Work place: Harcourt Butler Technical University/Department of Computer Science & Engineering, Kanpur, 208002, India
Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Vision, Computer Networks
Sushil Kumar is pursuing his PhD in Computational Intelligence from HBTU Kanpur, India and completed M. Tech in Modelling and Simulation from DIAT-DRDO Pune, India. He is currently research scholar in Department of Computer Science and Engineering of HBTU Kanpur, India. He is associated with the Nature-inspired Computational Intelligence Research Group (NCIRG) at HBTU. His areas of research include high-dimensional neurocomputing, computational intelligence, machine learning and computer vision focused on biometrics and 3D Imaging. He has published several research papers in these areas.
DOI: https://doi.org/10.5815/ijisa.2018.07.02, Pub. Date: 8 Jul. 2018
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.[...] Read more.
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