Visual-FIR: A tool for model identification and prediction of dynamical complex systems

A new platform for the fuzzy inductive reasoning (FIR) methodology has been designed and developed under the MATLAB environment. The new tool, named Visual-FIR, allows the identification of dynamic systems models in a user-friendly environment. FIR offers a pattern-based approach to modeling and predicting either univariate or multivariate time series, obtaining very good results when applied to various areas such as control, biology, and medicine. However, the available implementation of FIR was such that new code had to be developed for each new application studied. Visual-FIR resolves this limitation and offers a high-efficiency implementation. Furthermore, the Visual-FIR platform presents a new vision of the methodology based on process blocks and adds new features, increasing the overall capabilities of the FIR methodology. The DAMADICS benchmark problem is addressed in this research using the Visual-FIR approach.