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

• DOI: 10.1007/s00034-017-0572-z
• Publication Date: 2017-05-22T08:25:24Z
• Authors: Shujaat Khan; Jawwad Ahmad; Imran Naseem; Muhammad Moinuddin
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

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.