基于混合高斯过程模型的高光谱图像分类算法

提出了一种基于混合高斯过程模型的高光谱遥感图像分类算法,它不同于传统的基于多元统计的分类方法.为更好利用高光谱遥感图像的高谱分辨率特点,首先将函数数据分析的思想引进高光谱数据的分类问题,分类对象视为像元对应的谱线,故它们是函数型数据.为了有效模拟地物在空间上的分片聚集特性,则将混合高斯分布模型推广到混合高斯过程模型并用于高光谱数据分类算法中.数值实验表明,混合高斯过程模型是处理函数型数据的有效方法.     

一维紧邻的接触过程与选举模型的混合过程的光滑性与单调性

证明了一维紧邻的接触过程与选举模型的混合过程,其极点平稳分布除在临界点λc 处外总是光滑的,且该混合过程具有单调性.     

两个粒子接触一一排它混合模型的局部遍历性

本文研究两个粒子接触一一排它混合模型的局部遍历性,证明了临界值δc的存在性,并对δc的上下界作了粗略的估计。同时证明了在δ〉δc的条件下该模型即为排它过程。     

标的股价服从混合过程的期权定价公式及有限元算法

本文将马尔科夫跳跃过程叠加于 Ito过程 ,形成混合过程 ,并用该过程来刻画股价走势情况。而后在标的股价服从混合过程的基础上 ,推导出了欧式看涨期权的定价公式 ,并对美式看跌期权定价给出了有限元算法。     

混合分数O-U过程的最小范数估计

本文研究混合分数O-U过程的最小范数估计问题.利用分数布朗运动驱动的随机微分方程偏差不等式,获得了混合分数O-U过程漂移参数的最小范数估计、相合性及渐近分布.     

一类两指标过程—EARMA过程

本文将两指标 ARMA 过程加以推广,提出了一类新的两指标弱平稳过程——EARMA 过程,给出了它的平稳性条件和可逆性条件;讨论了它的若干性质;并讨论了它的参数估计,其中主要是自相关估计的强相合速度。     

二参数ORNSTEIN—UHLENBECK过程在射线上的导出过程

本文证明了二参数ORNSTEIN-UHLENBECK过程在射线上导出的过程是马氏过程,并求出了它的转移密度。同时证明了它为oup1的充要条件及它是弱平稳过程的充要条件。     

随机环境中纯生过程的一个极限性质

本文证明了随机环境中纯生过程的一个强极限性质,假设环境为一混合序列,不必平稳,且具有有限均值,混合系数由环境序列中一对随机变量而定。     

等价类空间中离散时间选举-排它混合模型的遍历性

利用李雅谱诺夫函数首先证明了等价类空间中离散时间非紧邻选举模型是正常返的,且首次击中D0时刻的阶为15/14,其次给出了等价类空间中离散时间非紧邻选举模型与排它过程的混合模型遍历性的一个判别准则,从而推广和改进了紧邻情形的相应结果.     

等价类空间中离散时间选举一排它混合模型的遍历性

利用李雅谱诺夫函数首先证明了等价类空间中离散时间非紧邻选举模型是正常返的,且首次击中D0时刻的阶为15/14,其次给出了等价类空间中离散时间非紧邻选举模型与排它过程的混合模型遍历性的一个判别准则,从而推广和改进了紧邻情形的相应结果.     

标的资产服从一类混合过程的欧式未定权益定价

文中假设标的资产价格服从受分数布朗运动和泊松过程共同驱动的一类混合模型，并给出了基于这一模型的欧式未定权益定价的基本公式，以及欧式看涨、看跌期权和上限型欧式期权的定价公式。     

基于效用比的多工序并联制造过程能力分析

多工序制造过程通常包含串联和并联两种结构,具有串并联混合结构的多工序制造过程是实践中最为常见的形式,而不同模式的并联结构其上游工序质量波动对下游工序及总过程能力的影响不同。针对多工序制造过程并联结构特点,本文从波动减少的角度重点对多工序并联制造过程中并行、分散和收敛三种基本模式进行过程能力分析,研究多工序制造过程中各子过程波动对整体过程能力的影响,并根据各子过程质量波动减少的“困难度”和“效用比”评价质量改进的效果,给出多工序并联过程能力改进策略选择依据。通过实例表明,本方法能较好地识别各工序质量波动减少对本工序过程能力和总过程能力的不同影响,确定质量改进的优先顺序,实现多工序制造过程的经济性质量改进和优化。     

违约风险的交换期权的定价模型与解法

含交易对手违约风险的交换期权采用混合模型定价,借助公司价值模型中的补偿率,同时采用以强度为基础的违约函数来确定违约的发生.假定违约强度遵从均值回复的重随机Poisson过程：且违约强度过程与标的资产,企业价值都相关.利用等价鞅测度变换方法导出含有违约风险的交换期权的价格闭解.     

Real-coded Genetic Algorithm for system identification and tuning of a modified Model Reference Adaptive Controller for a hybrid tank system

Modeling and controlling of level process is one of the most common problems in the process industry. As the level process is nonlinear, Model Reference Adaptive Control (MRAC) strategy is employed in this paper. To design an MRAC with equally good transient and steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID) controller are employed, and their performances are compared by using MATLAB. Input–output data of a coupled tank setup of the hybrid tank process are obtained by using Lab VIEW and a system identification procedure is carried out. The accuracy of the resultant model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient and steady state performance during the control of any nonlinear process.     

求解带时间窗车辆路径问题的混合Memetic算法

为提高带时间窗车辆路径问题的求解精度和求解效率,设计了一种混合Memetic算法。采用基于时间窗升序排列的混合插入法构造初始种群,提高解质量的同时兼顾多样性,扩大搜索空间;任意选择组成父代种群,以维持搜索空间;运用简化的变邻域搜索进行局部开发,引入邻域半径减少策略提高开发效率,约束放松机制开放局部空间;以弧为对象,增加种群向当前最优解和全局最优解的后学习过程。实验结果表明,所提出的算法具有较好的寻优精度和稳定性,能搜索到更好的路径长度结果,更新了现有研究在最短路径长度的目标函数上的下限。     

Stochastic stability analysis for joint process driven and networked hybrid systems

The stochastic stability and impulsive noise disturbance attenuation in a class of joint process driven and networked hybrid systems with coupling delays (JPDNHSwD) has been investigated. In particular, there are two separable processes monitoring the networked hybrid systems. One drives inherent network structures and properties, the other induces random variations in the control law. Continuous dynamics and control laws in networked subsystems and couplings among subsystems change as events occur stochastically in a spatio-temporal fashion. When an event occurs, the continuous state variables may jump from one value to another. Using the stochastic Lyapunov functional approach, sufficient conditions on the existence of a remote time-delay feedback controller which ensures stochastic stability for this class of JPDNHSwD are obtained. The derived conditions are expressed in terms of solutions of LMIs. An illustrative example of a dynamical network driven by two Markovian processes is used to demonstrate the satisfactory control performance.     

Stochastic hybrid system with non-homogeneous jumps

A study of a class of stochastic hybrid dynamic processes is investigated. The hybrid dynamic process is composed of both continuous and discrete time states. In this work we assume that its continuous time state is driven by the Brownian motion process, while the transitions of its discrete time state are governed by either a non-homogeneous Poisson process or by hitting the boundaries. Under this formulation we develop an infinitesimal generator of the stochastic hybrid dynamic process. Moreover we obtain results concerning the quantitative properties of the solution process. A few illustrative examples are presented.     

Robust stabilization of a class of state-dependent jump linear systems

In this article, we consider a continuous-time state-dependent jump linear system (SDJLS), a kind of stochastic hybrid system, with the presence of uncertainties in system parameters. In SDJLS, we consider that the transition rates of the underlying random jump process depend on the state variable. In particular, we assume the transition rates to have different values across suitably defined sets to which the state of the system belongs, and address a problem of robust stability and stabilization analysis. We obtain sufficient conditions for robust stability and state-feedback stabilization in terms of linear matrix inequalities (LMIs). We validate the obtained sufficient robust stability and stabilization conditions with numerical examples.     

On existence and uniqueness of solutions to uncertain backward stochastic differential equations简

This paper is concerned with a class of uncertain backward stochastic differential equations （UBSDEs） driven by both an m-dimensional Brownian motion and a d-dimensional canonical process with uniform Lipschitzian coefficients. Such equations can be useful in mod- elling hybrid systems, where the phenomena are simultaneously subjected to two kinds of un- certainties： randomness and uncertainty. The solutions of UBSDEs are the uncertain stochastic processes. Thus, the existence and uniqueness of solutions to UBSDEs with Lipschitzian coeffi- cients are proved.