Add to ORKGThe application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the use of feed-forward nets for event classification and function approximation. This network type is best suited for a hardware implementation and special VLSI chips are available which are used in fast trigger processors. Also discussed are fully connected networks of the Hopfield type for pattern recognition in tracking detectors. (orig.)14 refs.Available from TIB Hannover: RA 2999(95-061) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Forschung und Technologie (BMFT), Bonn (Germany)DEGerman
Pattern recognition as used in triggers for large particle physics experiments should be at the same...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and for...
The application of artificial neural networks in particle physics is reviewed. The use of feed-forwa...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the u...
Over the past years, AI methods have gained much interest in particle physics experiments, concernin...
During the past years artificial neural networks (ANN) have gained increasing interest not only in t...
Feed-Forward Neural Networks are nowadays a standard tool in the toolbox of high energy physicists. ...
A summary of neural network applications in high energy physics over the past ten years is given, in...
This course presents an overview of the concepts of the neural networks and their aplication in...
Feed forward and recurrent neural networks are introduced and related to standard data analysis tool...
After their inception in the 1940s and several decades of moderate success, artificial neural networ...
This book discusses neural computation, a network or circuit of biological neurons and relatedly, pa...
This thesis describes the application of an Artificial Neural Network classifier to identify the par...
We describe some of the challenges of particle accelerator control, highlight recent advances in neu...
Pattern recognition as used in triggers for large particle physics experiments should be at the same...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and for...
The application of artificial neural networks in particle physics is reviewed. The use of feed-forwa...
The application of Artificial Neural Networks in Particle Physics is reviewed. Most common is the u...
Over the past years, AI methods have gained much interest in particle physics experiments, concernin...
During the past years artificial neural networks (ANN) have gained increasing interest not only in t...
Feed-Forward Neural Networks are nowadays a standard tool in the toolbox of high energy physicists. ...
A summary of neural network applications in high energy physics over the past ten years is given, in...
This course presents an overview of the concepts of the neural networks and their aplication in...
Feed forward and recurrent neural networks are introduced and related to standard data analysis tool...
After their inception in the 1940s and several decades of moderate success, artificial neural networ...
This book discusses neural computation, a network or circuit of biological neurons and relatedly, pa...
This thesis describes the application of an Artificial Neural Network classifier to identify the par...
We describe some of the challenges of particle accelerator control, highlight recent advances in neu...
Pattern recognition as used in triggers for large particle physics experiments should be at the same...
ABSTRACT In a test setup, a hardware neural network determined track parameters of charged particles...
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and for...