Implementation of evolving fuzzy models of a nonlinear process

This paper presents details on the implementation of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of a nonlinear process represented by the pendulum dynamics in the framework of the representative pendulum-crane systems. The pendulum angle is the output variable of the TSK fuzzy models that are obtained by online identification. The rule bases and the parameters of the TSK fuzzy models are continuously evolved by an online identification algorithm (OIA) that adds new rules with more summarization power and modifies the existing rules and parameters. The OIA is associated with an input selection algorithm that guides the modelling in terms of ranking the inputs according to their importance factors. Three TSK fuzzy models evolved by the OIA are exemplified. The performance of the new evolving TSK fuzzy models is illustrated by experimental results conducted on pendulum-crane laboratory equipment.