IJISA Vol. 10, No. 7, 8 Jul. 2018

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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.

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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