基于粒子群优化算法的Richards模型参数估计和算法有效性

针对Richards模型参数估计较为困难的实际问题,提出将Richards模型的参数估计问题转化为一个多维无约束函数优化问题。结合谷氨酸菌体的实际生长浓度数据,在Matlab 2012b环境中,利用粒子群优化（PSO）算法建立适应度函数,在最小线性二乘意义下估计Richards模型中的4个参数,并建立了拟合的生长曲线和最优值变化曲线。为进一步验证算法有效性,将PSO算法与该模型传统参数估计法中的四点法和遗传算法(GA)进行了比较,以相关指数和剩余标准差作为评价指标。结果表明,PSO算法对Richards模型的拟合效果良好,对模型的参数估计有着很好的适用性。     

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。     

二值probit回归模型的坍缩变分贝叶斯推断算法

给出了二值probit回归模型的坍缩变分贝叶斯推断算法.此算法比变分贝叶斯推断算法能更逼近对数边缘似然,得到更精确的模型参数后验期望值.如果两个算法得到的分类错误一致,则该算法的迭代次数较变分法明显减少.仿真实验结果验证了所提出算法的有效性.     

基于微粒群算法的非线性系统模型参数估计

微粒群优化(PSO)算法是一种进化算法,包含的概念简单.本文不同于传统的非线性模型参数估计方法,将微粒群优化算法应用于非线性系统模型(NSM)的参数估计,并通过重油热解三集总模型参数估计进行PSO算法效果测试.实验结果表明:微粒群算法为非线性系统模型参数估计提供了一种新方法.     

基于HMM和微粒群优化算法的表情识别

提出基于微粒群优化算法(PSO)的隐马尔科夫模型(HMM)训练算法,分别用PSO和量子微粒群优化算法进行HMM的参数估计,以提高HMM的性能。将改进的HMM算法应用于人脸表情识别,采用离散余弦变换提取表情特征向量。实验结果表明,该算法能有效提高表情识别率,解决HMM的参数估计问题。     

舰艇发电机励磁系统参数辨识研究

介绍辅助变量法与粒子群优化算法在舰艇发电机励磁系统辨识中的应用,并在Labview软件中编写了相应的计算程序。以某舰艇发电机励磁系统为例,在Matlab/Simulink中搭建该励磁系统仿真模型,将采样获得的输入输出数据,输入Labview辨识软件中,估计各参数值。实验分别采用+10%、+50%阶跃响应,辨识系统线性模型和非线性模型,并比较不同噪声幅值情况下的PSO辨识结果。实验结果证明PSO算法在励磁系统参数估计中的有效性。     

基于多模型半参数非线性回归模型的外弹道节省参数估计

双星定位系统的应用是外弹道估计中的一种发展趋势。文章给出基于双星、光雷的联合测量模型,建立基于半参数回归的外弹道节省参数估计模型;给出基于半参数回归的外弹道节省参数估计算法及计算步骤,并从理论上分析了基于半参数回归的外弹道节省参数估计算法能有效地消除非线性因素对参数估计性能的影响,其估计精度优于经典的节省参数建模方法,并通过仿真得到：该算法确实有效。     

基于量子微粒群算法的发酵过程模型参数估计

量子微粒群优化算法(QPSO)是一种改进的微粒群优化算法(PSO),克服了PSO算法搜索空间有限和易陷入局部极值的不足,同时该算法具有参数少、易实现、收敛速度快等优点.应用量子微粒群优化算法,以谷氨酸发酵过程产物(谷氨酸)浓度数据为检验样本,以Verhulst方程为菌体生长模型,进行发酵模型参数估计.实验结果表明,基于QPSO算法的参数估计方法具有精度高、编程实现简单、计算量小等优点.     

PSO算法在非线性模型参数估计中的应用

将微粒群优化(PSO)算法用于非线性系统模型参数估计，并通过对谷氨酸菌体生长模型的参数估计进行了验证．实验结果表明：微粒群算法为非线性系统模型参数估计提供了一种有效的途径．     

基于量子粒子群算法的复杂函数参数估计

数理统计中在处理回归的问题时,常用的传统参数估计方法存在着一些严重不足之处.为解决此问题,提出了将基于量子行为的微粒群优化(QPSO)算法应用于复杂函数的参数估计中.通过仿真实验,表明了该算法不仅可以准确地估计出复杂函数的参数,并且具有计算简便、收敛速度快等特点.通过与传统微粒群(PSO)算法的比较,证明了QPSO算法的优越性.     

PSO算法在非线性回归模型参数估计中的应用

非线性回归模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对6种非线性回归模型的参数估计进行了验证。实验结果表明：粒子群优化算法是一种有效的参数估计方法。     

基于粒子群优化算法的非线性系统模型参数估计

非线性模型的参数估计是较为困难的寻优问题,经典方法常会陷入局部极值。由于粒子群算法是一种有效的解决优化问题的群集智能算法,它的突出特点是操作简便、容易实现且全局搜索功能较强,故将粒子群优化算法用于非线性系统模型参数估计,并通过对3种典型的非线性模型的参数估计进行了验证。实验结果表明：粒子群优化算法参数估计精度高,是一种有效的参数估计方法。     

A Bayesian approach to model-based clustering for binary panel probit models

Considering latent heterogeneity is of special importance in nonlinear models in order to gauge correctly the effect of explanatory variables on the dependent variable. A stratified model-based clustering approach is adapted for modeling latent heterogeneity in binary panel probit models. Within a Bayesian framework an estimation algorithm dealing with the inherent label switching problem is provided. Determination of the number of clusters is based on the marginal likelihood and a cross-validation approach. A simulation study is conducted to assess the ability of both approaches to determine on the correct number of clusters indicating high accuracy for the marginal likelihood criterion, with the cross-validation approach performing similarly well in most circumstances. Different concepts of marginal effects incorporating latent heterogeneity at different degrees arise within the considered model setup and are directly at hand within Bayesian estimation via MCMC methodology. An empirical illustration of the methodology developed indicates that consideration of latent heterogeneity via latent clusters provides the preferred model specification over a pooled and a random coefficient specification.     

基于粒子群优化的Wiener模型辨识与实例研究

针对一类工业过程中可描述成Wiener模型的非线性系统,其辨识问题可等价成以估计参数为优化变量的非线性极小值优化问题．利用粒子群优化(PSO)算法在整个参数空间内并行搜索获得极小值优化问题的最优解(Wiener模型的最优估计),通过对粒子的迭代轨迹进行分析,改进了PSO算法中惯性权重和学习因子的选择．通过一个Wiener模型的数值仿真验证了本文提出的辨识方法的有效性和实用性,并将该方法应用在连续退火机组加热炉产品质量模型的辨识研究,取得了满意的辨识效果．     

An iterative Kalman smoother/least-squares algorithm for the identification of delta-ARX models

Additive measurement noise on the output signal is a significant problem in the δ-domain and disrupts parameter estimation of auto-regressive exogenous (ARX) models. This article deals with the identification of δ-domain linear time-invariant models of ARX structure (i.e. driven by known input signals and additive process noise) by using an iterative identification scheme, where the output is also corrupted by additive measurement noise. The identification proceeds by mapping the ARX model into a canonical state-space framework, where the states are the measurement noise-free values of the underlying variables. A consequence of this mapping is that the original parameter estimation task becomes one of both a state and parameter estimation problem. The algorithm steps between state estimation using a Kalman smoother and parameter estimation using least squares. This approach is advantageous as it avoids directly differencing the noise-corrupted ‘raw’ signals for use in the estimation phase and uses different techniques to the common parametric low-pass filters in the literature. Results of the algorithm applied to a simulation test problem as well as a real-world problem are given, and show that the algorithm converges quite rapidly and with accurate results.     

Pairwise likelihood inference for ordinal categorical time series

Ordinal categorical time series may be analyzed as censored observations from a suitable latent stochastic process, which describes the underlying evolution of the system. This approach may be considered as an alternative to Markov chain models or to regression methods for categorical time series data. The problem of parameter estimation is solved through a simple pseudolikelihood, called pairwise likelihood. This inferential methodology is successfully applied to the class of autoregressive ordered probit models. Potential usefulness for inference and model selection within more general classes of models are also emphasized. Illustrations include simulation studies and two simple real data applications.     

An improved bias-compensation approach for errors-in-variables model identification

Parametric estimation of the dynamic errors-in-variables models is considered in this paper. In particular, a bias compensation approach is examined in a generalized framework. Sufficient conditions for uniqueness of the identified model are presented. Subsequently, a statistical accuracy analysis of the estimation algorithm is carried out. The asymptotic covariance matrix of the system parameter estimates depends on a user chosen filter and a certain weighting matrix. It is shown how these can be tuned to boost the estimation performance. The numerical simulation results suggest that the covariance matrix of the estimated parameter vector is very close to the Cramér-Rao lower bound for the estimation problem.