陆相碎屑岩储层隔夹层成因、特征及其识别

陆相储层隔夹层是形成储层流体流动非均质的主要因素之一，也是储层精细表征的重要内容，油田勘探开发实践表明，陆相储层隔夹层主要可分为泥质隔夹层、钙质隔夹层和物性隔夹层三大类。根据这种分类，深入分析和总结了这三类隔夹层成因、岩性特征、分布特征和测井识别特征，并比较其区别，这对以后新老区块隔夹层研究有参考和适用价值。     

陆相断陷湖盆中层序边界的形成机理及其识别特征研究

陆相断陷湖盆中层序边界是由于沉积物表面高出沉积基准面或盆地基准面、沉积物发生剥蚀或无沉积作用所形成的，其产生机理主要有边界断层停止活动、断块翘倾活动、湖盆整体抬升和湖平面下降。层序边界在地震特征上、测井曲线上、钻井剖面中的岩性、岩相特征、古生物组合、微量元素的变化上均有明显的特征。利用这些特征，在济阳坳陷下第三系中识别出9个三级层序。     

Step response classification for model-based autotuning via polygonal curve approximation

A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results.     

Discrete-time adaptive sliding mode power system stabilizer with only input/output measurements

In this paper a discrete-time adaptive sliding mode control method is newly developed and applied to the power system stabilization problem. A controllable canonical form of state space realization is constructed using the parameters identified by the on-line recursive least squares method and the system state is estimated from the input/output measurements and the simple state transformations. The identified parameters and the estimated state are then used by the discrete-time sliding mode control, which is suitable for the digital equipment. The most important advantage of the proposed power system stabilizer (PSS) is that it is able to maintain its regulating performance with a slower sampling period than that of the conventional sliding mode PSS because it is developed in a pure discrete-time domain. Another advantage of the proposed PSS is that it needs neither a mathematical model of the power system nor the full-state measurements because they are identified through on-line identifications. Several computer simulations for the linear power system are performed to verify the performance of the proposed PSS. In the computer simulations for various circumstances which are probable in a power system are considered, such as transitions of the active and reactive powers, change of parameters of the synchronous machine, line-to-ground faults and measurement noise. As a result, a new power system stabilizer which can operate in a wide range of operating conditions and can overcome various disturbances and measurement noises is proposed.     

Parameter identification for nonlinear systems: Guaranteed confidence regions through LSCR

In this paper we consider the problem of constructing confidence regions for the parameters of nonlinear dynamical systems. The proposed method uses higher order statistics and extends the LSCR (leave-out sign-dominant correlation regions) algorithm for linear systems introduced in Campi and Weyer [2005, Guaranteed non-asymptotic confidence regions in system identification. Automatica 41(10), 1751-1764. Extended version available at 〈http://www.ing.unibs.it/∼campi〉]. The confidence regions contain the true parameter value with a guaranteed probability for any finite number of data points. Moreover, the confidence regions shrink around the true parameter value as the number of data points increases. The usefulness of the proposed approach is illustrated on some simple examples.     

舰用燃气轮机框架模型的模态参数识别

本文采用一次有理正交多项式－多模态曲线合法对一舰用燃气轮机框架模型进行了实验模态分析，以Ｆｏｒｓｙｔｈｅ复合交多项式作为频响函数的理论值，用实测的频响函数建立系数矩阵，来寻找使目标函数最小的最优值，本文在ＨＰ９８２６微机上建立了建立了测试，数据采集及参数识别等系统识别软件，给出了具有工程实用意义的前五阶振动模态参数，识别结果和有限元计算相比较，令人满意。     

地层微电阻率成像测井在中原油田的应用

介绍地层微电阻率测井(FMI)技术以及它在地质构造解释、沉积学解释、裂缝识别、地层分析等方面独特的枝术优势,并详细介绍了FMI在中原油田濮深X井的实际应用,进行了应用效果分析,指出成像测井是未来油田油气勘探开发的重要技术手段。     

火箭弹道附近大气参数辨识系统的研究

研究了利用雷达实测数据对沿火箭飞行弹道的大气参数进行辨识和外推落点的方法，进而提出了一个对效力射诸元进行修正的方法。在此基础上，对软件系统进行了设计。最后通过仿真试验，分析了系统的准确性。     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model

A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities.The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone,based on which a universal-type parametric model can be established to approximate the entire system.This model can be established without separating the nonlinear part from the linear part.The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm.Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.