On the root-power mean aggregation based neuron in quaternionic domain

On the root-power mean aggregation based neuron in quaternionic domain

Автор: Sushil Kumar, Bipin K. Tripathi

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 7 vol.10, 2018 года.

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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.

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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

Короткий адрес: https://sciup.org/15016504

IDR: 15016504   |   DOI: 10.5815/ijisa.2018.07.02

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