Trial-and-Error Correlation Learning to Control an Inverted Pendulum
نویسندگان
چکیده
This paper presents a neural network approach to controlling nonlinear system. Trial-and-error correlation learning, which is a generally useful method for optimizing parameters, is applied to training a neural controller to balance an inverted pendulum. The controller is simplified by automatically pruning the hidden neurons to only two. Computer simulation shows that the trained neural controller can control the pendulum to recover quickly from a large deviation and to stabilize in a desired position with high accuracy.
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