Learning control with neural networks

A neural control model based on learning of the system inverse is proposed. Learning control is a control method wherein experience gained from previous performance is automatically used to improve future performance. A learning scheme called the inverse transfer learning scheme is introduced. Compared to previous learning schemes, this scheme provides faster convergences to the minimum error state and reflects properties of highly coupled nonlinear dynamic systems. The scheme is applied to the pole-balancing control problem through computer simulation to demonstrate control capability.<
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