Evolving Fuzzy Model Identification of Nonlinear Wiener-Hammerstein Processes

This paper presents a new approach to neuro-fuzzy model identification based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering method. The proposed identification algorithm generates the model on the fly and requires few user-defined parameters, which is one of the main advantages compared to other methods. The partitioning of the input-output data space depends on the chosen criteria and thresholds and depends only on the operating point of the model. As an example, the Wiener-Hammerstein type of a dynamic process was identified to show the potential of the proposed method in identifying nonlinear dynamic models. The Wiener-Hammerstein structure was used because a variety of processes can be modeled with this type of structure. Moreover, we tested the same identification concept on a real heat exchanger plant with strong nonlinear behavior. In addition, the limitations of the real sensors and actuators represent a serious challenge to the identification procedure. Both experiments, on a simulated Wiener-Hammerstein model and on a real plant, have shown that the proposed new neuro-fuzzy model identification with the new merging concept is very easy to implement, can perform all necessary calculations online, and can generate meaningful models.

Automated summary

This paper introduces a new approach to neuro-fuzzy model identification using a filtered recursive least squares method and an incrementally evolving Gaussian clustering method. The method shows potential in identifying nonlinear dynamic models and has been tested on a real heat exchanger plant. The results of the experiments demonstrate that the proposed method is easy to implement, can perform necessary calculations online, and generates meaningful models.