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In this paper a new approach to fuzzy iterative learning control is presented. In the proposed approach, coefficients of fuzzy system and learning rate of ILC are calculated using optimization algorithms such as steepest descent and genetic algorithm. The optimization algorithm must be applied to a predetermined number of iterations to determine unknown coefficients, offline. Calculating fuzzy coefficients...
The application of a recursive technique, the En-semble Kalman filter, to history matching problem has been considered. An algorithm is simplified in a way of reducing the order of the state vector by using Singular Value Decomposition (SVD). The state vector of the reservoir model is truncated to a few numbers of states corresponding to the largest singular values. The energy of the system is preserved...
The application of a recursive technique, the Ensemble Kalman filter, to history matching problem has been considered. An algorithm is simplified in a way of reducing the order of the state vector by using Singular Value Decomposition (SVD). The state vector of the reservoir model is truncated to a few numbers of states corresponding to the largest singular values. The energy of the system is preserved...
In this paper a new approach to fuzzy iterative learning control is presented. The method keeps the advantage of ILC while create appropriate updating law by using fuzzy TSK system that can deal with nonlinearities and uncertainties. Finally coefficients of fuzzy system and learning rate of ILC are calculated by using optimization algorithms such as steepest descent and genetic algorithm. The overall...
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