TY - GEN
T1 - Application of a self-organizing fuzzy neural network controller with group-based genetic algorithm to greenhouse
AU - Yao, Yuan
AU - Zhang, Kailong
AU - Zhou, Xingshe
PY - 2011
Y1 - 2011
N2 - As a complex nonlinear system, greenhouse can not be controlled perfectly by traditional control strategies. This paper proposes a self-organizing fuzzy neural network controller (SOFNNC) with group-based genetic algorithm (GGA) to drive the internal climate of the greenhouse. SOFFNNC is a hybrid control strategy which combines fuzzy control and neural network organically. It generates or prunes neurons automatically by the structure learning algorithm, which can adaptively strike a balance between the rule number and the desired performance. In other to avoid the shortage of the original learning algorithm to SOFNNC, we come up with an improved structure learning method and a new parameter learning method with GGA. Based on a greenhouse model established by an Elman neural network (ENN), we test the performance of SOFNNC. Simulation and comparison results prove that SOFNNC can achieve outstanding control effect with high efficiency.
AB - As a complex nonlinear system, greenhouse can not be controlled perfectly by traditional control strategies. This paper proposes a self-organizing fuzzy neural network controller (SOFNNC) with group-based genetic algorithm (GGA) to drive the internal climate of the greenhouse. SOFFNNC is a hybrid control strategy which combines fuzzy control and neural network organically. It generates or prunes neurons automatically by the structure learning algorithm, which can adaptively strike a balance between the rule number and the desired performance. In other to avoid the shortage of the original learning algorithm to SOFNNC, we come up with an improved structure learning method and a new parameter learning method with GGA. Based on a greenhouse model established by an Elman neural network (ENN), we test the performance of SOFNNC. Simulation and comparison results prove that SOFNNC can achieve outstanding control effect with high efficiency.
KW - EBF unit
KW - genetic algorithm
KW - parameter learning algorithm
KW - self-organizing fuzzy neural network
KW - structure learning algorithm
UR - http://www.scopus.com/inward/record.url?scp=80053393529&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2011.6022188
DO - 10.1109/ICNC.2011.6022188
M3 - 会议稿件
AN - SCOPUS:80053393529
SN - 9781424499533
T3 - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
SP - 641
EP - 648
BT - Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011
T2 - 2011 7th International Conference on Natural Computation, ICNC 2011
Y2 - 26 July 2011 through 28 July 2011
ER -
