# A Novel Fractional Gradient-Based Learning Algorithm for Recurrent Neural Networks

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## Abstract

In this research, we propose a novel algorithm for learning of the recurrent neural networks called as the fractional back-propagation through time (FBPTT). Considering the potential of the fractional calculus, we propose to use the fractional calculus-based gradient descent method to derive the FBPTT algorithm. The proposed FBPTT method is shown to outperform the conventional back-propagation through time algorithm on three major problems of estimation namely nonlinear system identification, pattern classification and Mackey–Glass chaotic time series prediction.

## Keywords

Back-propagation through time (BPTT) Recurrent neural network (RNN) Gradient descent Fractional calculus Mackey–Glass chaotic time series Minimum redundancy and maximum relevance (mRMR)## References

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