基于聚类及优化集成神经网络的地铁车站空调负荷预测

分别从优化算法集成神经网络及将数据聚类后按类建模两方面建立3种模型对地铁车站空调负荷进行逐时预测,结果表明：同一物理量对地铁车站空调负荷所产生的影响程度随时间呈现某种动态变化特征,根据历史数据定量分析这些特征,对精准筛选模型输入参数、提高模型预测精度大有裨益。在3种模型中,粒子群优化算法-神经网络（PSO-BPNN）和果蝇优化算法-神经网络（FOA-BPNN）预测的平均相对误差（MAPE）较单纯神经网络（BPNN）分别降低25.87%和40.08%,聚类-神经网络（Kmeans-BPNN）预测的MAPE比PSO-BPNN及FOA-BPNN分别降低61.12%和51.90%。说明在同等情况下,优化算法集成模型比单纯BPNN预测精度更高,而当区分实际负荷变化特点后,采用聚类后建模比优化集成建模效果更佳。     

基于改进果蝇的混合小波神经网络交通流预测

为了提高交通流预测精度,提出了一种基于果蝇算法的混合小波神经网络模型.首先,选择果蝇优化算法对小波神经网络的初始参数进行调节,解决了小波神经网络预测对初始参数敏感的问题.其次,将迭代次数和当前解的情况作为搜索半径和种群规模的动态调整因子,对果蝇算法进行了改进,提高了果蝇算法的全局寻优能力和局部收敛速度.最后,鉴于小波神经网络预测误差存在一定的规律性,使用误差补偿法将调参后的小波神经网络与其他模型进行组合,进行二次误差提取.实验证明,所有混合模型均提高了交通流预测的准确度,其中,与随机森林模型的结合预测精度最高.     

基于优化BP神经网络算法的网络质量评价

针对现有的BP神经网络算法,提出了在变步长BP神经网络算法基础上的优化方案,并将其应用于网络质量评价当中.在优化方案中,对步长的上升和下降阶段分别采用不同策略进行优化.理论分析表明:优化后的算法能够克服传统算法权值收敛过慢,和变步长算法误差收敛中的震荡问题.仿真表明,优化后的算法会使神经网络的学习误差和网络质量分类的总体误差明显下降并大幅提高评价的准确性.优化算法较传统算法相比误差收敛过程更加稳定,且学习误差下降达9.64％,网络质量分类的总体误差下降达23.1％；优化算法的验证准确率在传统算法的基础上提高了19.65％,在变步长算法的基础上提高了9.88％.由此可见,优化算法在BP神经网络的预测精度方面起到了大幅度提高的作用.     

基于改进BAS-BPNN的Stewart平台位姿正解

针对目前Stewart平台位姿正解难以兼顾高精度和实时性的问题，设计出满足平台精度要求且运算时间尽量少的正解方法是研究并联平台运动学的关键。首先建立平台运动学反解模型求得BP神经网络（BPNN）的训练集，然后调整步长策略和引入惯性权重改进天牛须搜索算法（improved BAS，IBAS），提升算法性能，通过改进后的算法优化BP神经网络的初始权阈值，反复学习训练至最小误差，最后选取一段位姿变化进行仿真验证。仿真结果表明，IBAS-BPNN正解法在选取的100组位姿点中的最大位姿误差小于0.6%，运算时间为31.9 s，通过与遗传算法(GA)、粒子群算法(PSO)和天牛须搜索算法(BAS)优化的BP神经网络模型进行对比分析，验证了IBAS-BPNN模型在精度和运算速度方面具有优越性。算法模型有效提升了Stewart平台位姿正解的精度和实时性，为平台的运动学和控制提供了理论和方法参考。     

基于改进粒子群优化-反向传播神经网络算法的小麦储藏品质预测模型

在使用反向传播神经网络(back propagation neural network,BPNN)预测小麦的储藏品质时,由于其易陷入局部极值且收敛速度慢,导致预测误差较大且稳定性较差,由此提出一种改进粒子群(improved particle swarm optimization,IPSO)算法优化的BPNN预测模型.采用非线性函数动态调整粒子群算法中的惯性权重和学习因子,优化BPNN中的权值参数,进而构建IPSO-BPNN预测模型.为验证该模型的准确性和稳定性,将其与BPNN模型、PSO-BPNN模型进行对比,结果表明:IPSO-BPNN模型预测的均方误差显著降低,有助于提高小麦储藏品质预测的准确性和可靠性.     

基于混沌天牛群算法优化的神经网络分类模型

针对传统BP神经网络受初始权阈值影响大且易陷入局部极值,标准天牛须搜索算法局部搜索能力差、寻优精度低等问题,提出一种自适应步长因子的混沌天牛群算法用于优化BP神经网络分类模型。通过增加天牛种群,引入自适应步长更新策略优化天牛须搜索算法的局部搜索能力,使其跳出局部最优,提高算法的计算精度;利用Logisitic混沌映射产生新个体,替换性能较差的个体,增强全局搜索效果。为了改善BP神经网络对非均衡数据集中少数类的分类效果,采用SMOTE算法处理非均衡数据集。将改进的天牛须搜索算法用于优化BP神经网络中的初始权值和阈值,建立IBAS-BPNN（Improved Beetle Antennae Search and Back Propagation Neural Network）分类模型,提高BP神经网络分类模型的准确率。为验证分类模型的性能,将改进的BP神经网络分类模型与其他六种典型的分类算法进行比较,实验结果表明IBAS-BPNN分类模型的平均分类正确率高于其他算法。改进的混沌天牛群算法泛化能力强,鲁棒性好,具有一定的优越性。     

粒子群算法优化BP神经网络

现实生活中绝大数系统都是非线性的,BP神经网络通过训练能否达到局部最优值、能否收敛以及训练的时间长短与初始值和阈值的选取关系密切.为此采用了具有动态惯性权重的粒子群算法对BP神经网络初始值进行优化.实验表明具有动态惯性权重的粒子群算法优化BP神经网络预测误差很小,能够跳出局部极小值,得到更优的结果.     

基于IDNPSO-BP神经网络的股票市场指数预测

针对动态邻居粒子群算法的局限性,引入新的动态邻居拓扑结构,动态调整粒子群算法参数设置,提出改进的动态邻居粒子群算法(IDNPSO).为了提高BP神经网络模型的预测准确性,提出一种基于改进动态邻居粒子群算法的BP神经网络模型(IDNPSO-BP神经网络).利用IDNPSO-BP神经网络和GA-BP神经网络对上证指数、深证指数进行预测,结果表明IDNPSO-BP神经网络的预测误差优于GA-BP神经网络,具有股票市场指数预测能力.     

基于IPSO-BP神经网络的坝基扬压力预测方法研究

针对坝基扬压力预测的传统BP神经网络模型初始权值和阈值随机性强、易陷入局部最优等局限,采用惯性权重动态调整的改进粒子群算法对BP网络的初始权值和阈值进行优化,建立了基于IPSO的BP神经网络坝基扬压力预测模型．通过算例验证算法的优越性及程序的准确性,并以某大坝多年扬压力监测数据进行工程实例应用,结果表明,IPSO—BP扬压力预测模型与传统BP模型相比,拟合相关系数大,统计误差小,预测精度更高．     

改进粒子群算法在BP神经网络拟合非线性函数方面的应用

为解决BP神经网络拟合非线性函数的预测结果误差较大问题,笔者将标准粒子群算法进行改进,形成基于免疫接种的粒子群算法(IPSO);然后将该算法与BP神经网络理论相结合,实现基于IPSO算法优化的BP神经网络非线性函数拟合算法。新的拟合算法首先确定BP神经网络结构,然后用IPSO算法优化初始权值和阈值,最后进行BP神经网络预测。数值实验表明,本文提出的IPSO算法提高了BP神经网络的拟合能力,减小了拟合误差,提高了拟合精度。     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

On Tragic Consciousness In The Works By Ernest Hemingway

As an American modern novelist who were famous in the literary world, Hemingway was not a person who always followed the trend but a sharp observer. At the same time, he was a tragedy maestro, he paid great attention on existence, fate and end-result. The dramatis personae's tragedy of his works was an extreme limit by all means tragedy on the meaning of fearless challenge that failed. The beauty of tragedy was not produced on the destruction of life, but now this kind of value was in the impact activity. They performed for the reader about the tragedy on challenging for the limit and the death.     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to