GA based neural network retraining using adaptive genetic operations

This data was imported from Scopus:

Authors: Bauer, C.I., Yu, H. and Boffey, B.

Journal: Technological Developments in Networking, Education and Automation

Pages: 511-515

ISBN: 9789048191505

DOI: 10.1007/978-90-481-9151-2-89

Within cellular systems prediction has proven to be a potential solution to enhancing the handover procedure to guarantee constant Quality of Service to mobile users. By using historical route information, the future movement of mobile devices is predicted in advance with the aim to reserve resources prior to arrival of the device in a new cell. However, as the traffic patterns of devices in this environment change over time this needs to be taken into consideration when designing a prediction system. Some mechanism has to be provided to address this issue. This paper presents a Genetic Algorithm (GA) based retraining scheme using adaptive layer-based genetic operations for a Neural Network (NN) based movement prediction systems in a cellular environment to enhance system performance in the presence of changing traffic patterns. Experimentation has shown that using an adaptive layer-based GA approach can provide a significant improvement to the predictive properties of the NN based prediction system in the presence of changing movement patterns. © Springer Science+Business Media B.V. 2010.

This source preferred by Hongnian Yu

This data was imported from Web of Science (Lite):

Authors: Bauer, C.I., Yu, H. and Boffey, B.


Pages: 511-515

ISBN: 978-90-481-9150-5

DOI: 10.1007/978-90-481-9151-2_89

The data on this page was last updated at 04:57 on May 21, 2019.