共查询到20条相似文献，搜索用时 31 毫秒
1.
Recognizing spatial distribution patterns of grassland insects: neural network approaches 总被引：2，自引：1，他引：1
WenJun Zhang XiaoQing Zhong GuangHua Liu 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(2):207-216
The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various
neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland
insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant
analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons
and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing
spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns.
BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network
became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern
recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the
general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network.
It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion
for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological
scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding
on spatial distribution patterns. 相似文献
2.
高层建筑基础抗震选型评价指标与智能优选方法 总被引：5，自引：0，他引：5
文章规划了能全面表征高层建筑基础及其相关全系统、全性能、全寿命过程的各种影响因素、约束与要求等的基础抗震选型性能评价指标体系模型，为通过选型对基础的性能进行预测、评价、控制与优化等提供了依据。建立了带置信与变权因子的选型方法并给出了应用实例，该方法全面考虑了表征领域专家及决策者经验、知识、偏好及决策因素变化影响等方面的重要信息，使决策结果所包容的信息更丰富，完备和有效。给出了基于模糊评判、模糊辨识、模糊推理、BP神经网络、模糊神经网络技术的高层建筑基础智能优选算法和网络结构，为建立集成化、智能化、自动化的选型支持系统及提高基础选型质量与水平等奠定了基础。 相似文献
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A systematic comparison of two basic types of neural network, static and dynamic, is presented in this study. Two back-propagation (BP) learning optimization algorithms, the standard BP and conjugate gradient (CG) method, are used for the static network, and the real-time recurrent learning (RTRL) algorithm is used for the dynamic-feedback network. Twenty-three storm-events, about 1632 rainfall and runoff data sets, of the Lan-Yang River in Taiwan are used to demonstrate the efficiency and practicability of the neural networks for one hour ahead streamflow forecasting. In a comparison of searching algorithms for a static network, the results show that the CG method is superior to the standard BP method in terms of the efficiency and effectiveness of the constructed network's performance. For a comparison of the static neural network using the CG algorithm with the dynamic neural network using RTRL, the results show that (1) the static-feedforward neural network could produce satisfactory results only when there is a sufficient and adequate training data set, (2) the dynamic neural network generally could produce better and more stable flow forecasting than the static network, and (3) the RTRL algorithm helps to continually update the dynamic network for learning—this feature is especially important for the extraordinary time-varying characteristics of rainfall–runoff processes. 相似文献
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为了解决煤储层物性的预测方法问题,本文基于大量的文献调研,梳理了煤储层孔隙性和渗透性的影响因素和预测方法,并进行了预测技术展望.研究表明,孔隙性影响因素主要有煤层埋深、压实作用、变质程度和显微组分等,孔隙度预测方法主要有双侧向迭代法、阿尔奇公式裂缝孔隙度估算法、双侧向数值模拟法、相关分析方法及支持向量机等方法;渗透性影响因素主要有煤层埋深、储层压力、煤的变质程度、煤体结构、煤岩组分、应力状态、基质收缩作用和裂隙系统发育程度等,渗透性预测方法主要有F-S计算方法、基于达西定律的计算方法、相关分析法及多层次模糊综合评判法等其他方法.本文认为遵循“地质约束测井、岩心刻度测井”的原则,加强煤层气储层岩石物理研究和物性影响因素分析是基础;常规测井信息与测井新技术信息结合,“多尺度信息融合”建立煤岩孔隙度和渗透率解释新模型,充分发挥多种非线性数学方法的优势构建煤岩物性非线性数学预测方法有一定的实际意义. 相似文献
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最优化测井解释方法能充分利用各种测井资料及地质信息,可以有效地评价复杂岩性油气藏.优化算法的选择是最优化测井解释方法的关键,影响着测井解释结果的准确性.细菌觅食算法(BFA)是新兴的一种智能优化算法,具有较强的全局搜索能力,但在寻优后期收敛速度较慢.复合形算法(CM)局部搜索能力极强,将其与BFA算法相结合构成BFA-CM混合算法,既提高了搜索精度又提高了搜索效率.利用BFA-CM最优化测井解释方法对苏里格致密砂岩储层实际资料进行了处理,计算结果与岩心及薄片分析资料吻合得很好. 相似文献
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快速、准确地识别天然地震和人工爆破事件是地震台网监测的重要工作之一,也是提高地震观测记录质量、开展地震研究工作的重要基础。针对反向传播神经网络、支持向量机等主流分类识别方法在地震事件分类识别应用上的不足,提出一种基于改进EWT和LogitBoost集成分类器的地震事件分类识别算法。首先,基于S谱能量曲线对传统经验小波变换进行改进,将信号自适应分解为按频率和能量分布的本征模函数;其次,提取P波与S波最大振幅比,前4个本征模函数的香农熵、对数能量熵,以及去噪后重构信号主频等特征;最后,采用基于集成学习LogitBoost的决策树集成分类器进行分类。实验结果表明,所提算法具有较高的鲁棒性,能有效解决样本不足的问题,识别准确率达93.1%以上,比集成学习AdaBoost、反向传播神经网络和支持向量机等方法提高了1%以上,且分类识别效果好。 相似文献
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为满足地球物理资料反演解释的高精度、快速、稳定的要求,本文结合免疫遗传算法寻优速度快和BP神经网络反演不依赖初始模型等优点,设计了一种将BP神经网络和免疫遗传算法进行有机结合的全局优化反演策略,并将该策略成功地应用于二维高密度电法数据反演.利用免疫遗传算法(Immune Genetic Algorithm,简称IGA)对神经网络的反演参数进行同步优化,提高了电阻率反演的精度.仿真和实验结果验证设计的全局优化反演策略取得了较好的效果,通过与线性反演方法和BP法以及遗传神经网络法等反演方法进行比较,得出该方法具有反演精度更高,反演时间更短等显著优势的结论. 相似文献
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以煤层瓦斯富集地质理论为基础，根据煤层瓦斯与常规砂岩储层天然气赋存机理的对比，提出了以煤层割理裂隙为探测目标的煤层瓦斯富集AVO技术预测理论. 根据AVO理论模拟，煤层顶面反射振幅通常是随着炮检距的增大而减小；不同结构煤体在AVO响应上存在明显的差异，随着煤层割理裂隙发育程度的增强，煤层顶面AVO的截距和梯度都会增大；煤层厚度的调谐作用对其AVO特征有明显的影响. 通过对淮南煤田实际地震资料的处理和分析，探讨了煤田地震资料AVO的处理和解释方法，认为AVO梯度和伪泊松比反射系数是对煤层割理裂隙发育程度最为敏感的属性，获得了研究区内有工程使用价值的瓦斯突出区预测成果，初步证实了应用AVO技术检测煤层割理裂隙、预测煤层瓦斯富集部位的可行性. 相似文献
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Hydrological and statistical models are playing an increasing role in hydrological forecasting, particularly for river basins with data of different temporal scales. In this study, statistical models, e.g. artificial neural networks, adaptive network-based fuzzy inference system, genetic programming, least squares support vector machine, multiple linear regression, were developed, based on parametric optimization methods such as particle swarm optimization (PSO), genetic algorithm (GA), and data-preprocessing techniques such as wavelet decomposition (WD) for river flow modelling using daily streamflow data from four hydrological stations for a period of 1954–2009. These models were used for 1-, 3- and 5-day streamflow forecasting and the better model was used for uncertainty evaluation using bootstrap resampling method. Meanwhile, a simple conceptual hydrological model GR4J was used to evaluate parametric uncertainty based on generalized likelihood uncertainty estimation method. Results indicated that: (1) GA and PSO did not help improve the forecast performance of the model. However, the hybrid model with WD significantly improved the forecast performance; (2) the hybrid model with WD as a data preprocessing procedure can clarify hydrological effects of water reservoirs and can capture peak high/low flow changes; (3) Forecast accuracy of data-driven models is significantly influenced by the availability of streamflow data. More human interferences from the upper to the lower East River basin can help to introduce greater uncertainty in streamflow forecasts; (4) The structure of GR4J may introduce larger parametric uncertainty at the Longchuan station than at the Boluo station in the East river basin. This study provides a theoretical background for data-driven model-based streamflow forecasting and a comprehensive view about data and parametric uncertainty in data-scarce river basins. 相似文献
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Linqi Zhu Chong Zhang Chaomo Zhang Xueqing Zhou Jun Wang Xiu Wang 《Acta Geophysica》2018,66(5):983-1000
The total organic carbon (TOC) content reflects the abundance of organic matter in marine mud shale reservoirs and reveals the hydrocarbon potential of the reservoir. Traditional TOC calculation methods based on statistical and machine learning have limited effect in improving the computational accuracy of marine mud shale reservoirs. In this study, the collinearity between log curves of marine mud shale reservoirs was revealed for the first time, which was found to be adverse to the improvement of TOC calculation accuracy. To this end, a new TOC prediction method was proposed based on Multiboost-Kernel extreme learning machine (Multiboost-KELM) bridging geostatistics and machine learning technique. The proposed method not only has good data mining ability, generalization ability and sound adaptivity to small samples, but also has the ability to improve the computational accuracy by reducing the effect of collinearity between logging curves. In prediction of two mud shale reservoirs of Sichuan basin with proposed model, the results showed that the predicted value of TOC was in good consistence with the measured value. The root-mean-square error of TOC predicting results was reduced from 0.415 (back-propagation neural networks) to 0.203 and 1.117 (back-propagation neural networks) to 0.357, respectively; the relative error value decreased by up to 8.9%. The Multiboost-KELM algorithm proposed in this paper can effectively improve the prediction accuracy of TOC in marine mud shale reservoir. 相似文献
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Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models 总被引：4，自引：0，他引：4
Pumping optimization of coastal aquifers involves complex numerical models. In problems with many decision variables, the computational burden for reaching the optimal solution can be excessive. Artificial Neural Networks (ANN) are flexible function approximators and have been used as surrogate models of complex numerical models in groundwater optimization. However, this approach is not practical in cases where the number of decision variables is large, because the required neural network structure can be very complex and difficult to train. The present study develops an optimization method based on modular neural networks, in which several small subnetwork modules, trained using a fast adaptive procedure, cooperate to solve a complex pumping optimization problem with many decision variables. The method utilizes the fact that salinity distribution in the aquifer, depends more on pumping from nearby wells rather than from distant ones. Each subnetwork predicts salinity in only one monitoring well, and is controlled by relatively few pumping wells falling within certain control distance from the monitoring well. While the initial control area is radial, its shape is adaptively improved using a Hermite interpolation procedure. The modular neural subnetworks are trained adaptively during optimization, and it is possible to retrain only the ones not performing well. As optimization progresses, the subnetworks are adapted to maximize performance near the current search space of the optimization algorithm. The modular neural subnetwork models are combined with an efficient optimization algorithm and are applied to a real coastal aquifer in the Greek island of Santorini. The numerical code SEAWAT was selected for solving the partial differential equations of flow and density dependent transport. The decision variables correspond to pumping rates from 34 wells. The modular subnetwork implementation resulted in significant reduction in CPU time and identified an even better solution than the original numerical model. 相似文献
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人工神经网络原理在建筑物震陷预测中的应用 总被引：1，自引：0，他引：1
运用人工神经网络原理，对BP型神经网络作了多方面的改进，采用改进后的BP算法，建立了建筑物震陷预测模型，研究结果表明，改进的BP网络性能良好，所建立的模型预测精度高，能满足工程要求，是一种有效可行的预测新方法。 相似文献
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石炭系火山岩的低渗透气藏具有的埋藏深、成因复杂、类型多、分布广、三孔隙度曲线响应特征不明显等特点,并且主要的四种火山岩气藏：玄武岩、安山岩、英安岩、流纹岩的密度和纵波时差测井值差别较大,因此使基于三孔隙度测井资料的一系列识别气层的有效方法在该盆地石炭系火山岩气层的识别中无明显效果.数据挖掘技术从数据的角度出发,在进一步丰富岩心、测井和试油资料的前提下,利用数据挖掘技术中的聚类分析和关联分析获取核心参数和数据之间的内在联系,用决策树提取预测火山岩气层的模型.该方法充分利用已有的数据资料,用数学分析方法遍历寻找对识别火山岩气层有用的信息,而不仅仅依靠三孔隙度和电阻率曲线,并且消除了岩性的影响,因此获得了较高的识别率. 相似文献
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针对网格结构中杆件数量众多,但节点数总是远远小于杆件数的特点,损伤识别中采用了基于BP神经网络技术和面向节点的损伤初步定位方法的网格结构损伤识别的三步法。对双层柱面网壳结构模型在不同杆件去掉时的四种损伤情况下的振动特性进行了实测,并以实测低阶模态的频率变化率和少数测点的振型分量作为神经网络输入参数,对模型的各种损伤情况进行了识别。结果表明,所用的方法可以精简神经网络的结构,并提高其模式识别的能力。该方法可用于对大型复杂结构的损伤识别。 相似文献
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本文从瞬变电磁均匀半空间二次磁场响应公式出发,提出了一种基于神经网络的视电阻率快速计算方法.以中心回线为例,根据瞬变响应公式的特点,简化网络结构,选用三层BP神经网络和误差训练算法,用均匀半空间样本数据进行训练,确定了收敛快、误差小的一步正割法和隐含单元数,得到基于不同采样时窗的一组网络参数.用本文方法与二分法、牛顿迭代法做模型计算比较,及最后的实验计算,说明算法的快速,准确.本文方法不依赖初始模型,避开了复杂的电磁场数值计算,实现了视电阻率的快速计算,对瞬变电磁法资料的快速解释有一定的参考价值. 相似文献
18.
Prediction of effective porosity from seismic attributes using locally linear model tree algorithm 
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下载免费PDF全文 Ahmad Ashtari Talkhestani 《Geophysical Prospecting》2015,63(3):680-693
In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir. 相似文献
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本文试图解释用ＢＰ神经网络解界面反问题时效果不佳的原因。文中首先从信息量的角度提出了ＢＰ神经网络训练本集容量的概念，给出了它的定义及组织训练样本集时应遵循的原则和方法。对于如何用ＢＰ神经网络解界面反问题，给出了其基本步骤，并根据上述训练样本集容量的概念及界面反总理的特殊性，给出了组织界面反问题训练样本集的方法。 相似文献
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根据不同流体性质在角度道集上所反映特征的差异,构建了多属性角度叠加数据体组合流体识别因子.并将量子粒子群与模糊神经网络相结合,利用量子粒子群方法来优化模糊神经网络中的连接权值和隶属函数参数,并进行一系列的改进措施,显著提高了算法的全局寻优能力.将近远角度叠加数据体组合流体识别因子作为改进模糊神经网络的输入,流体性质作为输出,同时引入“相控流体识别”的思想,利用碳酸盐岩储集相进行控制,建立了碳酸盐岩流体识别模型.通过塔中实际井区进行验证,证明该方法能够提高流体的识别精度,具有很好的实际应用价值. 相似文献