Please use this identifier to cite or link to this item:
|Title:||Multi-objective evolutionary recurrent neural networks for system identification|
|Citation:||Ang, J.H., Goh, C.K., Teoh, E.J., Mamun, A.A. (2007). Multi-objective evolutionary recurrent neural networks for system identification. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 1586-1592. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2007.4424662|
|Abstract:||This paper proposes a new multi-objective evolutionary approach for training recurrent neural networks (RNNs). The algorithm uses features of a variable length representation allowing easy adaptation of neural networks structures and a micro genetic algorithm (μGA) with an adaptive local search intensity scheme for local fine-tuning. In addition, a structural mutation (SM) operator for evolving the appropriate number of neurons for RNNs is used. Simulation results demonstrated the effectiveness of proposed method for system identification tasks. © 2007 IEEE.|
|Source Title:||2007 IEEE Congress on Evolutionary Computation, CEC 2007|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 11, 2018
WEB OF SCIENCETM
checked on Nov 26, 2018
checked on Nov 24, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.