Application of a Self-Organizing Fuzzy Neural Network Controller with Group-Based Genetic Algorithm to Greenhouse

摘要：

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 groupbased 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 El man neural network (ENN), we test the performance of SOFNNC. Simulation and comparison results prove that SOFNNC can achieve outstanding control effect with high efficiency.

关键词： self-organizing fuzzy neural network EBF unit genetic algorithm structure learning algorithm parameter learning algorithm

作者: Yuan YAO Kailong ZHANG Xingshe ZHOU

作者单位: Shaanxi Provincial Key Embedded System Technology Laboratory, School of Computer Northwestern Polytechnical University Xian, China

会议类型: 国际会议

会议名称: 2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

会议地点: 上海

会议语种:英文

页码: 660-667

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）