Levenberg-Marquardt Learning Algorithm for Quaternion-Valued Neural Networks

Calin-Adrian Popa

doi:10.1109/SYNASC.2016.050

2016 18th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

Levenberg-Marquardt Learning Algorithm for Quaternion-Valued Neural Networks

Year: 2016, Pages: 272-278

DOI Bookmark: 10.1109/SYNASC.2016.050

Authors

Calin-Adrian Popa, Dept. of Comput. & Software Eng., Polytech. Univ., Timisoara, Romania

Abstract

In this paper, we present the deduction of the Levenberg-Marquardt algorithm for training quaternion-valued feedforward neural networks, using the framework of the HR calculus. Its performances in the real-and complex-valued cases lead to the idea of extending it to the quaternion domain, also. The proposed method is exemplified on time series prediction applications, showing a significant improvement over the quaternion gradient descent algorithm.

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Levenberg-Marquardt Learning Algorithm for Quaternion-Valued Neural Networks

Authors

Abstract

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