储层敏感性快速预测软件在大港油田的研究与应用

在储层特征分析，室内实验，机理研究的基础上，利用人工神经BP网络建立预测模型，并编制了预测储层5种敏感性的软件，该软件受人为因素干扰小，所需参数少，准确度高，综合符合率大于80％现场应用结果表明，该技术能为制定保护油气层技术措施提供较可靠的依据，能提高油田滚动开发效益，改善老油田的开发效果，该软件对BP算法进行了改进，（1）从两方面入手使网络摆脱平坦区，一是对输入数据进行归一化处理，使O1的取值在[0,1]之间，二是一旦网络陷入平坦区域，局部极小，使连接权值Wkj,Wji和阈值Qk,Qj同时缩小一个因子，λ＞1，可使Ok(1-Ok)脱离零值，离开平坦区；（2）加速收敛，方法有自动调整学习因子，添加动量项以及对权值进行批处理，用BP算法预测储层潜在敏感性，首先应确定影响储层敏感性的主要因素，然后根据这些因素有针对性地收集有关资料并进行处理，再根据敏感性预测的要求，设计相应的网络结构进行训练，最后对训练好的网络进行.检验。     

RBF网络及其在卫星姿态控制中的应用

提出了一种基于RBF网络扰动力矩补偿的控制技术，通过在传统PID控制的基础上，引入RBF网络补偿环节，进一步抑制了扰动力矩的影响，提高了卫星姿态控制精度，首先给出了RBF网络的学习算法，然后根据全补偿原则设计了神经网络补偿姿态控制方案，最后结合某型卫星的姿态控制系统设计实例，进行了数学仿真，结果表明：有RBF网络补偿的系统，姿态的稳态精度得到了明显的改善。     

基于一类T-模的模糊Hopfield网络的稳定性分析

本文建立了基于一类T-模的一种动态模糊神经网络-(∨,T)Hopfield网.文章首先证明了系统本身的稳定性及系统平衡态的Lyapunov稳定性,然后建立了吸引子的一个非平凡吸引域,使系统具有良好的容错性.最后,通过例子验证了得到的结论.     

Quantitative analysis of Ni effect on CCT diagrams of novel air-cooled bainite steels using artificial neural network models

The quantitative effect of Ni content on continuous cooling transformation （CCT） diagrams of novel air-cooled bainite steels was analyzed using artificial neural network models. The results showed that Ni may retard the high- and medium-temperature transformation and martensite transformation. The results conform to the materials science theories.     

Specification methods for material-handling control algorithms in flexible manufacturing systems

Good methods are needed to specify, test, and debug material-handling control logic. This article surveys a number of representative methods for defining and describing control algorithms for programmable material-handling equipment used in flexible manufacturing systems. The methods are evaluated with regard to their suitability for communication between people and as bases for interfaces to automatic program generators. It is concluded that no single method is entirely satisfactory. Three methods (position diagrams, function block diagrams, and operation networks) have potential to be combined into an effective hybrid approach that minimizes the need for the user to switch between various conceptual models.     

Synthesis and characterization of an interpenetrating polymer network composed of poly(methacrylic acid) and poly(vinyl alcohol)

Temperature and pH‐responsive interpenetrating polymer network (IPN) hydrogels, constructed with poly(methacrylic acid) (PMAA) and poly(vinyl alcohol) (PVA), by a sequential IPN method, were studied. The characterization of IPN hydrogels was investigated by Fourier‐transform infrared spectroscopy, differential scanning calorimetry (DSC) and swelling under various conditions. The IPN hydrogels exhibited relatively high swelling ratios, in the range 230–380 %, at 25 °C. The swelling ratios of the PMAA/PVA IPN hydrogels were pH and temperature dependent. DSC was used for the quantitative determination of the amounts of freezing and non‐freezing water. The amount of free water increased with increasing PMAA content in the IPN hydrogels. Copyright © 2004 Society of Chemical Industry     

应用遗传神经网络研究低渗透储层成岩储集相—以胜利渤南油田三区沙河街组为例

以渤南油田三区沙河街组为例,应用遗传人工神经网络模式识别方法,开展了低渗透储层成岩储集相的研究。该工作是在储集层沉积相、成岩作用研究的基础上,选用流动层带指标、孔隙度、渗透率、粒度中值、泥质含量、孔喉半径均值和变异系数等7项参数,采用神经网络模式识别方法,通过建立遗传神经网络的学习及预测模型,对渤南油田三区沙河街组进行了成岩储集相识别,识别出4类成岩储集相:不稳定组分强溶解次生孔隙成岩储集相、碳酸盐胶结物溶解次生孔隙成岩储集相、强压实强胶结残余粒间孔成岩储集相和极强压实强胶结致密成岩储集相。Ⅰ类储集相的储集性能最好,Ⅳ类最差,为非储层或差储层。     

Optimal ensemble construction via meta-evolutionary ensembles

In this paper, we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods.     

地震勘探技术在焉耆盆地勘探开发中的合理应用探讨

地震勘探技术应用可以分为以二维地震研究构造为主的地震勘探初级阶段、以地震地层学为主的地震勘探中级阶段、以三维地震油藏静态描述为主的地震勘探高级阶段、以地震储层研究为主的开发地震妆级阶段和以四维地震进行油藏监测为主的开发地震高级阶段。针对5个不同阶段的地质条件和地质任务，选择使用合理的地震勘探技术是有效提高勘探开发效益的重要途径。焉耆盆地的勘探开发体现了以地震勘探为主的思路，在勘探开发过程中重视了针对不同勘探开发阶段和不同的地质 地震技术，因此，在早期勘探开发中取得了很好的经济效益。     

Wavefront reduction using graphs,neural networks and genetic algorithm

This paper presents an algorithm for nodal numbering in order to obtain a small wavefront. Element clique graphs are employed as the mathematical models of finite element meshes. A priority function containing five vectors is used, which can be viewed as a generalization of Sloan's function. These vectors represent different connectivity properties of the graph models. Unlike Sloan's algorithm, which uses two fixed coefficients, here, five coefficients are employed, based on an evaluation by artificial neural networks. The networks weights are obtained using a simple genetic algorithm. Examples are included to illustrate the performance of the present hybrid method. Copyright © 2004 John Wiley & Sons, Ltd.