title: Neural network learning without backpropagation.
abstract: The method introduced in this paper allows for training arbitrarily connected neural networks, therefore, more powerful neural network architectures with connections across layers can be efficiently trained. The proposed method also simplifies neural network training, by using the forward-only computation instead of the traditionally used forward and backward computation.
authors.affiliation: Department of Electrical and Computer Engineering, Auburn University, Auburn, AL 36849-5201 USA. wilam@ieee.org
authors.forename: Bogdan M
authors.initials: BM
authors.lastname: Wilamowski
authors.forename: Hao
authors.initials: H
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D009474:Neurons; D012985:Software Design; D013997:Time Factors