Opposite Transfer Functions and Backpropagation Through Time

Backpropagation through time is a very popular discrete-time recurrent neural network training algorithm. However, the computational time associated with the learning process to achieve high accuracy is high. While many approaches have been proposed that alter the learning algorithm, this paper presents a computationally inexpensive method based on the concept of opposite transfer functions to improve learning in the backpropagation through time algorithm. Specifically, we will show an improvement in the accuracy, stability as well as an acceleration in learning time. We will utilize three common benchmarks to provide experimental evidence of the improvements