甘草真伪品的FTIR光谱法鉴别研究

采用傅里叶变换红外(FTIR)光谱法并结合二维相关(Two-dimensional correlation spectroscopy)分析技术分别对药用植物甘草(药材对照品)及其伪品刺果甘草进行了无损快速鉴别研究。 结果表明：虽然甘草和伪品刺果甘草都是来源于同一科属,但两者所含化学组分的含量不同,其红外光谱图既有一定的差异,又有一定的相似。 而在红外二阶导数谱图上差异较明显,在二维红外谱图的差别不但较明显而且很直观。 凭借这些差异,可方便地进行真伪鉴别,同时还进一步表明了这两种甘草药材中化学组分之间的差异。 该法无损,快速,准确,样品量少,为客观评价中药材的来源,真伪,优劣等提供了一种新的方法和手段。     

红外光谱结合光谱检索对石斛品种的鉴别研究

用傅里叶变换红外光谱技术结合光谱检索的方法对石斛茎进行品种鉴别研究。测试了23个品种,165株石斛茎样品的红外光谱,光谱显示,各样品的红外光谱非常相似,石斛茎中的主要成分是纤维素等多糖物质。利用光谱软件Omnic8.0建立了三个光谱数据库Lib01、Lib02和Lib03,Lib01由每个品种前4株样品的平均光谱组成,Lib02由平均光谱的一阶导数光谱组成,Lib03由平均光谱的二阶导数光谱组成。各样品光谱分别与Lib01进行指定在1 800～500 cm-1光谱范围的相关性、平方差和平方微分差检索,相关性检索的正确率达到92.7%,平方差检索的正确率达到74.5%,平方微分差检索的正确率达到92.7%。各样品光谱的一阶导数光谱和二阶导数光谱分别与Lib02和Lib03进行指定在1 800～500 cm-1光谱范围的平方微分差检索,一阶导数光谱的检索正确率达到93.9%,二阶导数光谱的检索正确率达到90.3%。结果表明,平方微分差算法的一阶导数红外光谱检索更适合于石斛的品种鉴别。红外光谱结合光谱检索的方法能较好的鉴别不同品种的石斛,有望成为简便、易行的植物品种鉴别方法。     

Feng混沌系统参数识别及自适应模糊控制器的反步设计

设计了参数观测器,对Feng混沌系统中未知参数进行识别.采用反步控制方法,在存在输入扰动项的情况下,构造模糊自适应控制器控制Feng混沌系统.模糊逻辑系统用来逼近反步控制方法中出现的未知、非线性且结构复杂的函数.和传统的反步控制方法相比,本文采用的控制器结构简单,易于实现.仿真的结果验证了本文控制方法的有效性.     

Data-Recursive Smoother Formulae for Partially Observed Discrete-Time Markov Chains

Abstract

We address a dynamic portfolio optimization problem where the expected utility from terminal wealth has to be maximized. The special feature of this paper is an additional constraint on the portfolio strategy modeling bounded shortfall risks, which are measured by value at risk or expected loss. Using a continuous-time model of a complete financial market and applying martingale methods, analytic expressions for the optimal terminal wealth and the optimal portfolio strategies are given. Finally, some numerical results are presented.     

Stochastic Approximation Algorithms for Parameter Estimation in Option Pricing with Regime Switching

Abstract

This work is concerned with option pricing. Stochastic approximation/optimization algorithms are proposed and analyzed. The underlying stock price evolves according to two geometric Brownian motions coupled by a continuous-time finite state Markov chain. Recursive stochastic approximation algorithms are developed to estimate the implied volatility. Convergence of the algorithm is proved. Rate of convergence is also ascertained. Then real market data are used to compare our algorithms with other schemes.     

A least squares identification algorithm for a state space model with multi-state delays

A parameter estimator is presented for a state space model with time delay based on the given input–output data. The basic idea is to expand the state equations and to eliminate some state variables, and to substitute the state equation into the output equation to obtain the identification model which contains the information vector and parameter vector. A least squares algorithm is developed to estimate the system parameter vectors. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm.     

A differential equation model for predicting public opinions and behaviors from persuasive information: Application to the index of consumer sentiment

This paper shows that ideodynamics is a differential equation model capable ofpredicting public opinions and behaviors from persuasive information. Statistics are also developed for the model. The methodology is applied to predict the time trend of public opinion about the economy as quantified by the Index of Consumer Sentiment compiled by the University of Michigan. The explanatory variables are derived from news coverage of the economy in positive and negative terms.     

Model-Based Recursive Partitioning

Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a dataset; (2) test for parameter instability over a set of partitioning variables; (3) if there is some overall parameter instability, split the model with respect to the variable associated with the highest instability; (4) repeat the procedure in each of the daughter nodes. The algorithm yields a partitioned (or segmented) parametric model that can be effectively visualized and that subject-matter scientists are used to analyzing and interpreting.     

Multivariate polynomial regression for identification of chaotic time series

Multivariate polynomial regression was used to generate polynomial iterators for time series exhibiting autocorrelations. A stepwise technique was used to add and remove polynomial terms to ensure the model contained only those terms that produce a statistically significant contribution to the fit. An approach is described in which datasets are divided into three subsets for identification, estimation, and validation. This produces a parsimonious global model that is can greatly reduce the tendency towards undesirable behaviours such as overfitting or instability. The technique was found to be able to identify the nonlinear dynamic behaviour of simulated time series, as reflected in the geometry of the attractor and calculation of multiple Lyapunov exponents, even in noisy systems.

The technique was applied to times series data obtained from simulations of the Lorenz and Mackey – Glass equations with and without measurement noise. The model was also used to determine the embedding dimension of the Mackey – Glass equation.