ATM Traffic Prediction Using Artificial Neural Networks and Wavelet Transforms
ICN '01 Proceedings of the First International Conference on Networking-Part 2
Dynamic bandwidth allocation based on online traffic prediction for real-time MPEG-4 video streams
EURASIP Journal on Applied Signal Processing
Oil Price Forecasting with an EMD-Based Multiscale Neural Network Learning Paradigm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Multi-resolution learning for knowledge transfer
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Multiscale bilinear recurrent neural network with an adaptive learning algorithm
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Real-time network traffic prediction based on a multiscale decomposition
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
A novel method of network burst traffic real-time prediction based on decomposition
ICN'05 Proceedings of the 4th international conference on Networking - Volume Part I
Information Sciences: an International Journal
| Hi-index | 35.68 |
Current neural network learning processes, regardless of the learning algorithm and preprocessing used, are sometimes inadequate for difficult problems. We present a new learning concept and paradigm for neural networks, called multiresolution learning, based on multiresolution analysis in wavelet theory. The multiresolution learning paradigm can significantly improve the generalization performance of neural networks