Space or time adaptive signal processing by neural network models

1986 ◽  
Author(s):  
J. Herault ◽  
C. Jutten
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Models ◽  
Adaptive Signal Processing ◽  
Neural Network Models ◽  
Adaptive Signal

LOCALLY IMPLEMENTABLE MATRIX FLOWS AND THEIR APPLICATION TO NEURAL NETWORK LEARNING

1994 ◽  
Vol 04 (01) ◽  
pp. 23-51 ◽  
Author(s):  
JEROEN DEHAENE ◽  
JOOS VANDEWALLE
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Network Structure ◽  
Adaptive Signal Processing ◽  
External Signal ◽  
Neural Network Learning ◽  
Network Learning ◽  
Adaptive Signal

A number of matrix flows, based on isospectral and isodirectional flows, is studied and modified for the purpose of local implementability on a network structure. The flows converge to matrices with a predefined spectrum and eigenvectors which are determined by an external signal. The flows can be useful for adaptive signal processing applications and are applied to neural network learning.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

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