Neural network control of an unstable process

We explore the application of neural network methods to the development of controllers for unstable and highly nonlinear systems using the bioreactor benchmark problem as a concrete example. We demonstrate that the dynamic gradient method leads to controllers that are effective for the nominal plant and robust to reasonable changes of plant parameters.<
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