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Decomposition of a Greenhouse Fuzzy Model | IEEE Conference Publication | IEEE Xplore

Decomposition of a Greenhouse Fuzzy Model


Abstract:

This paper describes the identification of greenhouse climate processes with multiple fuzzy models by resulting of decomposition of one global (flat) fuzzy model. This pr...Show More

Abstract:

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.
Date of Conference: 20-22 September 2006
Date Added to IEEE Xplore: 07 May 2007
Print ISBN:0-7803-9758-4

ISSN Information:

Conference Location: Prague, Czech Republic
Citations are not available for this document.

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.

Cites in Papers - |

Cites in Papers - Other Publishers (2)

1.
Sebastian-Camilo Vanegas-Ayala, Julio Barón-Velandia, Daniel-David Leal-Lara, "Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods", Advances in Fuzzy Systems, vol.2023, pp.1, 2023.
2.
Sebastian-Camilo Vanegas-Ayala, Julio Barón-Velandia, Daniel-David Leal-Lara, "A Systematic Review of Greenhouse Humidity Prediction and Control Models Using Fuzzy Inference Systems", Advances in Human-Computer Interaction, vol.2022, pp.1, 2022.
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