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• W2243974938 abstract "An adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. Another key feature of this structure is the fact that, it does not need model of the system. In this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  Online training of neurolinearizer is compared to model predictive recurrent training. Relationships between this controller and neural network based model reference adaptive controller are established. A CSTR reactor and pH control in a neutralization process illustrate performance of this method. Simulation studies show a superior performance with respect to a PI controller." @default.
• W2243974938 created "2016-06-24" @default.
• W2243974938 creator A5001722063 @default.
• W2243974938 creator A5020546325 @default.
• W2243974938 date "2009-06-01" @default.
• W2243974938 modified "2023-09-25" @default.
• W2243974938 title "Real-Time Output Feedback Neurolinearization" @default.
• W2243974938 doi "https://doi.org/10.30492/ijcce.2009.13400" @default.
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