利用MODIS反演武汉东湖叶绿素a浓度

以武汉东湖作为研究区域,对经过大气校正后MODIS影像的波段反射率与叶绿素a浓度实测值进行相关分析,分别应用BP人工神经网络模型和线性回归模型对武汉东湖的叶绿素a浓度进行了反演,并对两种反演方法的拟合和预测效果进行了比较.利用BP神经网络反演得到的拟合值与叶绿素a实测值的拟合效果略好于线性回归方法得到的结果,神经网络模型的可决系数R2值0.90大于线性回归模型的R2值0.820.神经网络模型预测的最小绝对误差为0.07 μg/L,线性回归模型的最小绝对误差为2.08 μg/L.最后分析了两个模型各自的优势,将模型应用到武汉东湖2008年5月19日的MODIS影像上反演出东湖水体叶绿素a浓度的分布情况,并对东湖水质进行了评价,结论与多年的地面监测结果一致.     

基于 R-RNN的孵化器运营水平评价及应用

针对科技企业孵化器运营水平难以科学定量评估的问题,提出了基于粗糙集和 RBF神经网络的 R RNN孵化器运营水平评价模型.基于孵化器运营工作原理的归纳分析,提出多层次孵化器运营水平评价指标体系.根据指标重要程度采用粗糙集理论对评价指标进行预处理,去除冗余指标项,选取重要控制指标并减少网络输入维度,进而采用 RBF神经网络对科技企业运营水平进行综合评价.最后通过具体的应用实例验证了该评价模型的有效性与可行性     

基于PCA-GA-BP的回采工作面瓦斯涌出量预测模型研究

为进一步提高回采工作面瓦斯涌出量预测的准确性,建立了主成分分析法(PCA)、遗传算法(GA)、BP神经网络相结合的预测模型。该模型采用主成分分析法降维处理原始输入数据;将主成分分析结果作为BP神经网络的输入,消除冗余信息;然后采用遗传算法优化BP神经网络的初始权值和阈值,有效克服BP神经网络极易陷入局部最优的问题。选取某矿井回采工作面的实测数据进行分析,结果表明,该模型较单一BP神经网络预测精度高,能更有效地实现回采工作面瓦斯涌出量的高准确度预测。     

基于BP神经网络的内部控制预警体系构建

定量的内部控制预测方法能够科学精确的判别企业内部控制状况,不受评价人员主观判断的影响。基于此,利用危机预警中广泛运用的BP神经网络方法建立内部控制评价的量化模型,通过构建内部控制评价指标,把描述企业内部控制的特征信息作为神经网络的输入向量,把内部控制状况量化为输出,并使用足够的样本来训练这个网络,最终构建了内部控制预警体系。通过与距离判别法的比较发现,BP神经网络在内部控制预警方面具有较好的准确性。     

基于BP神经网络方法研究岩石节理形貌粗糙度系数

岩石节理粗糙度系数(JRC)是研究岩石力学性质的重要参数。为了更准确地描述这一参数,本文基于人工神经网络的原理,提出一种研究JRC的新方法——BP神经网络预测法。选取节理表面最大峰高S_p、表面最大高度S_z、表面最大谷深S_v、峰度系数S_(ku)、偏斜度系数S_(sk)、均方根高度S_q、算术平均高度S_a7个表面形貌高度特征参数作为网络输入,剖面线分维值和JRC作为网络输出,以此为基础构建网络模型,并对10组实测数据进行了预测验证。结果表明:该方法误差很小,具有很高的预测精度,可为进一步的研究提供新的思路和方法。     

基于Bagging神经网络集成的燃料电池性能预测方法

利用三维质子交换膜燃料电池数学模型模拟研究了电池流道进、出口高度对电池性能的影响,然后将数值模拟结果作为神经网络模型的训练数据.以流道进、出口高度和电池电压值作为输入变量,以电池电流密度作为输出变量,建立了3层反向传播神经网络模型;然后利用Bagging集成学习方法对神经网络模型进行集成,构建了燃料电池性能预测方法.研究发现:与单一神经网络模型相比, Bagging神经网络集成模型预测精度更高,且所需模型训练数据量更少.此外对于超出训练数据以外的情形, Bagging神经网络集成模型仍然能够准确地预测燃料电池的性能,且精度良好,表明Bagging神经网络集成模型的鲁棒性较好,可用于更宽工况范围内燃料电池性能的快速预测.     

基于神经网络模型的紫外极光卵边界建模

极光卵的尺度大小和太阳风、磁层、电离层以及它们间的耦合过程有密切的联系,会随着空间和地磁环境的变化而变化.建立准确的极光卵边界预测模型对空间天气的预报以及了解日地关系具有重要意义.本文利用误差反向传播(back propagation, BP)神经网络和广义回归神经网络(general regression neural network, GRNN)两种神经网络模型对极光卵边界进行建模.结果显示GRNN的极光卵边界模型具有较高的准确性,赤道向边界预测平均绝对误差在0.77～1.20磁纬度(MLAT);极向边界预测平均绝对误差在0.83～1.39 MLAT.基于GRNN的极光卵边界模型预测准确性分别在极向边界和赤道向边界的整个磁地方时(MLT)上比BP神经网络的极光卵边界模型平均提高了0.74和0.73 MLAT,比多元线性回归模型平均提高了0.82和0.82 MLAT.而在模型的外推性方面, GRNN的极光卵边界模型的外推性优于BP神经网络的极光卵边界模型,与多元线性回归模型接近.     

基于PCA和BP神经网络的采空区稳定性评价模型研究

针对采空区稳定性评价因素的复杂性和相关性特点,提出主成分分析(PCA)与BP神经网络相结合的采空区稳定性综合评价方法。经综合分析确定以工程地质因素、采空区赋存结构参数、采动因素3个一级影响因素为基础的评价指标体系,以此为基础构建了采空区稳定性评价的BP神经网络评价模型。以某大型铅锌矿山地下采空区为例,应用CMS探测系统获取采空区相关数据生成采空区3D实体模型,并根据BP神经网络训练出的计算模型对采空区稳定性的等级进行评价。研究结果表明:PCA和BP神经网络相结合的方法使输入变量由13个减少为5个,避免了由于变量相关性带来的影响,简化了评价过程,结果更加合理。现场探测结果与BP神经网络计算结果相互支持。     

基于GB-CART集成算法的铁路物流需求量预测

为了准确、快速地对铁路物流需求量进行预测,针对现有铁路物流需求量预测模型存在的问题,采用梯度提升算法对分类与回归树算法进行集成,提出一种GB-CART集成算法。以1990~2014年的铁路物流需求量为研究对象,选取预测年份前3年的铁路物流需求量作为模型输入,预测年份铁路物流需求量作为模型输出,采用GBCART集成算法进行仿真实验,并与单一CART、SVR、RBF和LR模型进行比较。结果表明:GB-CART模型的预测效果与单一CART模型相比得到了大幅度提升,且预测精度高于SVR、RBF和LR,验证了所提出模型的有效性及准确性。     

基于广义线性模型的危险货物专用线风险评价

为科学合理地评价铁路危险货物专用线安全状况,选取了设备设施、安全设施、安全管理和人员管理4个评价指标,以北京铁路局内的10条专用线为例,将其分为样本集和验证集,根据所对应的安全检验报告对指标进行量化,建立基于广义线性模型的专用线风险等级概率分布回归模型,得到专用线风险等级概率分布情况,并以发生概率最大的作为最终风险评定等级。如此依概率评价专用线风险情况,更能体现风险事件的随机性。     

日径流预报贝叶斯回声状态网络方法

回声状态网络（ESN）相比传统递归神经网络，具有模型简单、参数训练速度快的特点．针对标准ESN因常采用线性回归率定模型参数容易出现过拟合问题，提出了基于贝叶斯回声状态网络（BESN）的日径流预报模型．该模型将贝叶斯理论与ESN模型相结合，通过权重后验概率密度最大化而获得最优输出权重，提高了模型的泛化能力．通过安砂和新丰江两座水库日径流预测实例表明，BESN模型是一种有效、可行的预测方法，与传统BP神经网络和ESN模型对比，进一步表明BESN模型具有更好的预测精度．     

Neural model identification,variable selection and model adequacy

In recent years an impressive array of publications has appeared claiming considerable successes of neural networks in modelling financial data but sceptical practitioners and statisticians are still raising the question of whether neural networks really are ‘a major breakthrough or just a passing fad’. A major reason for this is the lack of procedures for performing tests for misspecified models, and tests of statistical significance for the various parameters that have been estimated, which makes it difficult to assess the model's significance and the possibility that any short‐term successes that are reported might be due to ‘data mining’. In this paper we describe a methodology for neural model identification which facilitates hypothesis testing at two levels: model adequacy and variable significance. The methodology includes a model selection procedure to produce consistent estimators, a variable selection procedure based on statistical significance and a model adequacy procedure based on residuals analysis. We propose a novel, computationally efficient scheme for estimating sampling variability of arbitrarily complex statistics for neural models and apply it to variable selection. The approach is based on sampling from the asymptotic distribution of the neural model's parameters (‘parametric sampling’). Controlled simulations are used for the analysis and evaluation of our model identification methodology. A case study in tactical asset allocation is used to demonstrate how the methodology can be applied to real‐life problems in a way analogous to stepwise forward regression analysis. Neural models are contrasted to multiple linear regression. The results indicate the presence of non‐linear relationships in modelling the equity premium. Copyright © 1999 John Wiley & Sons, Ltd.     

基于SVM的旋转机械转子故障预示研究

支持向量机包括支持向量回归机和支持向量分类机.本文提出了一种用于旋转机械转子故障预示的方法,通过支持向量分类机（SVC）对旋转机械转子故障进行分类并建立故障分类器,利用支持向量回归机（SVR）对转子运行状态趋势进行预示,并将预示结果输入到SVC以判断预示结果的属性.对支持向量回归机进行了仿真研究.将支持向量机与神经网络算法从理论和实验研究两个方面进行了对比研究,结果表明,该方法具有较好的故障预示能力.     

Forecasting Core Business Transformation Risk Using the Optimal Rough Set and the Neural Network

In the process of enterprise growth, core business transformation is an eternal theme. Enterprise risk forecasting is always an important concern for stakeholders. Considering the completeness and accuracy of the information in the early‐warning index, this paper presents a new risk‐forecasting method for enterprises to use for core business transformation by using rough set theory and an artificial neural network. First, continuous attribute values are discretized using the fuzzy clustering algorithm based on the maximum discernibility value function and information entropy. Afterwards, the major attributes are reduced by the rough sets. The core business transformation risk rank judgement is extracted to define the connection between network nodes and determine the structure of the neural networks. Finally, the improved back‐propagation (BP) neural network learning and training are used to judge the risk level of the test samples. The experiments are based on 265 listed companies in China, and the results show that the proposed risk‐forecasting model based on rough sets and the neural network provides higher prediction accuracy rates than do other widely developed baselines including logistic regression, neural networks and association rules mining. Copyright © 2015 John Wiley & Sons, Ltd.     

基于BP算法的纹理图象合成研究

针对目前纹理图象合成技术存在的弱点，应用人工神经网络BP算法实现了纹理图象的合成。通过对纹理图象的分析和特征提取来获取图象的各种参数信息，然后采用人工神经网络BP算法，建立BP网络模型，将原始图象的特征函数作为BP网络的输入，通过学习-训练，确定该图象的人工神经网络结构，并最终通过程序合成了纹理图象。     

利用AR模型参数和BP神经网络辨识微震信号

利用AR模型参数和BP神经网络,针对矿山微震信号具有频带较宽、谱成分丰富的特性,提出了时不同频率范围的信号和噪声进行滤波处理的方法.利用该方法可将噪声与信号分离以及将不同频段信号分解,从而达到滤波的目的.实验结果表明,利用AR模型参数和BP神经网络能够有效去除微震异常信号的噪声,可应用于微震信号的预处理和微震预测.     

Economic factors and the stock market: a new perspective

It has been widely accepted that many financial and economic variables are non‐linear, and neural networks can model flexible linear or non‐linear relationships among variables. The present paper deals with an important issue: Can the many studies in the finance literature evidencing predictability of stock returns by means of linear regression be improved by a neural network? We show that the predictive accuracy can be improved by a neural network, and the results largely hold out‐of‐sample. Both the neural network and linear forecasts show significant market timing ability. While the switching portfolio based on the linear forecasts outperforms the buy‐and‐hold market portfolio under all three transaction cost scenarios, the switching portfolio based on the neural network forecasts beats the market only if there is no transaction cost. Copyright © 1999 John Wiley & Sons, Ltd.     

An intelligent model selection and forecasting system

In this paper we present an intelligent decision‐support system based on neural network technology for model selection and forecasting. While most of the literature on the application of neural networks in forecasting addresses the use of neural network technology as an alternative forecasting tool, limited research has focused on its use for selection of forecasting methods based on time‐series characteristics. In this research, a neural network‐based decision support system is presented as a method for forecast model selection. The neural network approach provides a framework for directly incorporating time‐series characteristics into the model‐selection phase. Using a neural network, a forecasting group is initially selected for a given data set, based on a set of time‐series characteristics. Then, using an additional neural network, a specific forecasting method is selected from a pool of three candidate methods. The results of training and testing of the networks are presented along with conclusions. Copyright © 1999 John Wiley & Sons, Ltd.     

Mortality forecasting using neural networks and an application to cause‐specific data for insurance purposes

Mortality forecasting is important for life insurance policies, as well as in other areas. Current techniques for forecasting mortality in the USA involve the use of the Lee–Carter model, which is primarily used without regard to cause. A method for forecasting morality is proposed which involves the use of neural networks. A comparative analysis is done between the Lee–Carter model, linear trend and the proposed method. The results confirm that the use of neural networks performs better than the Lee–Carter and linear trend model within 5% error. Furthermore, mortality rates and life expectancy were formulated for individuals with a specific cause based on prevalence data. The rates are broken down further into respective stages (cancer) based on the individual's diagnosis. Therefore, this approach allows life expectancy to be calculated based on an individual's state of health. Copyright © 2008 John Wiley & Sons, Ltd.