Energy Recovery of Dead Calorimeter Channels using Artificial Neural Networks (ANN)

Nov 21, 2014

20 likes | 131 Views

Energy Recovery of Dead Calorimeter Channels using Artificial Neural Networks (ANN). Stilianos Kesisoglou Victoria Giakoumopoulou Georgios Daskalakis. ECAL DPG Meeting Institute of Nuclear Physics National Center for Scientific Research “Demokritos” December 20, 2012.

Share Presentation

Embed Code

Link

institute
energy recovery
december 20
scientific research demokritos
nuclear physics national center
artificial neural networks ann

cameron-sellers

Download Presentation

Energy Recovery of Dead Calorimeter Channels using Artificial Neural Networks (ANN)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Energy Recovery of Dead Calorimeter Channelsusing Artificial Neural Networks (ANN) Stilianos KesisoglouVictoria GiakoumopoulouGeorgios Daskalakis ECAL DPG Meeting Institute of Nuclear Physics National Center for Scientific Research “Demokritos” December 20, 2012

Artificial Neural Networks

Articifial Intelligence. Artificial Neural Networks. Brian Talecki CSC 8520 Villanova University. ANN - Artificial Neural Network. A set of algebraic equations and functions which determine the best output given a set of inputs.

1.02k views • 41 slides

Artificial Neural Networks

Artificial Neural Networks. Dan Simon Cleveland State University. Neural Networks. Artificial Neural Network (ANN) : An information processing paradigm that is inspired by biological neurons

2.07k views • 44 slides

Artificial Neural Networks

Artificial Neural Networks. Torsten Reil torsten.reil@zoo.ox.ac.uk. Outline. What are Neural Networks? Biological Neural Networks ANN – The basics Feed forward net Training Example – Voice recognition Applications – Feed forward nets Recurrency Elman nets Hopfield nets

739 views • 47 slides

Chapter 3 ARTIFICIAL NEURAL NETWORKS

2. Outline. 1. Introduction 2. ANN representations3. Perceptron Training4. Multilayer networks and Backpropagation algorithm5. Remarks on the Backpropagation algorithm6. Neural network application development7. Benefits and limitations of ANN8. ANN Applications. 3. INTRODUCTION Biological Motivation.

911 views • 71 slides

Artificial Neural Networks

Artificial Neural Networks. Introduction. Artificial Neural Networks (ANN) Information processing paradigm inspired by biological nervous systems ANN is composed of a system of neurons connected by synapses ANN learn by example Adjust synaptic connections between neurons. History.

1.32k views • 82 slides

Artificial Neural Networks

Artificial Neural Networks. Rohit Ray ESE 251. What are Artificial Neural Networks?. ANN are inspired by models of the biological nervous systems such as the brain Novel structure by which to process information

464 views • 16 slides

Artificial Neural Networks

Artificial Neural Networks . R. Michael Winters Music Information Retrieval Dr. Ichiro Fujinaga February 28, 2012. Outline. A brief history of ANN Quick facts about biological n eural networks Simple explanation of mathematical implementation Detailed mathematical implementation

265 views • 14 slides

Artificial Neural Networks

Artificial Neural Networks. Biointelligence Laboratory Department of Computer Engineering Seoul National University. Contents. Introduction Perceptron and Gradient Descent Algorithm Multilayer Neural Networks Designing an ANN for Face Recognition Application. Introduction.

934 views • 64 slides

Artificial Neural Networks : An Introduction

Artificial Neural Networks : An Introduction. G.Anuradha. Learning Objectives. Fundamentals of ANN Comparison between biological neuron and artificial neuron Basic models of ANN Different types of connections of NN, Learning and activation function

996 views • 76 slides

Energy Recovery of Dead Calorimeter Channels using Artificial Neural Networks (ANN)

521 views • 35 slides

Artificial Neural Networks (ANN)

Artificial Neural Networks (ANN). Artificial Neural Networks (ANN). Output Y is 1 if at least two of the three inputs are equal to 1. Artificial Neural Networks (ANN). Artificial Neural Networks (ANN). Model is an assembly of inter-connected nodes and weighted links

2.85k views • 10 slides

INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS (ANN)

INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS (ANN). Mohammed Shbier. Outline. Definition, why and how are neural networks being used in solving problems. Human biological neuron. Artificial Neuron. Applications of ANN. Comparison of ANN vs conventional AI methods.

4.64k views • 42 slides

Artificial Neural Networks (ANN)

CES 514 Lec 11 April 28,2010 Neural Network, case study of naïve Bayes and decision tree, text classification. Artificial Neural Networks (ANN). Output Y is 1 if at least two of the three inputs are equal to 1. Neural Network with one neuron.

824 views • 59 slides

Chapter 3 ARTIFICIAL NEURAL NETWORKS

Chapter 3 ARTIFICIAL NEURAL NETWORKS. HCMC University of Technology Sep. 2008. Outline. 1. Introduction 2. ANN representations 3. Perceptron Training 4. Multilayer networks and Backpropagation algorithm 5. Remarks on the Backpropagation algorithm

996 views • 71 slides

Artificial Neural Networks : An Introduction

Artificial Neural Networks : An Introduction. G.Anuradha. Learning Objectives. Reasons to study neural computation Comparison between biological neuron and artificial neuron Basic models of ANN Different types of connections of NN, Learning and activation function

1.11k views • 81 slides

Artificial Neural Networks - Introduction -

Artificial Neural Networks - Introduction -. Overview. Biological inspiration Artificial neurons and neural networks Why use ANN? ANN Characterization. Biological inspiration.

465 views • 28 slides

Artificial Neural Networks (ANN)

Artificial Neural Networks (ANN). Artificial Neural Networks. First proposed in 1940s as an attempt to simulate the human brain’s cognitive learning processes. They have ability to model complex, yet poorly understood problems.

218 views • 16 slides

Artificial Neural Networks

751 views • 64 slides

Artificial Neural Networks (ANN)

637 views • 59 slides

Artificial Neural Networks

Artificial Neural Networks. 자연언어처리연구실 황규백. Artificial neural network(ANN) General, practical method for learning real-valued, discrete-valued, vector-valued functions from examples BACPROPAGATION 알고리즘

533 views • 44 slides

Artificial Neural Networks

Artificial Neural Networks. Overview. Introduction ANN representations Perceptron Training Gradient Descent and Delta Rule Multilayer networks and Backpropagation algorithm Remarks on the backpropagation algorithm An illustrative example: face recognition

454 views • 28 slides

More Related