On the Root-Power Mean Aggregation Based Neuron in Quaternionic Domain

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Sushil Kumar 1,* Bipin K. Tripathi 1

1. Harcourt Butler Technical University/Department of Computer Science & Engineering, Kanpur, 208002, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.07.02

Received: 27 Jun. 2017 / Revised: 5 Aug. 2017 / Accepted: 15 Sep. 2017 / Published: 8 Jul. 2018

Index Terms

Quasi-arithmetic means, Root-power means in quaternionic domain (ℍ), Quaternionic-valued multilayer perceptron, Quaternionic-valued backpropagation, Quaternionic resilient propagation, 3D face recognition


This paper illustrates the new structure of artificial neuron based on root-power means (RPM) for quaternionic-valued signals and also presented an efficient learning process of neural networks with quaternionic-valued root-power means neurons (ℍ-RPMN). The main aim of this neuron is to present the potential capability of a nonlinear aggregation operation on the quaternionic-valued signals in neuron cell. A wide spectrum of aggregation ability of RPM in between minima and maxima has a beautiful property of changing its degree of compensation in the natural way which emulates the various existing neuron models as its special cases. Further, the quaternionic resilient propagation algorithm (ℍ-RPROP) with error-dependent weight backtracking step significantly accelerates the training speed and exhibits better approximation accuracy. The wide spectrums of benchmark problems are considered to evaluate the performance of proposed quaternionic root-power mean neuron with ℍ-RPROP learning algorithm.

Cite This Paper

Sushil Kumar, Bipin K. Tripathi, "On the Root-Power Mean Aggregation Based Neuron in Quaternionic Domain", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.7, pp.11-26, 2018. DOI:10.5815/ijisa.2018.07.02


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