We address the problem of optimizing operation of a process where there are extra degrees of freedom, and where the optimum is not at some constraint. In practice, there are always unknown disturbances and model uncertainties which make this task complicated. The key idea of this paper is to turn the non-linear optimization control problem into a setpoint control problem. If this can be done, t...

An optimization-based approach to fault diagnosis for nonlinear stochastic dynamic models is developed. An optimal diagnosis problem is formulated according to a receding-horizon strategy. This approach leads to a functional optimization problem (also called ‘infinite optimization problem’), whose admissible solutions belong to a function space. As in such a context, the tools from mathematical...

In this paper, an optimal approach to design wideband tapped-delay line (TDL) array antenna is proposed. This approach lets us control the array angular and frequency response over a wide frequency band. To this end, some design restrictions are defined and a multi-objective optimization problem is constructed by putting the individual restrictions together. The optimal weights of the TDL proce...

This paper describes modeling and on-line optimization of a crude unit heat exchanger network at the Statoil Mongstad re nery. The objective is to minimize the energy input in the gas red heater by optimally distributing the cold crude oil in the heat exchanger network. The steady state mass and energy balance of the 20 heat exchangers in the network yields the process model. This model is tted...

Most neural network models base their ability to adapt to problems on changing their interconnec-tion strengths according to a learning algorithm. Evolutionary technics and a special class of learning algorithms enable a neural network to have a dynamic structure too. While in the rst case we obtain an optimized a-priori architecture the latter allows on-line adaptation. However, most of those ...

By now Artificial Intelligence (AI), Theoretical Computer Science (CS theory) and Operations Research (OR) have investigated a variety of search and optimization problems. However, methods from these scientific areas use different problem descriptions, models, and tools. They also address problems with particular efficiency requirements. For example, approaches from CS theory are mainly concern...

In this work we derive a new on-line parametric model for time series forecasting based on Vapnik-Chervonenkis (VC) theory. Using the strong connection between support vector machines (SVM) and Regularization theory (RT), we propose a regularization operator in order to obtain a suitable expansion of radial basis functions (RBFs) with the corresponding expressions for updating neural parameters...

Multi-dimensional expressions (MDX) provide an interface for asking several related OLAP queries simultaneously. An interesting problem is how to optimize the execution of an MDX query, given that most data warehouses maintain a set of redundant materialized views to accelerate OLAP operations. A number of greedy and approximation algorithms have been proposed for different versions of the prob...