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Prediction Horizon (prediction + horizon)

Distribution by Scientific Domains

Mathematics and Statistics	50%
Engineering	50%

Selected Abstracts

Multi-step forecasting for nonlinear models of high frequency ground ozone data: a Monte Carlo approach

ENVIRONMETRICS, Issue 4 2002
Alessandro Fassò
Abstract Multi-step prediction using high frequency environmental data is considered. The complex dynamics of ground ozone often requires models involving covariates, multiple frequency periodicities, long memory, nonlinearity and heteroscedasticity. For these reasons parametric models, which include seasonal fractionally integrated components, self-exciting threshold autoregressive components, covariates and autoregressive conditionally heteroscedastic errors with heavy tails, have been recently introduced. Here, to obtain an h step ahead forecast for these models we use a Monte Carlo approach. The performance of the forecast is evaluated on different nonlinear models comparing some statistical indices with respect to the prediction horizon. As an application of this method, the forecast precision of a 2 year hourly ozone data set coming from an air traffic pollution station located in Bergamo, Italy, is analyzed. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Explicit robust model predictive control using recursive closed-loop prediction

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 11 2006
Danlei Chu
Abstract In this paper, we develop an algorithm to compute robust MPC explicit solutions for constrained MIMO systems with internal uncertainties and external disturbances. Our approach is based on a recursive closed-loop prediction strategy to realize a finite horizon robust MPC regulator, which has the feature that only one-step state prediction is sufficient to realize robust MPC with an arbitrary prediction horizon. The paper defines a set of recursive sub-optimization problems as multiple-parametric sub-quadratic programming (mp-SQP), and shows that the optimal solution to the mp-SQP problem is piecewise affine functions of states, associated with piece objectives and state critical regions. Asymptotic closed-loop stability can be guaranteed by a terminal weighting and a terminal feedback gain; also by introducing two tuning variables, the algorithm is capable of adjusting the trade-off between system performance and robustness. The state admissible set, which is not easily derived from physical vision, is constructed by two methods: a piecewise linear norm of signals, and polyhedral Voronoi sets. Finally, two simulation examples demonstrate that the algorithm is efficient, feasible and flexible, and can be applied to both slow and fast industrial MIMO systems. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Properties of Predictors in Overdifferenced Nearly Nonstationary Autoregression

JOURNAL OF TIME SERIES ANALYSIS, Issue 1 2001
Ismael Sanchez
We analyze the effect of overdifferencing a stationary AR(p+1) process whose largest root is near unity. It is found that, if the process is nearly nonstationary, the estimators of the overdifferenced model ARIMA(p,1,0) are root- T consistent. It is also found that this misspecified ARIMA(p,1,0) has lower predictive mean squared error, to terms of small order, than the properly specified AR(p+1) model due to its parsimony. The advantage of the overdifferenced predictor depends on the remaining roots, the prediction horizon and the mean of the process. [source]

Application of two-loop robust control to air-conditioning systems,

ASIAN JOURNAL OF CONTROL, Issue 6 2009
Gongsheng Huang
Abstract This paper presents the design and application of a two-loop robust controller for temperature control in air-conditioning systems. A Takagi-Sugeno fuzzy model with uncertain local models is developed to describe the associated nonlinearities and uncertainties in the operation of the air handling units. Parallel distributed compensation is used to design the global control law. The local control law consists of two loops: an inner-loop integral controller and an outer-loop min-max predictive controller with short prediction horizon. A discounting scheme is developed to weight the contribution of the two loops. Experimental results are presented which show that the proposed strategy can achieve acceptable control performance with a minimum of onsite tuning. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]