A framework for data-driven enhancement of the TSK fuzzy neural network

However, the effective application of such models is often reliant on expert knowledge of the domain area that may not always be available, may contain human bias/error, and is challenging humans to articulate mathematically. Therefore novel, data-driven approaches to TSKFNN construction and operation are required to address this limitation.

This research proposes a series of data-driven improvements to the state-of-the-art TSKFNN in an incremental way including the fuzzification process, the rule base generation, and rule aggregation. Ensemble learning has inspired a novel data- driven rule aggregation method that enables increased performance and generalizability compared with the existing state-of-the-art TSKFNNs. Additionally, the existing TSKFNNs suffer from the curse of dimensionality with rule bases growing exponentially with an increasing number of input features.