基于一类GA—RBF神经网络的转炉炼钢静态模型控制

讨论了具有非线性、大时滞、不确定特性的工况复杂的转炉炼钢过程建模与控制问题．针对传统的控制方法控制效果差、精度不高，难以达到期望结果的问题，结合RBF神经网络的特点，提出用基于混合编码方式的混合遗传算法训练的RBF神经网络，同时优化网络的结构和参数，并利用RBF神经网络建立转炉炼钢静态模型．仿真结果表明，该模型具有在线调整和学习的功能，比传统模型具有更好的计算精度和适应能力，为提高转炉冶炼过程的控制精度给出了一个有效的方法．     

基于递归神经网络模型的传感器非线性动态补偿

讨论了递归神经网络模型在传感器非线性动态补偿中的应用，给出了递归神经网络模型的结构及相应的训练算法．递归神经网络模型本身具有动态映射能力，其结构仅与输入层和中间层的节点数有关，且不需要知道被补偿传感器的结构特性(如输出、输入的最大延迟)等先验知识，简化了动态补偿器的结构设计．采用递推预报误差算法训练神经网络，具有收敛速度快、收敛精度高的特点．实验结果表明，经过补偿后的传感器具有期望的输入输出特性，应用递归神经网络对传感器进行非线性动态补偿是一种行之有效的方法．     

基于BP神经网络的电解加工精度预测模型

为精确地预测电解加工精度,采用了BP神经网络的方法进行建模．在分析影响加工精度主要因素的基础上,确定了BP神经网络模型的特征参数,并根据实际情况,确定了输入层和中间隐层的维数,从而确定了模型的结构．用试验参数对模型结构进行训练,最终建立了一个用于电解加工精度预测的BP神经网络模型．利用该模型进行的精度预测结果表明,该模型的预测误差可以控制在10％以内,具有很高的精度预测能力．     

改进的神经网络观测器在非线性系统中的应用

为降低非线性观测器对模型精度的依赖性, 提出一种非传统的神经网络观测器设计方法。该神经网络为三层前馈网络, 采用带修正项的误差反传算法进行训练, 以保证控制的精度和权值有界, 利用神经网络识别系统的非线性部分, 并结合传统的龙伯格观测器重构系统状态; 利用Lyapunov 直接法保证基于权值误差的非观测器的稳定性, 并将该观测器应用于机器人轨迹跟踪控制中。仿真结果表明, 该方法解决了模型不确定系统状态观测问题, 适用于模型精度较低的非线性系统。     

线性化逐层优化MLP训练算法

提出了线性化逐层优化ＭＬＰ训练算法（ＬＯＬＬ）．ＬＯＬＬ采用循环方式逐层对ＭＬＰ的连接权值进行训练．训练连接权值时用一阶泰勒级数表示神经元的非线性激活函数以实现神经网络的线性化,使ＭＬＰ的训练问题转化为一个线性问题．同时,为保证神经网络线性化条件不被破坏,ＬＯＬＬ通过在神经网络的误差函数中计入部分线性化误差限制参数的改变幅度,对神经网络的误差函数进行了修正．实验结果显示,ＬＯＬＬ训练算法的速度比传统的ＢＰ算法快４倍,用它构成的语音信号非线性预测器有较好的预测性能．     

铁矿石烧结性能预报模型

研究了铁矿石烧结性能的评价指标及其主要影响因素, 提出了误差修正的带动量项的线性再励自适应变步长BP神经网络算法, 建立了铁矿石烧结性能预报模型. 模型预报结果表明, 用拓扑结构为12-34-4的BP神经网络训练6 700次后, 神经网络训练误差为0.000 187, 模型预报命中率均达83.5%以上, 模型具有很好的泛化能力和自适应能力.     

基于一类GA-RBF神经网络的转炉炼钢静态模型控制

讨论了具有非线性、大时滞、不确定特性的工况复杂的转炉炼钢过程建模与控制问题.针对传统的控制方法控制效果差、精度不高,难以达到期望结果的问题,结合RBF神经网络的特点,提出用基于混合编码方式的混合遗传算法训练的RBF神经网络,同时优化网络的结构和参数,并利用RBF神经网络建立转炉炼钢静态模型.仿真结果表明,该模型具有在线调整和学习的功能,比传统模型具有更好的计算精度和适应能力,为提高转炉冶炼过程的控制精度给出了一个有效的方法.     

采用MEA-AdaBoost-BP模型的工程结构可靠性分析方法

针对工程结构可靠性设计中算法和计算存在的问题,提出基于MEA-AdaBoost-BP神经网络算法模型的可靠性求解方法. 运用思维进化算法(MEA)求解训练集权值和阈值优化的BP神经网络,并构造为弱预测器函数.然后,运用AdaBoost算法将多个优化后的BP神经网络弱预测器函数迭代训练,形成MEA-AdaBoost-BP神经网络算法模型强预测器函数. 最后,利用逼近隐性功能函数求解可靠性指标,并将其与AdaBoost-BP算法和Monte-Carlo算法进行比较.研究结果表明:所提算法在计算中与Monte-Carlo算法相比,其迭代次数分别仅为16次和46次,效率高,计算精度与Monte-Carlo法接近;而和AdaBoost-BP法相比,其可靠性指标误差分别仅为1.59%和1.88%,计算结果更精确.     

推进学习对传神经网络ACPN

结合Adaboost算法的加权投票机制，提高对传神经网络算法CPN(Counterpropagation Networks)的学习效率，提出新型快速分类算法ACPN．实验证明，新算法的学习最小误差比传统CPN算法下降了96%，训练时间同比下降44%，网络训练阶段误差下降趋势明显稳定，不像传统CPN有一定的波动性．     

基于优化FOA-BPNN模型的脱贫时间预测

针对精准扶贫缺乏有效的分析模型对扶贫的成效与脱贫时间进行准确刻画与定量分析问题，提出了基于 FOA-BPNN 贫困户脱贫时间预测模型．针对BP神经网络模型可能陷入局部最小的缺陷，引入果蝇优化算法，以BP 神经网络的预测误差作为适应度值，寻找最优的BP神经网络参数值，提高参数精度．由于标准果蝇优化算法的搜 索半径固定，可能导致后期局部寻优性能弱，提出了一种动态步长变更策略的DSFOA-BPNN模型，通过引入变速因 子与种群密度，将动态步长FOA算法与传统误差反向传播神经网络(BPNN) 结合，提高模型预测时间精度．在湖北 省某贫困地区 50000 条扶贫数据的基础上，通过实验表明:与BPNN和FOA-BPNN模型相比， 提出的 DSFOA-BPNN 模型预测精度分别提高了44%和11%．增量实验表明: 提出的DSFOA-BPNN模型更适用于精度预测.     

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