A Neuro-Fuzzy Approach to Identify LettuceGrowth and Greenhouse Climate

Abstract

A hybrid neuro-fuzzy approach called the NUFZY system, which embeds fuzzy reasoning into a triple-layered network structure, has been developed to identify nonlinear systems. A set of membership functions at the input layer is partially linked with a layer of rules, using pre-set parameters. By means of a simplified centroid of gravity defuzzification method, the output becomes linear in the weights. Therefore, very fast estimation of the weight parameters can be achieved by using the orthogonal least squares (OLS) method, which also provides a method to efficiently remove the redundant fuzzy rules from the prototype rule base of the NUFZY system. In this paper, the NUFZY system is applied to identify lettuce growth and greenhouse temperature from real experimental data. Results show that the NUFZY model with the fast OLS training can perform quite well in predicting both lettuce growth and greenhouse temperature. In contrast to the mechanistic modeling procedures, the neuro-fuzzy approach offers an easier route and a fast way to build the nonlinear mapping of inputs and outputs. In addition, the resulting internal network structure of the NUFZY system is a self-explanatory representation of fuzzy rules. Under this frame, it is a perspective that one is able to incorporate the human knowledge in this approach, and, hopefully, to deduce any interpretable rules that describe the systems‘ behavior.