Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity

Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification.Mathematical functions for the a posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R = 0.890 and R = 0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively.These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort's bias towards low SI insulin resistant subjects, limiting the data set's ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications.     

非线性动力系统的参数反演及灵敏度分析

研究了非线性动力系统中的微分方程参数反演问题,给出了求解此类问题的信赖域算法,并对参数的灵敏度进行了详细分析,给出了数值模拟。     

非均匀采样多率ARMAX系统RELS算法性能分析

针对一类有色噪声干扰的非均匀采样多率ARMAX系统的辩识问题,基于增广参数维数理论,将系统模型参数化,将信息向量中含有的不可测噪声项用其估计残差代替,推导了非均匀采样ARMAX系统的递推增广最小二乘(RELS)算法;利用鞅收敛定理对该算法的收敛性进行了理论分析,结果表明该算法在噪声方差有界和广义持续激励的条件下能够收敛到真参数.仿真例子验证了该算法具有良好的收敛速度与估计精度.     

状态空间模型基于正交梯度搜索的预报误差辨识

提出状态空间模型的正交梯度搜索的预报误差辨识方法.通过极小化输出预报误差而获得系统的参数估计,提出了在观测等价类相切面正交子空间进行参数更新的实现方法,从而解决了全参数化引起的状态空间模型参数非唯一性问题;给出了融合预报误差局部线性逼近性能的正则化因子自适应确定方法,揭示了算法复杂度与系统的能控性、能观测性的内在联系.仿真结果验证了所提出方法的有效性.     

Uniformization based sensitivity estimation for a class of discrete-event systems

Infinitestimal Perturbation Analysis (IPA) estimators are based on particular couplings of parameteric families of discrete event systems where small changes in the parameter value, typically, cause small changes in the timing of events and, for finite horizons, the sequence of states visisted remains the same. We consider another coupling approach based on the uniformization procedure and a simple generalization of it. In our case any small change in the parameter value causes a change in the state of the system; our parameterization of trajectories keeps them highly synchronized, hence the effect of such changes can be estimated, sometimes efficiently. In this framework, we define three tupes of performance sensitivity estimators for a broad class of performance measures and with respect to a range of parameter values. Performance measures on finite deterministic horizons are considered and it is shown that they are unbiased under mild conditions. We show that for some systems the derivative estimators can be calculated from a nominal sample path of the system.     

模型降阶和参数估计的一种快速遗传算法

针对传统遗传算法求解模型降阶和参数估计时,适配值评价既费时又效率较低的缺点,对实数编码GA引入相似度和可信度的概念,采用插值方法进行适配值评价,大大减少了评价环节的计算量,提高了整个算法的效率和实时性．通过对典型模型降阶和参数估计问题的仿真,验证了所提出方法的可行性和有效性．     

On model and filter sensitivity

It is shown that there is a stochastic version of the fact that the standard IIR, AR, ARX and ARMAX model and filter parametrizations suffer from a worst-case exponential growth of the required relative coefficient accuracy to preserve model and filter stability. This means that, inevitably, such standard model and filter parametrizations become infeasible to use for a subclass of order n filters and models when n is not small.     

基于统计学理论的参数模型运动估计方法

运动估计对视频编码十分重要,基于参数模型的运动估计方法也越来越受到人们的关注,参数模型的选择是该方法的关键。基于此,提出了基于统计学原理的模型选择方法,它以少量的图像数据流为基础,通过参数估计,并分析各近似模型的预测风险和误差,选出最优模型,它最符合预测对象的实际发展变化规律,进而利用该模型对未知对象进行运动估计。试验结果表明,在对实际图像序列进行运动估计时,这种方法是可靠并且实用的。     

Quantifying the accuracy of Hammerstein model estimation

This paper investigates the accuracy of the linear component that forms part of an overall Hammerstein model-structure estimate, and a key finding is that the process of estimating the non-linear element can have a strong effect on the associated estimate of the linear dynamics. Furthermore, this effect is not explained simply by way of considering how the input spectrum is changed by the non-linearity. Instead, it arises that the linear model-estimate variability may be dominated by a term that depends on the frequency response of the linear system itself. Amongst other things, the main results derived here have experiment design implications for Hammerstein system estimation.     

Mathematical model of heart rate regulation during exercise

A model of the heart rate regulation during physical load and recovery has been developed. The model contains four parameters with the following physiological meanings: Heart rate increment related to the load of 1 watt, a load which can be coped with by vagal inhibition, a time constant of the vagal component and a time constant of the slower complex neurohumoral sympathetic component. All these parameters were estimated uniquely with relatively small reliability intervals for a group of 15 healthy men. The results are useful practically for the evaluation of the physical efficiency of individuals and whole groups of healthy subjects.     

Recursive estimation of the parameters of linear multivariable systems

A recursive algorithm is proposed for the identification of linear multivariable systems. Utilization of a canonical state space model minimizes the number of parameters to be estimated. The problem of identification in the presence of noise is solved by using a recursive generalized least-squares method.     

Procedures for parameter and state estimation of microbial growth process models

Practical problems concerning parameter and state estimation of microbial growth processes, with application to batch and continuous fermentation processes and to the activated sludge wastewater treatment process are discussed. Besides the usual aggregated single substrate-single organism mass balance model with Michaelis-Menten growth dynamics, an alternative model, in which the biomass concentration is divided into age classes, is introduced. A method in which the parameters were adjusted manually with sensitivity functions was used for parameter estimation. With the aid of some examples it was shown that the Michaelis-Menten model is not practically identifiable. A recursive state-estimation algorithm for control and supervision purposes was developed and its application in combination with both the aggregated and the age distribution model was demonstrated.     

State and parameter estimation of microbial growth processes

Based on a second order nonlinear model of microbial growth, two distinct problems—state estimation and parameter estimation—are considered. The first case assumes only one state is available from measurement and an asymptotic estimate of the other state is required. The second problem assumes both states are available, and considers the design of an estimator to provide asymptotic estimates of the growth system parameters.     

图像统计模型参数估计中的期望最大值算法

期望最大值算法是近年来图像统计模型参数估计技术领域的研究热点之一。在对期望最大值算法分析的基础上,结合其在图像统计模型参数估计中的应用研究,对改变标准期望最大值算法的3种方式进行比较分析。结合图像恢复、分割、目标跟踪以及与其他优化算法的融合应用,从丢失数据集的选取、丢失数据集和不完全数据集统计模型的建立,以及统计模型参数估计3个方面,评述期望最大值算法优缺点。丢失数据的选取和不完全数据的描述形式直接决定期望最大值算法的结构和计算复杂度,以致算法的成败。最后,讨论期望最大值算法目前存在的问题及未来的发展方向,指出其在具有丢失数据统计模型参数估计中广泛应用。     

应用遗传算法估算溶液热力学模型参数

应用遗传算法估算溶液热力学模型参数,并对标准遗传算法中的变异策略和竞争方式作了适当的改进,得到改进的遗传算法。举DMF(二甲基甲酰胺)+water体系的溶液热力学模型参数的估算为例,并与POWELL法比较。计算结果表明,遗传算法比POWELL法具有更强的寻优能力,而本文所提出的改进的遗传算法比标准遗传算法的寻优速度明显较快,对解决溶液热力学模型这类复杂的非线性函数的参数估算问题,本文所提出的改进的遗传算法是一种较好的寻优算法。     

Multi-output parameter estimation of dynamic systems by output shapes

A multi-output method of parameter estimation is introduced for dynamic systems that relies on the shape attributes of model outputs. The shapes of outputs in this method are represented by the surfaces that are generated by continuous wavelet transforms (CWTs) of the outputs in the time-scale domain. Since the CWTs also enhance the delineation of outputs and their sensitivities to model parameters in the time-scale domain, regions in the time-scale plane can be identified wherein the sensitivity of the output with respect to one model parameter dominates all the others. This allows approximation of the prediction error in terms of individual model parameters in isolated regions of the time-scale domain, thus enabling parameter estimation based on a small set of wavelet coefficients. These isolated regions of the time-scale plane also reveal numerous transparencies to be exploited for parameter estimation. It is shown that by taking advantage of these transparencies, the robustness of parameter estimation can be improved. The results also indicate the potential for improved precision and faster convergence of the parameter estimates when shape attributes are used in place of the magnitude.     

一般双率随机系统状态空间模型及其辨识

对于双率采样数据系统,本文使用提升技术,推导了双率系统的提升状态空间模型.对于系统状态可测量的双率系统,利用最小二乘原理辨识提升系统模型的参数矩阵;对于状态不可测的未知参数双率系统,利用递阶辨识原理,提出了双率系统递阶状态空间模型辨识方法,来辨识系统的状态和参数.具体做法:基于获得的状态估计和提升系统的输入输出数据递归估计系统参数,然后基于获得的参数估计,计算系统的状态.     

基于粒子群算法的Logistic回归模型参数估计

针对Logistic回归模型中的参数估计计算复杂难题,提出一种基于粒子群优化算法（PSO）的估计方法。以最大似然准则作为粒子群优化算法的适应度函数,建立了Logistic回归模型中的参数估算模型。数值仿真分析表明,粒子群优化算法可以更精确地计算出相关参数。