Abstract
In fuzzy modeling, the most important problem is how to divide the input space. Several methods have been proposed. For example, methods to incorporate variables sequentially, methods based on clusters, or stochastic searching methods using genetic algorithm are the notable ones. In this paper, we propose a genetic algorithm-based machine learning method for emergent generation of necessary rules. The action part is the linear model, and with small number of rules, the nonlinearity is to be achieved by the mixture of fuzzy rules.Moreover, the actual data of a plant factory was used for the modeling experiment. A plant factory is made of translucent materials and equipped with shading and supplemental light. The objective of the plant is to realize an optimal environment for vegetables to grow. The outside solar radiation, the status of the control equipments and optimal environment for vegetables to grow. The outside solar radiation, the status of the control equipments and the information of the sun locations are used as the input of the model, and the illuminance inside the house are taken as the output. A good model was derived using our method.
