Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.     

Reliability models for computer systems: An overview including dataflow graphs

The reliability of a system is the probability that the system will perform its intended mission under given conditions. This paper provides an overview of the approaches to reliability modelling and identifies their strengths and weaknesses. The models discussed include structure models, simple stochastic models and decomposable stochastic models. Ignoring time-dependence, structure models give reliability as a function of the topological structure of the system. Simple stochastic models make direct use of the properties of underlying stochastic processes, while decomposable models consider more complex systems and analyse them through subsystems. Petri nets and dataflow graphs facilitate the analysis of complex systems by providing a convenient framework for reliability analysis.     

局域网服务器缓冲区容量的Petri网设计方法

本文在分析局域网服务器信息缓冲区、数据包接受和发送缓冲区的相互关系基础上提出一种新的局域网服务器缓冲区设计方法─—广义随机Ｐｅｔｒｉ网方法，并给出了局域网服务器信息缓冲区的广义随机Ｐｅｔｒｉ网模型。该设计方法理论完备、严谨实用，且不再局限于追求某一边界值（上限或下限），并且避免了排队理论处理多级服务的繁琐公式。最后本文用一例题说明了这种方法的应用。     

三维结构可靠度对随机变量的敏感性研究

本文基于三维随机有限元提出了结构点可靠度、失效模式的可靠度及整个结构体系的可靠度分别对随机变量的＂分布参数＂和＂极限状态方程参数＂的敏感性计算方法，并以一典型重力坝为例进行了计算，得出了若干有益的结论。     

Optimal predictor for a singular random process

An optimal predictor is developed for a singular random process generated by a known system driven by a white noise sequence. The properties of the predictor are compared with those of the optimal predictor for a nonsingular random process.     

A stochastic maximum principle for systems with jumps, with applications to finance

We consider a stochastic control problem with linear dynamics with jumps, convex cost criterion, and convex state constraint, in which the control enters the drift, the diffusion, and the jump coefficients. We allow these coefficients to be random, and do not impose any L^p-bounds on the control.

We obtain a stochastic maximum principle for this model that provides both necessary and sufficient conditions of optimality. This is the first version of the stochastic maximum principle that covers the consumption–investment problem in which there are jumps in the price system.     

Beyond Wiener–Askey Expansions: Handling Arbitrary PDFs

In this paper we present a Multi-Element generalized Polynomial Chaos (ME-gPC) method to deal with stochastic inputs with arbitrary probability measures. Based on the decomposition of the random space of the stochastic inputs, we construct numerically a set of orthogonal polynomials with respect to a conditional probability density function (PDF) in each element and subsequently implement generalized Polynomial Chaos (gPC) locally. Numerical examples show that ME-gPC exhibits both p- and h-convergence for arbitrary probability measures     

A time series approach to fatigue crack propagation

The inherently nonlinear phenomenon of fatigue crack propagation is modeled as a linear random process. To a first approximation, simple, nonstationary time series models are introduced and standard techniques for determining the parameters of autoregressive integrated moving-average processes are applied. Multiplicative time series models are next utilised for the representation of a group of crack history curves. Implementation of the models on the Virkler experimental data set yields satisfactory results. Reliable Gaussian approximations to the distribution of the time required by a crack to reach a specified critical length are obtained, and the usefulness of the approach is demonstrated when updating lifetime predictions after periodic inspections.     

Mixture of probability distributions as a basic component in simulating measurement accuracy

A stochastic model is proposed for a basic mechanism in the formation of uncertainties in physical quantity measurements. __________ Translated from Izmeritel'naya Tekhnika, No. 1, pp. 12–17, January, 2006.     

Tandem queues with general blocking: A unified model and comparison results

We study tandem queues with finite intermediate buffers and develop a general blocking scheme that unifies and generalizes various blocking schemes previously studied in the literature, such as manufacturing blocking, communication blocking, kanban blocking and its variations. In the general blocking scheme, job movement and service at each stage are controlled by three parameters, which represent, respectively, the upper limits (at that stage) on work-in-process inventory, finished goods inventory, and buffer space. We derive a set of recursive equations that characterize the dynamics of the system, in terms of the job completion and departure processes, and establish comparison results for these processes in the settings of stochastic ordering, variability ordering and stochastic convexity. Free of distributional assumptions, the results provide characterization of system behavior with respect to the control parameters as well as to the arrival and service processes. We also compare system performance under two different modes of operation: make-to-order versus make-to-stock, and demonstrate the trade-off between improving service and reducing inventory. Numerical studies are also presented to illustrate the diversity of performance trade-off, including composition of inventory, offered by the general model.Supported in part by NSF under grant ECS-89-96201.