In this paper, we consider the problem of construtcing reduced complexity controllers for output feedback nonlinear H1 control. We give su cient conditions, under which the controllers so obtained, guarantee asymptotic stability of the closed-loop system when there are no exogenous inputs. The controllers obtained are non-optimal in general. However, in case optimality holds, we show that these...

A minimum principle for stochastic control problems with output feedback is derived by applying Bismut’s minimum principle for stochastic control problems with full information about the past to the Kushner-Stratonovitch equation describing the controlled evolution of the conditional density of the state. The well-known solution of the linear-quadratic Gaussian problem is obtained from the prin...

In this paper, using Poisson stochastic processes, we introduce an approach to the problem of the approximate join operation in random databases. It is shown that the cardinalities of the result sets obtained from an approximate join operation follow this type of stochastic process. Initially, we treat the case of the approximate join between two random tables, which is accomplished using a hom...

in the present article, we focus on the numerical approximation of stochastic partial differential equations of itˆo type with space-time white noise process, in particular, parabolic equations. for each case of additive andmultiplicative noise, the numerical solution of stochastic diffusion equations is approximated using two stochastic finite difference schemes and the stability and consisten...

In this paper a novel approach for output-feedback model predictive control of discrete-time linear stochastic systems is presented, allowing for the presence of unbounded noise. The satisfaction of probabilistic constraints and the stability properties of the method are guaranteed by imposing suitable constraints on the mean and the variance of the state variable. A simulation example is prese...

We extend previous constructions of probabilities for a prime event structure E by allowing arbitrary confusion. Our study builds on results related to fairness in event structures that are of interest per se. Executions of E are captured by the set Ω of maximal configurations. We show that the information collected by observing only fair executions of E is confined in some σ-algebra F0, contai...

Abstract : In this paper , we apply the stochastic dynamic programming to approximate the mean project completion time in dynamic Markov PERT networks. It is assumed that the activity durations are independent random variables with exponential distributions, but some social and economical problems influence the mean of activity durations. It is also assumed that the social problems evolve in ac...

Finding optimal path in a given network is an important content of intelligent transportation information service. Static shortest path has been studied widely and many efficient searching methods have been developed, for example Dijkstra’s algorithm, Floyd-Warshall, Bellman-Ford, A et al. However, practical travel time is not a constant value but a stochastic value. How to take full use of the...

In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...