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|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|
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