scholarly journals Introduction and Analysis of a Method for the Investigation of QCD-Like Tree Data

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Marko Jercic ◽  
Ivan Jercic ◽  
Nikola Poljak
Keyword(s):  
Neural Network ◽  
Iterative Method ◽  
Probability Distributions ◽  
Test System ◽  
Jet Formation ◽  
Decay Probability ◽  
Decay Channels ◽  
System Of Particles ◽  
Tree Data ◽  
True System

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.

Methods for Multi-Step Time Series Forecasting Neural Networks

2011 ◽  
pp. 226-250 ◽  
Author(s):  
Douglas M. Kline
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Iterative Method ◽  
Time Series Forecasting ◽  
Independent Method ◽  
Data Series ◽  
Operating Characteristics ◽  
Quarterly Data ◽  
Joint Method

In this study, we examine two methods for Multi-Step forecasting with neural networks: the Joint Method and the Independent Method. A subset of the M-3 Competition quarterly data series is used for the study. The methods are compared to each other, to a neural network Iterative Method, and to a baseline de-trended de-seasonalized naïve forecast. The operating characteristics of the three methods are also examined. Our findings suggest that for longer forecast horizons the Joint Method performs better, while for short forecast horizons the Independent Method performs better. In addition, the Independent Method always performed at least as well as or better than the baseline naïve and neural network Iterative Methods.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

2018 ◽  
Vol 22 (3) ◽  
pp. 225-242 ◽  
Author(s):  
K. Mathan ◽  
Priyan Malarvizhi Kumar ◽  
Parthasarathy Panchatcharam ◽  
Gunasekaran Manogaran ◽  
R. Varadharajan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Heart Disease ◽  
Decision Tree ◽  
Gini Index ◽  
Mining Method ◽  
Data Mining Method ◽  
Tree Data ◽  
Neural Network Classifiers

An Iterative Method for Deciding SVM and Single Layer Neural Network Structures

2011 ◽  
Vol 33 (2) ◽  
pp. 171-186 ◽  
Author(s):  
Tarek M. Hamdani ◽  
Adel M. Alimi ◽  
Mohamed A. Khabou
Keyword(s):  
Neural Network ◽  
Iterative Method ◽  
Single Layer ◽  
Network Structures

Artificial Neural Network Application in Optimal Coordination of Directional Overcurrent Protective Relays in Electrical Mesh Distribution Network

2015 ◽  
Vol 785 ◽  
pp. 48-52 ◽  
Author(s):  
Osaji Emmanuel ◽  
Mohammad Lutfi Othman ◽  
Hashim Hizam ◽  
Muhammad Murtadha Othman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Time ◽  
Short Circuit ◽  
Distribution Networks ◽  
Test System ◽  
Wrong Response ◽  
Optimal Coordination ◽  
Artificial Neural ◽  
Short Circuit Fault

Directional Overcurrent relays (DOCR) applications in meshed distribution networks (MDN), eliminate short circuit fault current due to the topographical nature of the system. Effective and reliable coordination’s between primary and secondary relay pairs ensures effective coordination achievement. Otherwise, the risk of safety of lives and installations may be compromised alongside with system instability. This paper proposes an Artificial Neural Network (ANN) approach of optimizing the system operation response time of all DOCR within the network to address miscoordination problem due to wrong response time among adjacent DOCRs to the same fault. A modelled series of DOCRs in a simulated IEEE 8-bus test system in DigSilent Power Factory with extracted data from three phase short circuit fault analysis adapted in training a custom ANN. Hence, an improved optimized time is produced from the network output to eliminate miscoordination among the DOCRs.

scholarly journals Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering

2020 ◽  
Vol 80 (12) ◽  
Author(s):  
M. Grossi ◽  
J. Novak ◽  
B. Kerševan ◽  
D. Rebuzzi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vector Boson ◽  
Higgs Mechanism ◽  
Beyond The Standard Model ◽  
Decay Channels ◽  
Leptonic Decays ◽  
Learning Techniques ◽  
The Standard Model ◽  
Vector Boson Scattering

AbstractMeasuring longitudinally polarized vector boson scattering in $$\mathrm {WW}$$ WW channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible physics beyond the Standard Model. In order to perform such a measurement, it is crucial to develop an efficient reconstruction of the full $$\mathrm {W}$$ W boson kinematics in leptonic decays with the focus on polarization measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their abilities in reconstructing the $$\mathrm {W}$$ W boson reference frame and in consequently measuring the longitudinal fraction $$\mathrm {W}_{\text {L}}$$ W L in both semi-leptonic and fully-leptonic $$\mathrm {WW}$$ WW decay channels.

A neural network based diagnostic test system for armored vehicle shock absorbers

1996 ◽  
Vol 11 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Patrick Sincebaugh ◽  
William Green ◽  
Gerard Rinkus
Keyword(s):  
Neural Network ◽  
Diagnostic Test ◽  
Test System ◽  
Shock Absorbers ◽  
Armored Vehicle

Based on RBF Neural Network Modeling of Well Test System

2014 ◽  
Vol 535 ◽  
pp. 606-609
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Effective Means ◽  
Test System ◽  
Neural Network Modeling ◽  
Well Test ◽  
Fast Learning ◽  
The Neural Network ◽  
Neural Network System ◽  
Neural Network Toolbox

The Neural Network Toolbox in MATLAB is a powerful instrument of analyzing and designing a neural network system. RBF Neural Network has small computational burden and fast learning rate and is not liable to be trapped by local minimal points etc. So it is an effective means to identify and model a system. In this paper, the Neural Network Toolbox in MATLAB and RBF Neural Network are combined to solve the problem of modeling the pressure in oilfield test well systems and the result is excellent.

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