Sequential decision problems can often be modeled as Markov decision processes. Classical solution approaches assume that the parameters of the model are known. However, model parameters are usually estimated and uncertain in practice. As a result, managers are often interested in how estimation errors affect the optimal solution. In this paper we illustrate how sensitivity analysis can be perf...

It is commonly the case in biochemical modelling that we have knowledge of the qualitative 'structure' of a model and some measurements of the time series of the variables of interest (concentrations and fluxes), but little or no knowledge of the model's parameters. This is, then, a system identification problem, that is commonly addressed by running a model with estimated parameters and assess...

Abstract This article deals with anti-synchronization between different hyperchaotic systems such as Lu system and Newton-Leipnik system; and Newton-Leipnik system and Rossler system using adaptive control method. Based on Lyapunov stability theory, the anti synchronization between a pair of hyperchaotic systems with fully unknown parameters are derived. An adaptive control law and a parameter ...

Active Systems with Uncertain Parameters: an Information-Theoretic Perspective by Gireeja Vishnu Ranade Doctor of Philosophy in Electrical Engineering and Computer Sciences University of California, Berkeley Professor Anant Sahai, Chair The sister fields of control and communication share many common goals. While communication aims to reduce uncertainty by communicating information about the st...

In this paper, the robust adaptive finite-time synchronization problem for nonlinear resource management system with uncertain parameters is discussed. By incorporating the finite-time stability theory and adaptive control approach, a novel and more general adaptive finite-time synchronization control scheme is proposed. The developed result depends on the terminal attractors, which can not onl...

For modeling decentralized decision-making problems with uncertain random parameters, an uncertain random multilevel programming is proposed. For some special case, an equivalent crisp mathematical programming to the established uncertain random programming is presented. A searching method by integrating uncertain random simulations, neural network and genetic algorithm is produced to search th...

the hub location decision is a long term investment and any changes in it take considerable time and money. in real situations, some parameters are uncertain hence, deterministic models cannot be more efficient. the ability of two-stage stochastic programming is to make a long-term decision by considering effects of it in short term decisions simultaneously. in the two-stage stochastic programm...