Search International and National Patent Collections

1. (WO2018146514) GENERALIZED OPERATIONAL PERCEPTRONS: NEWGENERATION ARTIFICIAL NEURAL NETWORKS

Pub. No.:    WO/2018/146514    International Application No.:    PCT/IB2017/050658
Publication Date: Fri Aug 17 01:59:59 CEST 2018 International Filing Date: Wed Feb 08 00:59:59 CET 2017
IPC: G06F 17/28
G06N 3/08
H04B 1/40
H04B 15/00
Applicants: QATAR UNIVERSITY
Inventors: KIRANYAZ, Serkan
INCE, Turker
GABBOUJ, Moncef
IOSIFIDIS, Alexandros
Title: GENERALIZED OPERATIONAL PERCEPTRONS: NEWGENERATION ARTIFICIAL NEURAL NETWORKS
Abstract:
Certain embodiments may generally relate to various techniques for machine learning. Feed-forward, fully-connected Artificial Neural Networks (ANNs), or the so-called Multi- Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance may vary significantly depending on the function or the solution space that they attempt to approximate for learning. This is because they are based on a loose and crude model of the biological neurons promising only a linear transformation followed by a nonlinear activation function. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. In order to address this drawback and also to accomplish a more generalized model of biological neurons and learning systems, Generalized Operational Perceptrons (GOPs) may be formed and they may encapsulate many linear and nonlinear operators.