TY - GEN
T1 - A hybrid genetic algorithm for designing feedforward neural networks
AU - Jinhua, Xu
AU - Yue, Lu
PY - 2008
Y1 - 2008
N2 - In this paper, a hybrid algorithm is proposed for designing feedforward neural networks, A genetic algorithm is proposed to tune the connections and parameters between the input layer and the hidden layer, and orthogonal transformation is applied to tune the connections and parameters between the hidden layer and the output layer. The crossover operator and mutation operator are based on the singular value decomposition of the outputs of the hidden nodes. Using the proposed algorithm, both the structure and parameters of a neural network can be optimized efficiently. Simulations are presented to demonstrate the effectiveness of the proposed approach.
AB - In this paper, a hybrid algorithm is proposed for designing feedforward neural networks, A genetic algorithm is proposed to tune the connections and parameters between the input layer and the hidden layer, and orthogonal transformation is applied to tune the connections and parameters between the hidden layer and the output layer. The crossover operator and mutation operator are based on the singular value decomposition of the outputs of the hidden nodes. Using the proposed algorithm, both the structure and parameters of a neural network can be optimized efficiently. Simulations are presented to demonstrate the effectiveness of the proposed approach.
UR - https://www.scopus.com/pages/publications/60349110109
U2 - 10.1109/ISKE.2008.4730992
DO - 10.1109/ISKE.2008.4730992
M3 - 会议稿件
AN - SCOPUS:60349110109
SN - 9781424421978
T3 - Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008
SP - 549
EP - 554
BT - Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008
T2 - Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering, ISKE 2008
Y2 - 17 November 2008 through 19 November 2008
ER -