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Using Neural Networks for
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Thesis
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Conclusions
Contents
Index
On the Neural Networks Tools
ChapterChapter
Subsections
Using Neural Networks for Traffic Prediction: a New Method
Introduction
Our Method
Experimental Results and Evaluation of the New Model
Real Traces for Training
Training Database Description
Experimental Tests to Identify the Best Length of Each Window
Performance of the NN to Predict Traffic
More Than One Step in the Future
Conditions to Retrain the NN
Traffic Flow Type
Comparison with Other Models
Possible Uses of our Model
Conclusion and Discussion
New Random Neural Network Training Algorithms
Introduction
Gradient Descent Training Algorithm for RNN
New Levenberg-Marquardt Training Algorithms for RNN
Analytical Formulation of the Algorithm
Different Variants of the LM Training Algorithm
New LM with Adaptive Momentum for RNN
The Algorithm with Adaptive Momentum
Performance Evaluation of the Proposed Algorithms
and
Parameters for AM-LM
Algorithms' Performance Comparison
Testing the Success Rate and the Performance
Learning Performance in the Video Quality Problem
Conclusions
General Conclusions of this Dissertation
Summary of the Main Contributions
Possible Extensions
Appendix
Samir Mohamed 2003-01-08
and 
Parameters for AM-LM