1,720,991 research outputs found
Optimal neural feedback control applied to a problem of economic growth in freight transportation market
No full textApproximate Solution of a Class of Optimal Estimation Problems via Nonlinear Programming and Sampling Techniques
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Nonlinear model predictive control for resource allocation in the management of intermodal container terminals
No full textManagement of Water Resources Systems in the Presence of Uncertainties by Nonlinear Approximators and Deterministic Sampling
No full textTwo methods of approximate solution are developed for T -stage stochastic
optimal control (SOC) problems, aimed at obtaining finite-horizon management
policies for water resource systems. The presence of uncertainties, such as river and
rain inflows, is considered. Both approaches are based on the use of families of nonlinear
functions, called “one-hidden-layer networks” (OHL networks), made up of
linear combinations of simple basis functions containing parameters to be optimized.
The first method exploits OHL networks to obtain an accurate approximation of the
cost-to-go functions in the dynamic programming procedure for SOC problems. The
approximation capabilities of OHL networks are combined with the properties of
deterministic sampling techniques aimed at obtaining uniform samplings of highdimensional
domains. In the second method, admissible solutions to SOC problems
are constrained to take on the form of OHL networks, whose parameters are determined
in such a way to minimize the cost functional associated with SOC problems.
Exploiting these tools, the two methods are able to cope with the so-called “curse of
dimensionality,” which strongly limits the applicability of existing techniques to highdimensional
water resources management in the presence of uncertainties. The theoretical bases of the two approaches are investigated. Simulation results show that the
proposed methods are effective for water resource systems of high dimension
Approximate Dynamic Programming with Bounds on Model Complexity and Sample Complexity: An Application to an Inventory Forecasting Problem
No full textAn approximate solution to optimal Lp state estimation problems
No full textWe consider optimal estimation problems characterized by a state vector with i) dynamics described via a differential equation with Lipschitz nonlinearities, ii) partial information provided via a Lipschitz nonlinear mapping, and iii) an Lp norm measure of the estimation error to be minimized. An approximate solution of such optimal estimation problem is searched for by restricting the optimization to parameterized nonlinear approximators such as feedforward neural networks. The parameters of a feedforward neural network are the neural weights. This approach entails a constrained nonlinear programming problem, whose constraints are given by the dynamic and measurement equations, and the conditions guaranteeing the stability of the estimation error. To optimize the parameters values of neural networks an algorithm is developed that is based on appropriate sampling of the state and error spaces. Choices of the sample points are devised based on the notion of dispersion, which allow one to obtain an approximate solution of the optimal estimation problem by a small sample complexity. © 2005 AACC
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