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This work is a survey of the average cost control problem for discrete-time Markov processes. The authors have attempted to put together a comprehensive account of the considerable research on this problem over the past three decades. The exposition ranges from finite to Borel state and action spaces and includes a variety of methodologies to find and(More)
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at only <i>two</i> settings of the <i>N</i>-dimensional parameter vector being optimized rather than at the(More)
We introduce a new randomized method called Model Reference Adaptive Search (MRAS) for solving global optimization problems. The method works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the(More)
This thesis presents a prototype vehicle acoustic signal classification system with low classification error and short processing delay. To analyze the spectrum of the vehicle acoustic signal, we adopt biologically motivated feature extraction models – cochlear filter and A1-cortical wavelet transform. The multi-resolution representation obtained from these(More)
Most discrete event system models are based on deening the alphabet set or the set of events as a fundamental concept. In this paper, we take an alternative view of treating the state space as the fundamental concept. We approach the problem of controlling discrete event systems by using predicates and predicate transformers. Predicates have the advantage(More)
applies and teaches advanced methodologies of design and analysis to solve complex, hierarchical, heterogeneous and dynamic problems of engineering technology and systems for industry and government. Abstract This paper proposes a simple analytical model called M timescale Markov Decision Process (MMDP) for hierarchically structured sequential decision(More)
In this paper, we consider Simultaneous Perturbation Stochastic Approximation (SPSA) for function minimization. The standard assumption for convergence is that the function be three times differentiable, although weaker assumptions have been used for special cases. However, all work that we are aware of at least requires differentiability. In this paper, we(More)