Quasi-newton learning methods for quaternion-valued neural networks

Abstract

This paper presents the deduction of the quasi-Newton learning methods for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. Since these algorithms yielded better training results than the gradient descent for the real- and complex-valued cases, an extension to the quaternion-valued case is a natural idea to enhance the performance of quaternion-valued neural networks. Experiments done on four time series prediction applications show a significant improvement over the quaternion gradient descent algorithm.