Fast temporal neural learning using teacher forcing

A methodology for faster supervised temporal learning in nonlinear neural networks is presented. The authors introduce the concept of terminal teacher forcing and appropriately modify the activation dynamics of the neural network. They also indicate how teacher forcing can be decreased as the learning proceeds. In order to make the algorithm more tangible, the authors compare its different phases to an important aspect of learning inspired by a real-life analogy. The results show that the learning time is reduced by one to two orders of magnitude with respect to conventional methods. The authors limited themselves to an example of representative complexity. It is demonstrated that a circular trajectory can be learned in about 400 iterations.<>