Time warping recurrent neural networks and trajectory classification

The authors propose a model of a time warping recurrent neural network (TWRNN) to handle temporal pattern classification where severely time warped and deformed data may occur. This model is shown to have built-in time warping ability. The authors analyze the properties of TWRNN and show that for trajectory classification it has several advantages over such schemes as dynamic programming, hidden Markov models, time-delayed neural networks, and neural network finite automata. A numerical example of trajectory classification is presented. This problem, making a feature of variable sampling rates, having internal states, continuous dynamics, heavily time-warped data, and deformed phase space trajectories, is shown to be difficult for the other schemes. The TWRNN has learned it easily. The authors also trained it with TDNN and failed