Approximate channel identification via δ‐signed correlation

A method of approximate channel identification is proposed that is based on a simplification of the correlation estimator. Despite the numerical simplification (no multiplications or additions are required, only comparisons and an accumulator), the performance of the proposed estimator is not significantly worse than that of the standard correlation estimator. A free (user selectable) parameter moves ‘smoothly’ from a situation with small sum‐squared channel estimation error but hard‐to‐identify channel peaks, to one with a larger sum‐squared estimation error but easy‐to‐identify channel peaks. The proposed estimator is shown to be biased and its behaviour is analysed in a number of situations. Applications of the proposed estimator to sparsity detection, symbol timing recovery and to the initialization of blind equalizers are suggested. Copyright © 2002 John Wiley & Sons, Ltd.     

储层识别模式的研究

用数学方法建立气测井油气储层识别模式,以解决录井中实时软件在判别依据少的条件下,及时识别油气显示假异常的难题。该判别式经实用,正到率达90％。     

TRANSFER FUNCTION MODEL ORDER AND PARAMETER ESTIMATION

Abstract. A distributed lag model with a rational transfer function is considered. We have demonstrated that the C -table can be used to determine the orders of the rational transfer function and the Padé table can be used to estimate the parameters of an identifiable transfer function. In case the model is not uniquely identified, the C -table reduces possible competing models to only a few for further examination. Padé approximant gives consistent initial values for possibly more efficient iterative procedures to estimate the coefficients of the identified model. Some asymptotic results on the estimation of the C -table and the Padé table are given. A few examples are presented to demonstrate the effectiveness of the method.     

Sensorless control of synchronous reluctance motors using on‐line parameter identification method not affected by position estimation accuracy

This paper presents a novel on‐line parameter identification method for sensorless control of Synchronous Reluctance Motors (SynRMs). Although conventional sensorless control methods based on mathematical models usually need some complex measurements of motor parameters in advance, the proposed identification method does not require them and can be realized on‐line. The proposed method identifies motor parameters under sensorless control, so rotor position and velocity cannot be used to identify these parameters. However, the proposed method does not need rotor position and velocity, and identified parameters are not affected by these estimation errors. The sensorless control using identified motor parameters is realized, and the effectiveness of the proposed method is verified by experimental results. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 155(3): 62–69, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20258     

微通道电泳芯片系统中信号识别算法的设计与实现

介绍适用于微通道电泳芯片系统的DNA测序软件的总体流程，以及其中的信号处理与识别算法。以激光诱导荧光检测系统所采集到的原始数据为源信号，以小波平滑和小波去噪为理论基础，将滤波算法和峰值识别算法综合在一起进行设计，从而使其适用于检测速度更快、样品量更少的微通道电泳芯片系统。将本算法应用于DNA片段的分离实验中，可以有效地达到滤波以及信号识别的目的。     

高层建筑损伤识别中的补偿算法

本文在已有研究工作的基础上，提出了处理激励测量信息不完备情况下的补偿算法。此方法主要针对高层建筑动力检测中强迫激励的情况，是一种基于时域的识别方法，该方法实际上是利用客观工程实际中结构系统激励所具有的物理持征作为识别计算的辅助条件，来解决测量信息的不完备性问题。算例分析表明了该方法的可行性。     

无基坑轨道衡的研究

本文对无基坑轨道衡的开发与研制,进行了可行性研究。     

测井相识别模型及其在东海油气勘探中的应用

文章提出一种测井相识别模型,该模型用常规测井信息,定量地确定测井相在钻遇地层中的空间位置,建立测井相。配合东海油气勘探工作,用该识别模型对苏堤构造带的钻遇地层进行了测井相分析。     

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.     

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.