INFGMN – Incremental Neuro-Fuzzy Gaussian mixture network

Abstract Accuracy and interpretability are contradictory objectives that conflict in all machine learning techniques and achieving a satisfactory balance between these two criteria is a major challenge. The objective is not only to maximize interpretability, but also to guarantee a high degree of accuracy. This challenge is even greater when it is considered that the model will have to evolve and adapt itself to the dynamics of the underlying environment, i.e. it will have to learn incrementally. Little research has been published about incremental learning using Mamdani–Larsen (ML) fuzzy models under these conditions. This article presents a novel proposal for a Neuro-Fuzzy System (NFS) with an incremental learning capability, the Incremental Neuro-Fuzzy Gaussian Mixture Network (INFGMN), that attempts to generate incremental models that are highly interpretable and precise. The principal characteristics of the INFGMN are as follows: (i) the INFGMN learns incrementally using a single sweep of the training data (each training pattern can be immediately used and discarded); (ii) it is capable of producing reasonable estimates based on few training data; (iii) the learning process can proceed in perpetuity as new training data become available (learning and recalling phases are not separate); (iv) the INFGMN can deal with the Stability-Plasticity dilemma and is unaffected by catastrophic interference (rules are added or removed whenever necessary); (v) the fuzzy rule base is defined automatically and incrementally (new rules are added whenever necessary); and (vi) the INFGMN maintains an ML-type fuzzy rule base that attempts to provide the best trade-off between accuracy and interpretability, thereby dealing with the Accuracy-Interpretability dilemma. The INFGMNs performance in terms of learning and modelling is assessed using a variety of benchmark applications and the results are promising.