Most of the dynamics in real-world systems are compiled by shifts and drifts, which are uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. In practice, the rule growing and pruning are carried out merely benefiting from a small snapshot of the complete training data to truncate the computational load and memory demand to the low level. An exposure of a novel algorithm, namely parsimonious network based on fuzzy inference system (PANFIS), is to this end presented herein. PANFIS can commence its learning process from scratch with an empty rule base. The fuzzy rules can be stitched up and expelled by virtue of statistical contributions of the fuzzy rules and injected datum afterward. Identical fuzzy sets may be alluded and blended to be one fuzzy set as a pursuit of a transparent rule base escalating human's interpretability. The learning and modeling performances of the proposed PANFIS are numerically validated using several benchmark problems from real-world or synthetic datasets. The validation includes comparisons with state-of-the-art evolving neuro-fuzzy methods and showcases that our new method can compete and in some cases even outperform these approaches in terms of predictive fidelity and model complexity.

Nowadays, there is increasing demand for an integrated system usable to real-time environments under limited computational resources and minimum operator supervision. In contrast, the model is also supposed to actualize high predictive quality in order to confirm the process safety and attractive working framework allowing the user to grasp how the particular task is settled. A holistic concept of a fully data-driven modeling tool namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS) is proposed in this paper. The major spotlight of GENEFIS is in delivering a sensible tradeoff between high predictive accuracy and parsimonious rule base while reckoning tractable rule semantics. The viability of GENEFIS is numerically validated via a series of experimentations using real world and artificial datasets and is compared against state of the art of the evolving neuro-fuzzy systems (ENFSs), where GENEFIS not only showcases higher predictive accuracies but also lands on more frugal structures than other algorithms.