Input selection for learning human control strategy

In this paper, we study the input selection in reducing the problem of the high dimension of input variables severely affecting the learning control performance of artificial neural networks. We first locally transform a nonlinear mapping problem into a nearly linear one by using the first-order derivatives of it. Then, we performed a local measure of the sensitivity of each of the model inputs (state variables) with respect to model outputs (human control inputs) under the least square error standard. Finally, based on voting, we defined a determination-rule to decide the importance order of the system state variables globally. By abstracting a human expert skill for controlling a dynamically stabilized robot: Gyrover, we validated the proposed approach.