Artificial Neural Network and Full Factorial Design Assisted AT-MRAM on Fe Oxides, Organic Materials, and Fe/Mn Oxides in Surficial Sediments

Artificial neural network（ANN） and full factorial design assisted atrazine（AT） multiple regression adsorption model（AT-MRAM） were developed to analyze the adsorption capability of the main components in the surficial sediments（SSs）. Artificial neural network was used to build a model（the determination coefficient square r2 is 0.9977） to describe the process of atrazine adsorption onto SSs, and then to predict responses of the full factorial design. Based on the results of the full factorial design, the interactions of the main components in SSs on AT adsorption were investigated through the analysis of variance（ANOVA）, F-test and t-test. The adsorption capability of the main components in SSs for AT was calculated via a multiple regression adsorption model（MRAM）. The results show that the greatest contribution to the adsorption of AT on a molar basis was attributed to Fe/Mn（–1.993 μmol/mol）. Organic materials（OMs） and Fe oxides in SSs are the important adsorption sites for AT, and the adsorption capabilities are 1.944 and 0.418 μmol/mol, respectively. The interaction among the non-residual components（Fe, Mn oxides and OMs） in SSs interferes in the adsorption of AT that shouldn’t be neglected, revealing the significant contribution of the interaction among non-residual components to controlling the behavior of AT in aquatic environments.

Enhanced Self-Organizing Map Neural Network for DNA Sequence Classification

The artificial neural networks (ANNs), among different soft computing methodologies are widely used to meet the challenges thrown by the main objectives of data mining classification techniques, due to their robust, powerful, distributed, fault tolerant computing and capability to learn in a data-rich environment. ANNs has been used in several fields, showing high performance as classifiers. The problem of dealing with non numerical data is one major obstacle prevents using them with various data sets and several domains. Another problem is their complex structure and how hands to interprets. Self-Organizing Map (SOM) is type of neural systems that can be easily interpreted, but still can’t be used with non numerical data directly. This paper presents an enhanced SOM structure to cope with non numerical data. It used DNA sequences as the training dataset. Results show very good performance compared to other classifiers. For better evaluation both micro-array structure and their sequential representation as proteins were targeted as dataset accuracy is measured accordingly.

Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor （ATFBR） with a mesoporous granular activated carbon （GAC） as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate （OLR）, hydraulic retention time （HRT）, the efficiency of the reactor and to report on its steady- state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/（m^3·d）. The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m^3/（m^3·d） of the reactor. With the increase of OLR from 83.7 kg COD/（m^3·d）, the COD removal efficiency decreased. Also an artificial neural network （ANN） model using multilayer perceptron （MLP） has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error （MSE） was found to be only 0.0146.

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks

In agricultural systems,the regular monitoring of Soil Organic Matter(SOM)dynamics is essential.This task is costly and time-consuming when using the conventional method,especially in a very fragmented area and with intensive agricultural activity,such as the area of Sidi Bennour.The study area is located in the Doukkala irrigated perimeter in Morocco.Satellite data can provide an alternative and fill this gap at a low cost.Models to predict SOM from a satellite image,whether linear or nonlinear,have shown considerable interest.This study aims to compare SOM prediction using Multiple Linear Regression(MLR)and Artificial Neural Networks(ANN).A total of 368 points were collected at a depth of 0-30 cm and analyzed in the laboratory.An image at 15 m resolution(MSPAN)was produced from a 30 m resolution(MS)Landsat-8 image using image pansharpening processing and panchromatic band(15 m).The results obtained show that the MLR models predicted the SOM with(training/validation)R^(2)values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images,respectively.In contrast,the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images,respectively.Image pansharpening improved the prediction accuracy by 2.60%and 4.30%and reduced the estimation error by 0.80%and 1.30%for the MLR and ANN models,respectively.

Exploration of Nonlinear Modeling Techniques to Predict the Retention Time of Organic Pollutants in Natural Water and Wastewater

Water pollution affects plants and organisms living in these bodies of water; and, in almost all cases the effect is damaging not only to individual species and populations, but also to the natural biological communities. Genetic algorithm and kernel partial least square （GA-KPLS） and Levenberg- Marquardt artificial neural network （L-M ANN） techniques were used to investigate the correlation between retention time （tR） and descriptors for 150 organic contaminants in natural water and wastewater, which are obtained by gas chromatography coupled to high-resolution time-of-flight mass spectrometry （GC-TOF MS）. The L-M ANN model gave a significantly better performance than the GA-KPLS model. This indicates that L-M ANN can be used as an alternative modeling toot for quantitative structure-retention relationship （QSRR） studies.

基于深度学习的生物组织病理图像分析在海洋监测中的发展潜力及案例分析

生物组织病理指标可用于评价海洋生物健康,但在应用中存在效率低、成本高、主观性强等缺陷。将人工智能技术引入生物组织病理分析,可以发挥其高通量的图像分析优势,突破其在海洋生物健康评价和监测中的应用限制。该文通过对海洋生物组织健康评价指标、人工智能技术的图像分析应用以及利用人工智能开展组织病理图像处理的文献调研,提出基于深度学习的海洋动物组织病理图像分析思路,并以海洋贻贝作为模式生物进行技术开发。经过对贻贝鳃组织病理影像数据的训练、验证和预测等过程,确定Res-UNet深度学习模型可对贻贝在典型环境污染物胁迫下的病理损伤进行高效、准确定量,构建了一种能够自动化、高通量和弱主观性地分析海洋贻贝组织病理影像的工作流程,为海洋生物健康评价、海洋监测提供新思路与新技术。

Kohonen神经网络在遥感影像分类中的应用研究

根据Kohonen网的生物学基础 ,基本结构和学习算法 ,提出了解决遥感影像分类的途径。依据实验区土地利用类别的光谱特征 ,采用主成分分析对遥感影像进行预处理 ,结合地理辅助数据的量化输入训练出Kohonen自组织图后对融合有地理辅助数据的影像进行土地利用分类 ,并与BP网和最大似然法分类结果进行分析比较。结果表明 。

基于不同模型的土壤有机质含量高光谱反演比较分析

以新疆奇台县为研究区域,选取该县40个土壤样本,采用多元线性逐步回归法和人工神经网络法两种方法分别建立了土壤有机质含量的反演模型,并对模型进行了检验。结果发现:不同模型的精度值各异,其拟合效果从高到低依次为人工神经网络(ANNs)集成模型>单个人工神经网络(ANNs)模型>多元逐步回归(MLSR)模型。人工神经网络的线性和非线性逼近能力较强,而其集成模型作为提高反演模型精度的重要手段,相关系数高达0.938,均方根误差和总均方根误差最小,分别仅为2.13和1.404,对土壤有机质含量的预测能力与实测光谱非常接近,分析结果达到了较实用的预测精度,为最优拟合模型。

基于灰色神经网络的有机涂层寿命预测研究

暴露于大气腐蚀环境下的有机涂层使用寿命涉及因素繁多,难于精确预测。基于灰色理论和人工神经网络理论,建立了灰色神经网络模型对有机涂层腐蚀面积进行预测,进而预测其寿命。通过飞机结构模拟试验件加速腐蚀试验后的有机涂层寿命预测实例检验,发现灰色神经网络模型预测有机涂层寿命精度较高,结果理想。

人工神经网络法测定五组分红外光谱体系

介绍了人工神经网络在非线性多组分校准中的应用。所用三层神经网络由反向传播算法来训练。采用大量模拟数据训练神经网络 ,得到了一系列优化参数。选择红外谱图严重重叠的五种大气有机毒物作为多组分体系。相对标准误差 (RSD % )、百分标准预测误差 (SEP % )和百分标准校准误差 (SEC % )被用于衡量神经网络的性能。

基于人工神经网络的土壤有机质含量高光谱反演

研究了土壤有机质含量与土壤高光谱之间的关系,在对原始光谱进行了预处理分析后,运用多元线性逐步回归法(MLSR)和人工神经网络法(ANN)建立了土壤有机质含量的反演模型,并对模型进行了验证。结果表明:人工神经网络所建立的反演模型普遍优于回归模型,网络集成模型优于单个BP网络模型,网络集成是提高反演模型准确性与稳定性的有效途径。网络集成模型为最优模型,总均方根误差为1.31,可以用于土壤有机质含量的快速测算。

人工神经网络法在烃源岩测井评价中的应用

运用有机地球化学方法分析岩芯、岩屑样品的有机碳含量存在着昂贵、费时且不准确等问题 .利用测井方法的优点是经济、准确 .在测井评价中使用人工神经网络法具有极大的优越性和适用性 .本文结合Kohonen和BP网络方法 ,完成了塔里木台盆区 19口井的寒武、奥陶系烃源岩层段的识别与评价 .并通过测井资料处理成果和岩芯有机地化资料、地质录井情况的相互检验、证实 ,基本上能够满足评价烃源岩的需要 ,从而为利用测井资料进行烃源岩评价做出了新的尝试 .

自组织神经网络在有害赤潮预警研究中的应用

根据非线性理论及有害赤潮的特点,探讨应用人工神经网络原理进行赤潮预警预报的方法,提出网络可塑性较强、无监督的ART-KOHONEN网络模型,阐述了其基本原理和算法。将该模型应用于辽省营口市鲅鱼圈海域,结果表明:该方法能较理想地解决无训练样本的分类识别问题,具有较高的精度,可用于有害赤潮的预警预报工作。

自组织特征映射神经网络在测井岩性识别中的应用

为了解决测井岩性识别问题,引入具有较强的聚类和容错能力的自组织特征映射(SOFM)神经网络.在说明SOFM网络的模型和算法的基础上,结合某地的实际测井资料,建立SOFM网络岩性识别模型,进行岩性识别的应用研究.结果表明,识别的准确率较高,证明SOFM网络可以用于解决测井岩性识别问题,具有很好的应用前景.

用人工神经网络识别复杂γ能谱

用人工神经网络技术对在１４ＭｅＶ的快中子作用下爆炸物和日常有机物品产生的γ能谱进行识别，并对人工神经网络的基本原理与γ能谱识别的初步结果作了介绍．

基于人工神经网络的岩石含油气性评价方法

神经网络计算法是模拟人体经络系统活动的机理来研究事物的新方法。本文运用T .Kohonen提出的SOM自组织人工神经网络模型 ,以准噶尔盆地的彩南油田一主力油层为例 ,建立起岩石含油气性评价的人工神经网络模型。实例研究表明 ,人工神经网络法性能良好 。

基于小波分析与人工神经网络的水轮机压力脉动信号分析

针对水轮机尾水管压力脉动信号的非平稳和时变特性,提出了一种基于小波分析和自组织人工神经网络相结合的尾水管压力脉动信号的分析方法。这种方法首先应用小波阈值法对信号进行降噪减少干扰,然后将小波分解系数重构得到不同频带的信号分量,并提取显著的不同频带能量,最后将各频带能量作为特征向量,用自组织人工神经网络进行模式识别,得到了尾水管压力脉动的不同模式。应用该方法对某混流水轮机的压力脉动试验结果进行了分析,结果表明,该分析方法是有效的,能够对水轮机尾水管中的压力脉动状态进行有效的识别。

不同利用方式下土壤有机质和全磷的可见近红外高光谱反演

以太湖流域直湖港小流域稻田、桃园和菜地的土壤样本为研究对象,研究了不同光谱建模方法和土地利用方式对土壤有机质和全磷高光谱反演的影响。结果表明:(1)偏最小二乘回归分析(Partial least squarer egression,PLSR)模型的建模和预测精度较高且稳定;人工神经网络中广义回归神经网络(Generalized regression neural network,GRNN)网络预测精度较高但易出现过拟合现象,反向传播神经网络(Back propagation neural network,BPNN)网络比较稳健但精度略低;偏最小二乘与人工神经网络相结合则可综合两者优点,改善复杂样本下的预测精度。(2)土壤有机质的光谱反演结果优于全磷。3种土地利用方式中,稻田的预测效果总体优于桃园和菜地。在当前研究区域内土地利用方式对土壤有机质光谱反演影响不大,但对全磷反演影响较大。今后利用光谱对土壤全磷反演时需分土地利用方式对模型进行校准。

自组织映射用于数据分析的方法研究

自组织映射（SOM）是一种人工神经网络方法，它通过将相似的输入数据映射到相同或相近结点，将高维输入数据显示在低维空间中，具有保留输入数据的拓扑结构的优点，便于用户观察数据的分布特点。数据分析是SOM的重要应用领域之一。本文分析了SOM的各种学习算法（包括序列学习、批学习、k-batch学习、将空间访问方法引入SOM等算法）的优缺点和适用情况，挖掘了不同的SOM输出维度、结点形状及输出大小，并分析了三种成熟的SOM工具（SOM Toolbox，Viscovery SOMine和Databionic ESOM Tools）对这些算法、输出形式的支持程度，为SOM用于数据分析提供了细化而适当的方法借鉴。