SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

RBF-CSR方法及其应用于裂解装置建模的研究

RBF－CSR是在分析RBF－PLS的基础上提出的新方法。它保留了RBF－PLS的优点：采用神经网络的结构，又用数学方法直接求解，免去了ANN冗长的训练过程和其它诸多欠缺。RBF－CSR方法可以在更宽广的空间内寻找最优的网络参数，它所建立的模型具有很强的预报精度和良好的稳定性，又有简洁的解析形式，便于优化等进一步的计算和处理。该方法已成功地应用于裂解装置的建模。     

Adaptive model predictive inventory controller for multiproduct warehouse system

An inventory control system has been developed for a distribution system consisting of a single multiproduct warehouse serving a set of customers and purchasing products from multiple vendors. Purchase orders requesting multiple products are delivered to the warehouse in a process referred to as joint replenishment. The receipt of customer orders by the warehouse proceeds in order intervals and in order quantities that are subject to random fluctuations. The objective of warehouse operation is to minimize the total cost while maintaining inventory levels within the warehouse capacity by adjusting the purchase order intervals and quantities. An adaptive model predictive control algorithm is developed using a periodic square wave model to represent the material flows. The adaptive concept incorporates a stabilized minimum variance control-type input calculation coupled with input/output stream parameter predictions. The boundedness of the control output under the suggested algorithm is mathematically proven under the assumption that disturbances in the orders are bounded. The effectiveness of the scheme was demonstrated using simulations.     

聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的同时测定

为了在线监控聚氯乙烯塑料中增塑剂的含量,利用傅立叶变换红外光谱法-ATR技术测定样品的红外光谱,采用偏最小二乘法对所测红外光谱进行分析,建立了一种无损快速分析方法,定量地测定聚氯乙烯塑料中多种邻苯二甲酸酯类增塑剂的含量。该方法测量精密度的变异系数小于6%,测量准确度的变异系数小于3%。该方法不受基体的干扰,简单快速,适合于流水线的质量控制。     

A fuzzy constraint-based approach to data reconciliation in material flow analysis

Data reconciliation consists in modifying noisy or unreliable data in order to make them consistent with a mathematical model (herein a material flow network). The conventional approach relies on least-squares minimization. Here, we use a fuzzy set-based approach, replacing Gaussian likelihood functions by fuzzy intervals, and a leximin criterion. We show that the setting of fuzzy sets provides a generalized approach to the choice of estimated values, that is more flexible and less dependent on oftentimes debatable probabilistic justifications. It potentially encompasses interval-based formulations and the least squares method, by choosing appropriate membership functions and aggregation operations. This paper also lays bare the fact that data reconciliation under the fuzzy set approach is viewed as an information fusion problem, as opposed to the statistical tradition which solves an estimation problem.     

双硫腙负载树脂对铅和镉的吸附性能研究

用双硫腙溶液在特定条件下处理201×7阴离子树脂,制取了双硫腙负载树脂。经过最佳条件选择,在同一水样中可以同时进行富集、洗脱铅和镉。经过方波溶出伏安法测定,可得到铅和镉离子的峰电位分别为:-0.41 V-、0.62 V。根据溶出峰的峰电位、峰高,进行定性和定量分析。实验结果表明:双硫腙负载树脂用于金属离子的富集分离,具有交换速度快、易于洗脱、富集倍数大、选择性好等优点。     

偏最小二乘-神经网络光度法同时测定水中的NO3-和NO2-

应用偏最小二乘-神经网络直接解析硝酸根和亚硝酸根的紫外光谱,不经分离紫外吸光光度法同时测定硝酸根和亚硝酸根。在BP算法上,引用改进的误差传递函数,并采用均匀试验设计法确定了最佳网络运行参数。用于水样中硝酸根和亚硝酸根的同时测定,回收率分别为99.0%,105.0%。     

一种SISO线性系统的系统辨识软件包的开发

本文基于系统辨识的最小二乘法,介绍一种用Basic语言编程的SISO线性系统系统辨识软件包及其原理、结构、功能与特点。它包括输入/输出数据的生成,近十种辨识程序模块的调用,结果的处理,模型阶次与纯滞后时间的确定和模型校验等五大部分。经应用已证明设计合理,方便可靠,并具有结构简单灵活等特点。     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.     

面向大规模数据流处理的偏最小二乘法

用光谱分析鉴别生物特征,导致数据量大,而实际需要必须实时处理。偏最小二乘法是使用最广泛的鉴别算法,但是对于大规模数据流该算法无法达到实时性。为了解决这个应用矛盾,提出了一种基于NVIDIA CUDA架构下的并行计算策略,利用具有大规模并行计算特征的图形处理器（GPU）作为计算设备,结合GPU存储器的优势实现了偏最小二乘算法。实验的测试结果表明,在GPU上使用CUDA实现的偏最小二乘算法比在CPU上实现该算法快了47倍,性能得到了显著提高,从而使偏最小二乘算法应用于大规模数据流处理成为可能。