Detecting and diagnosing faults in dynamic stochastic distributions using a rational B-splines approximation to output PDFs

This paper presents a novel approach to detect and diagnose faults in the dynamic part of a class of stochastic systems . the Such a group of systems are subjected to a set of crisp inputs but the outputs considered are the measurable probability density functions (PDFs) of the system output, rather than the system output alone. A new approximation model is developed for the output probability density functions so that the dynamic part of the system is decoupled from the output probability density functions. A nonlinear adaptive observer is constructed to detect and diagnose the fault in the dynamic part of the system. Conver-gency analysis is performed for the error dynamics raised from the fault detection and diagnosis phase and an applicability study on the detection and diagnosis of the unexpected changes in the 2D grammage distributions in a paper forming process is included.     

Robust H∞ filtering for uncertain impulsive stochastic systems under sampled measurements

This paper is concerned with the problem of robust H_∞ filtering for uncertain impulsive stochastic systems under sampled measurements. The parameter uncertainties are assumed to be time-varying norm-bounded. The aim is to design a stochastically stable filter, using the locally sampled measurements, which ensures both the robust stochastic stability and a prescribed level of H_∞ performance for the filtering error dynamics for all admissible uncertainties. A sufficient condition for the existence of such a filter is proposed in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desired filter is given. An example is provided to demonstrate the effectiveness of the proposed approach.     

基于神经网络的非线性模型预测控制

该文提出了一种基于神经网络的模型预测控制结构 ,并使用一个新的随机搜索优化算法来求解预测控制律 ,计算机仿真证明了所设计的控制算法的正确性和有效性     

Consistency property of extended least-squares parameter estimation for stochastic diffusion equation

The extended least-squares parameter estimate for stochastic heat diffusion equations is considered. The unknown parameter is a heat diffusion coefficient which is a function of a spatial variable. Almost sure convergence for the estimated parameter is proved. A numerical example is demonstrated for supporting the theoretical results developed here.     

Least possible time paths in stochastic, time-varying networks

In this paper, two computationally efficient algorithms are presented for determining the least possible time paths for all origins to a single destination in networks where the arc weights are discrete random variables whose probability distribution functions vary with time. The first algorithm determines the least possible time path from each node for each departure time interval, the least possible travel time and a lower bound on the associated probability of the occurrence of this travel time. The second algorithm determines up to k least possible time paths, the associated travel times and the corresponding probabilities of occurrence of the travel times (or a lower bound on this probability). No such efficient algorithms for determining least time paths in stochastic, time-varying networks exist in the literature.     

Comparisons of optimal release policies for software systems

In a software product management, it is an important problem to determine the optimal release timing which minimizes the total expected cost incurred in both testing and operation phases. In this paper, we compare the performance between two kinds of software release methods, which are referred to as the T-policy and the N-policy. Based on the existing software reliability growth models, we formulate the expected cost functions and derive analytically the optimal policies under these control methods. Also, we derive a criterion for which control methods should be adopted. Finally, in numerical examples, we calculate the optimal release policies for several cost parameters.     

泥沙随机起动的简化模式

泥沙的起动既有随机性，又有模糊性，据此作者将模糊数学方法引入泥沙起动研究。建立了直接反映泥沙随机起动特性的起动流速计算模式，该模式得到了实测资料的检验。这一模式可以体现泥沙起动的随机性，较好地反映了泥沙起动的本来面目     

Queueing Petri nets with product form solution

The product form results recently published for stochastic Petri nets are combined with the well-known product form results for queueing networks in the model formalism of queueing Petri nets yielding the class of product form queueing Petri nets. This model class includes stochastic Petri nets with product form solution and BCMP queueing networks as special cases. We introduce an arrival theorem for the model class and present an exact aggregation approach extending known approaches from queueing networks.     

Observer matrix gain optimization for stochastic continuous time nonlinear systems

It is shown that for a class of stationary stochastic nonlinear systems (satisfying a global Lipschitz condition) the high-gain observer with a constant gain matrix may guarantee an upper bound for the averaged quadratic error of state estimation. The nonlinearity is assumed to be a priory known. The main contribution of this paper consists in designing of a numerical procedure for the optimal gain matrix minimizing this upper bound. The convergence analysis of this procedure is presented as well as an example illustrating its finite steps workability: it is shown that within a neighborhood of the optimal matrix gain the others provide lower estimation performance.