Decomposition of a Greenhouse Fuzzy Model

This paper describes the identification of greenhouse climate processes with multiple fuzzy models by resulting of decomposition of one global (flat) fuzzy model. This process is called separation of linguistic information methodology - SLIM. In this paper, the SLIM methodology is based on fuzzy clustering of fuzzy rules algorithm (FCFRA), which is a generalization of the well-known fuzzy c-means. It allows the automatic organization of the sets of fuzzy IF ... THEN rules of one fuzzy system into a multimodel hierarchical structure, result of clustering process of fuzzy rules. This technique is used to organize the fuzzy greenhouse climate model into a new structure more interpretable, as in the case of the physical model. This new methodology was tested to split the inside greenhouse air temperature and humidity flat fuzzy models into fuzzy sub-models.

1. Introduction

The greenhouse climate model describes the dependence of air temperature, humidity and CO₂ concentration inside the greenhouse on the outside weather conditions and the control equipment using a set of nonlinear differential equations of first order. These equations are formulated as a result of energy and mass balance of many physical and biological processes.