Adaptive Network Traffic Prediction Algorithm based on BP Neural Network

다국어 초록 (Multilingual Abstract)

With the rapid development of Internet technology, the network now has a large size and high complexity, and consequently the network management is becoming increasing difficult and complexity, so traffic forecast play a more and more role in network management. With a large amount of real traffic data collected from the actual network, an adaptive network traffic prediction algorithm based on BP neural network was proposed in this paper, it use an adaptive learning rate method to adjust the learning rate according to total error changing trend of decreased or increased and the difference of changing; and then it corrects the weights in each layers according to forward and reverse calculation. Simulation results show that, compared with the traditional BP neural network, our algorithm has better performance in the prediction results, and has smaller error.

목차 (Table of Contents)

• Abstract
• 1. Introduction
• 2. Related Work
• 3. Adaptive Network Traffic Prediction Method based on BP Neural Network
• 3.1. Network Traffic Measurement Method
• 3.2. Back-Propagation (BP) Neural Network Model
• 3.3. Training Algorithm of Weights in Layers
• 3.4. Improved BP Algorithm
• 4. Simulation Results
• 4.1. Performance Computation
• 4.2. Computation of Prediction Value with Actual Value
• Acknowledgements
• References