International Journal of Intelligent Systems and Applications(IJISA)
ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)
Published By: MECS Press
IJISA Vol.10, No.7, Jul. 2018
On the Root-Power Mean Aggregation Based Neuron in Quaternionic Domain
Full Text (PDF, 1159KB), PP.11-26
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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