title: On efficient learning machine with root-power mean neuron in complex domain.
abstract: This paper describes an artificial neuron structure and an efficient learning procedure in the complex domain. This artificial neuron aims at incorporating an improved aggregation operation on the complex-valued signals. The aggregation operation is based on the idea underlying the weighted root-power mean of input signals. This aggregation operation allows modeling the degree of compensation in a natural manner and includes various aggregation operations as its special cases. The complex resilient propagation algorithm ([Formula: see text]-RPROP) with error-dependent weight backtracking step accelerates the training speed significantly and provides better approximation accuracy. Finally, performance evaluation of the proposed complex root-power mean neuron with the [Formula: see text]-RPROP learning algorithm on various typical examples is given to understand the motivation.
authors: Computational Neuroscience Research Group, Indian Institute of Technology, Kanpur, India. abkt.iitk@gmail.com
authors: Bipin Kumar
authors: BK
authors: Tripathi
authors: Prem Kumar
authors: PK
authors: Kalra
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008432:Mathematical Computing; D055641:Mathematical Concepts; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D012985:Software Design