Scaled Conjugate Gradient Learning for Quaternion-Valued Neural Networks

This paper presents the deduction of the scaled conjugate gradient method for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. The performances of the scaled conjugate algorithm in the real- and complex-valued cases lead to the idea of extending it to the quaternion domain, also. Experiments done using the proposed training method on time series prediction applications showed a significant performance improvement over the quaternion gradient descent and quaternion conjugate gradient algorithms.