Application of a self-organizing fuzzy neural network controller with group-based genetic algorithm to greenhouse

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.