Extracting fuzzy If-Then rules using a neural network identifier with application to Boiler-Turbine system

Abstract

In this paper a feed-forward neural network is proposed to extract Fuzzy Hyperbolic Model (FHM) of industrial plants. FHMs resemble Takagi-Sugeno-Kang (TSK) fuzzy models in general, however have some advantages. FHM is an inherently nonlinear model and can capture all the nonlinearities of the system. On the other hand there are some systematic approaches to design and analysis such models. The synergy between Artificial Neural Networks (ANN), which are notorious for their black-box character, and fuzzy logic proved to be particularly successful. Such a synergy allows combining the powerful learning-from-examples capability of ANNs with the high-level symbolic information processing of fuzzy logic systems. The offered network is used to obtain the parameters of the plant from input-output data. It is shown that there is a unique transformation from the proposed network to hyperbolic model of the plant and vice versa. Parameters of the fuzzy model can be obtained from weights and biases in trained network. Boiler-Turbine system is considered as a case study to show how the proposed ANN can be used to extract the fuzzy model. The obtained model is validated by some input-output data provided from the reference model. Simulation results proved the effectiveness of the offered neural network in extracting the fuzzy model of the plant.