Novelty Detection for Iterative Learning of MIMO Fuzzy Systems

This paper proposes a methodology for iterative learning of multi-input multi-output (MIMO) fuzzy models focusing on dynamic system identification. The first step of the proposed method is the learning of the antecedent part of the fuzzy system, which is learned iteratively, where fuzzy rules can be added or merged based on the presented novelty detection and similarity criteria defined by a recursive extension of the Gath-Geva clustering algorithm. Then, the consequent part consists in the direct implementation of a non-recursive fuzzy approach that uses global least squares, Observer Kalman Filter Identification (OKID) and the Eigensystem Realization Algorithm (ERA). The proposed method is validated using experimental data from a real quadrotor aerial robot, a nonlinear dynamic system. Using quantitative performance metrics, the proposed method is compared with Hammerstein-Wiener models (H.-W.), nonlinear autoregressive models with exogenous input (NARX), and state-space models using subspace method with time-domain data (N4SID), other MIMO system identification techniques. The proposed method achieved better results compared to other techniques, showing the importance and versatility of learning based on novelty detection for MIMO problems.