在电液伺服系统中的建模性能.研究结果表明,采用正交最小二乘算法的径向基函数神经网络最适合电液伺服系统的建模.     

人工神经网络在弹体侵彻混凝土深度中的应用

针对弹体对混凝土材料侵彻深度问题,通过量纲分析和神经网络理论,建立了弹体侵彻深度h网络输出量与弹体长度lp、弹的长径比lp/d、弹体形状系数ψ、弹体与混凝土的比强度σyt/σyp、弹体与混凝土的密度比ρp/ρt等13个网络输入量之间的非线性映射关系。并采用RBF网络模型,通过Forrestal等文献的试验样本对网络模型训练,获得了弹体对混凝土材料侵彻深度的网络模型,输出结果满意。     

A surrogate assisted evolutionary optimization method with application to the transonic airfoil design

A multi-layer perceptron neural network (NN) method is used for efficient estimation of the expensive objective functions in the evolutionary optimization with the genetic algorithm (GA). The estimation capability of the NN is improved by dynamic retraining using the data from successive generations. In addition, the normal distribution of the training data variables is used to determine well-trained parts of the design space for the NN approximation. The efficiency of the method is demonstrated by two transonic airfoil design problems considering inviscid and viscous flow solvers. Results are compared with those of the simple GA and an alternative surrogate method. The total number of flow solver calls is reduced by about 40% using this fitness approximation technique, which in turn reduces the total computational time without influencing the convergence rate of the optimization algorithm. The accuracy of the NN estimation is considerably improved using the normal distribution approach compared with the alternative method.     

人工神经网络的单调序列逼近

设Nn,Ф是以Ф为激活函数的具有n+1个神经元的前向单隐层人工神经网络的全体.主要证明了,若f∈C[0,1],则对于任意的ε>0,存在两个神经网络序列{Pn,Ф}和{Qn,Ф},使得在[0,1]上Qn,Ф(x)≤Qn+1,Ф(x)≤f(x)≤Pn+1,Ф(x)≤Pn,Ф(x),而且Pn,Ф(x)-Qn,Ф(x)≤(6+4(2~(1/2)))En,Ф(f),这里的En,Ф(f)为Nn,Ф中的元对f的最佳逼近.     

BP神经网络在谐波测量中的应用研究

本文主要介绍了一种基于BP神经网络的谐波测量方法。与传统的谐波测量方法相比，其在实时性及精确度上有了很大的改善。文章重点介绍了BP神经网络结构和学习训练原理，并且针对传统BP训练算法学习速度较慢的缺点，提出了一种能够加快训练速度的新方法。实验结果表明，在谐波测量电路里采用新方法训练好的BP神经网络是可行的。     

Model validation of simple-graph representations of metabolism

The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules.     

基于神经网络的瓷酒瓶瓶盖配色

利用先进的科学知识解决如今白酒包装中存在的瓷瓶及瓶盖的颜色协调问题,提出用神经网络来解决瓷酒瓶塑料瓶盖的配色问题,建立了相应的BP系统,并用实验仿真对该系统进行了验证.     

汽轮机故障诊断的遗传神经网络法

阐明了遗传算法与神经网络结合的必要必邮多层感知器神经网络作为遗传搜索的问题表示方式的思想。设计了遗传神经网络故障诊断模型,用这种新方法解决了汽轮机多故障诊断问题。新方法可以简化神经网络的结构并逃逸局部极小。诊断结果与实际相符,从而验证了该方法的有效性和实用性。     

基于BP神经网络的ETO产品配置设计方法

按订单设计(engineering-to-order, ETO)的定制产品因产品族结构比较复杂,产品间结构差异较大,设计过程涉及个人经验和灵感,并大量应用人机交互处理,难以实现设计自动化、程序化。人工神经网络模仿人脑结构及智能行为,具有大规模并行处理、容错、自组织和自适应能力及联想功能,符合ETO配置设计的特点。通过对ETO定制产品需求的分析,构建并训练具有一定结构和功能的BP神经网络,训练好的网络蕴含着ETO配置设计规则和经验。实例证明了该方法的可行性。