储液容器跌落冲击动力学神经网络建模

运用LS-DYNA程序中的ALE算法模拟储液容器在不同的跌落角度、跌落高度、壳体厚度下的跌落冲击过程,获取神经网络预测模型的训练样本集;利用BP神经网络建立储液容器结构参数、跌落冲击参数与接触点最大应力之间的映射关系预测模型,并将各种参数下的接触点最大应力网络预测值与仿真值比较,两者差异较小,表明该方法是有效的,可以为实际生产过程中参数选择提供理论依据.     

基于光谱和神经网络模型的作物与杂草识别方法研究

利用光谱技术来识别作物与杂草是精细农业中一个非常重要的研究内容,但光谱数据中含有大量冗余数据,如何预处理以及建立识别模型,是决定识别准确率的关键。利用在325～1 075 nm波段的光谱识别了三种杂草（牛筋草、凹头苋、空心莲子草）与大豆幼苗。在幼苗生长的第三周与第六周分别采集杂草与作物的光谱,共378个样本。用其中的250个光谱样本,包括第一期和第二期采集的光谱样本,在采用db12小波经过三层分解后,将其小波系数作为输入数据建模,构造了一个径向基函数神经网络。然后,利用余下的光谱样本检验该模型的识别能力。结果表明,该模型对作物与杂草光谱具有极强的识别能力,只有3个第二期的牛筋草样本被判断为空心莲子草,其余的样本全部正确识别。这个结果表明,采用可见/近红外光谱识别大豆幼苗与三种伴随生长的杂草是可行的,同时也说明,随着作物的生长阶段的不同,其光谱的变化不会影响到种类识别。     

基于人工神经网络的混响时间预测方法

在分析混响时间的各种影响因素的基础上,运用人工神经网络的BP算法,建立了厅堂混响时间的预测模型,该方法具有速度快、使用方便、精度高、可考虑的因素多等特点,具有一定的工程实用价值,有待实践的考验并逐步提高完善。     

城市交通管理中的出租车规划3

根据城市的发展情况以及类比其他城市的发展状况,对城市交通出行情况建立模型,对城市出租车的数量及乘坐人口做出预测;并针对目前的油价上涨给出租车行业带来的收入问题做出分析;建立最优化问题对驾驶员和顾客的满意进行描述,最终给出合理的价格调整方案.     

Genetic Algorithm Based Potential Selection in Simultaneous Voltammetric Determination of Isoniazid and Hydrazine by Using Partial Least Squares (PLS) and Artificial Neural Networks (ANNs)

The voltammetric behavior of isoniazid and hydrazine at an overoxidized polypyrrole modified glassy carbon electrode has been investigated. The obtained cyclic voltammograms showed that their oxidation peaks were overlapped and it is difficult to determine them individually from a mixture without separation. To overcome this limitation, a procedure was proposed for resolution of overlapped voltammetric signals from mixtures of isoniazid and hydrazine. In this procedure, genetic algorithm was used for the selection of potentials for partial least squares. A feed forward artificial neural network with back propagation error algorithm was used to process the nonlinear relationship between currents and concentrations of hydrazine and isoniazid. The proposed method was suitable for determination of isoniazid in pharmaceutical tablets and detection of hydrazine impurities in the same samples.     

模式识别用于压电晶体传感器阵列识别可燃物质

用７个压电晶体组成传感器阵列,每个晶体上分别涂有不同种类的气相色谱固定液,通过测定各种可燃物质燃烧时放出的混合气体来识别所燃物质,在识别中分别应用了人工神经网络法（ＡＮＮ）和逐步判别分析法（ＳＤＡ）。讨论了解决神经网络开始训练时不收敛或产生麻痹现象的方法,提出了训练数据选取的新方法－训练集逐步扩展法,实验证明：人工神经网络对被测物质的识别准确率达１００％,高于逐步判别分析法（８３％）。     

Optimization of artificial neural network for retention modeling in high-performance liquid chromatography

An artificial neural network (ANN) model for the prediction of retention times in high-performance liquid chromatography (HPLC) was developed and optimized. A three-layer feed-forward ANN has been used to model retention behavior of nine phenols as a function of mobile phase composition (methanol-acetic acid mobile phase). The number of hidden layer nodes, number of iteration steps and the number of experimental data points used for training set were optimized. By using a relatively small amount of experimental data (25 experimental data points in the training set), a very accurate prediction of the retention (percentage normalized differences between the predicted and the experimental data less than 0.6%) was obtained. It was shown that the prediction ability of ANN model linearly decreased with the reduction of number of experiments for the training data set. The results obtained demonstrate that ANN offers a straightforward way for retention modeling in isocratic HPLC separation of a complex mixture of compounds widely different in pK_a and log K_ow values.     

基于时间序列的网络安全态势预测方法的研究

互联网上每天充斥着大量入侵与攻击事件,已经严重威胁到网络安全,如何把握网络目前安全态势并预测其发展趋势已成为国内外网络应急响应的热点研究内容之一.根据网络安全事件发生的统计规律性,分别研究了基于时间序列的神经网络和灰色系统理论对未来一段时间内的安全事件数量进行预测的方法,并在此基础上对未来的安全态势进行评估,从而得知网络的整体安全运行状况,有效指导了管理员对网络安全的控制.