Parameterized neurocontrollers

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364158, 364162, 395 21, G05D 1302

Patent

active

054869960

ABSTRACT:
A controller based on a neural network whose output is responsive to input signals that represent user or designer defined control system parameters which may include process parameters, control parameters and/or disturbance parameters. The neural network can be "trained" to mimic an existing controller which may or not receive inputs of control system parameters. The trained neural network controller may have advantages of faster execution and reduced code size. The neural network can also be trained to result in a nonlinear controller that is more powerful than an existing controller.

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J. J. Helferty et al., "Experiments in Adaptive Control Using Artificial Neural Networks", Aug., 1990, IEEE International Conference on Systems Engineering, in Pittsburgh, Pa., pp. 339-342.
C.-C. Ku et al., "System Identification and Control Using Diagonal Recurent Neural Networks", Jun., 1992, 1992 American Control Conference, at Chicago, Ill., vol. 2, pp. 545-549.
H-T. Su et al., "Control and System Identification Using Elements of Neural Network Computation Engineering", Jun., 1992, 1992 American Control Conference, at Chicago, Ill., vol. 1, pp. 485-489.

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