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。     

扑热息痛片剂药品的近红外光谱法非破坏定量分析

现代近红外光谱分析技术将近红外光谱 (NIR)法同计算机科学和化学计量学结合 ,实现了对样品进行无损非破坏性定量分析 .该法具有速度快、操作简单及所需样品少等特点 ,能够实现样品分析的时间同步、地点同步及无损非破坏分析 .为实现生产过程中即时、在线的质量控制提供了新的手段[1 ] .本文应用人工神经网络 [2 ]与近红外漫反射光谱相结合对扑热息痛片剂药品进行了非破坏快速定量分析 .用扑热息痛片剂药品的近红外漫反射光谱数据、一阶导数光谱数据及二阶导数光谱数据分别建立了 ANN模型 ,预测未知样品 ,讨论了影响网络的因素 ,使用了新…     

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.     

HIV-1逆转录酶抑制剂2-氨基6-苯磺酰基苄腈及其类似物活性OSAR研究

首先利用半经验AM I量子化学方法计算了54个2-氨基-6-苯磺酰基苄腈及其类似物的物理化学、电子结构、指示变量等共28个参数,然后使用偏最小二乘,穷举回归和混沌遗传乘法训练的人工神经网络方法建立了这些参数和其抑制H IV-1逆转录酶活性之间的定量构效关系模型,为设计、合成更高生物活性的该类化合物提供了理论参考。     

Predicting kinetic parameters for coal devolatilization by means of Artificial Neural Networks

The chemical percolation devolatilization (CPD) model has been shown to represent the devolatilization process of different coals and heating conditions with good accuracy. However, its use in computational fluid dynamics is limited because of its relatively high computational cost. Here, an Artificial Neural Network (ANN) based model for predicting coal devolatilization kinetics is developed based on a database constructed with the CPD model for a wide range of coals and heating rates. The heating rates and the information of ultimate and proximate analysis are chosen as inputs of the ANN model to consider the effects of coal types and heating conditions on coal devolatilization; the outputs are the kinetic parameters for the two-step kinetic model. The learning, validation, and application results show that the proposed ANN model has a competitive prediction capability on both the total volatile release and release rates when compared with the CPD model, but has obvious computational efficiency advantages. Furthermore, the relative impact of the coal type and heating rate on each kinetic parameter for coal devolatilization is quantitatively evaluated through the Garson equation. It is found that the heating rate has the strongest effect on the pre-exponential factor, while the coal types show significant influence on the activation energy and final yield of the two reactions in the two-step model.