Time Warping Invariant Neural Networks

Although TWINN is a simple modifica(cid:173) tion of well known recurrent neural network, analysis has shown that TWINN com(cid:173) pletely removes time warping and is able to handle difficult classification problem. This may help to understand the well accepted fact that for learning grammatical reference with NNF A one had to start with very short strings in training set. The numerical example we used is a trajectory classification problem. With TWINN this problem has been learned in 100 iterations. For benchmark we also trained the exact same problem with TDNN and completely failed as expected.