Recursive Level Set Fuzzy Modeling

The paper introduces a data driven recursive fuzzy modeling method using level sets. The level set is a fuzzy modeling technique whose outputs are computed using functions that map the activation levels of the inputs into values in the output space. It develops a general data-driven recursive framework for fuzzy modeling using the level set construct. The framework is intuitive, easy to implement, computationally efficient, and produces effective models. Its performance is evaluated against methods representative of the state of the art evolving fuzzy modeling, and long-short term memory and deep neural modeling. The results suggest that the data driven recursive fuzzy modeling method using level sets outperforms state of the fuzzy and neural modeling methods.