A hybrid intelligent system: genetic algorithm and rough set incorporated neural fuzzy inference network - Wang, Di (2014)
Neural Fuzzy Inference System (NFIS) has been intensively investigated due to its aptitudes in accurate data processing as well as extractable and human interpretable inference rule base. However, most NFIS architectures focus primarily on modeling accuracy. Lesser attention has been devoted to improve the interpretability of the fuzzy inference rule base. In this thesis, a high-level interpretability of the rule base is manifested through the use of a small number of features and a small number of simple rules without redundancies and inconsistencies. Furthermore, a small number of fuzzy membership functions without much overlap between the adjacent ones promote greater legibility. With a high-level accuracy and better interpretability, intelligent systems become more convincing to developers, experts, and users. However, many established methods that perform knowledge reduction are applied to the derived rule base. Lesser attention has been devoted to derive a compact fuzzy inference rule base from the initial design and during an iterative optimization process. This thesis aims to construct an NFIS that achieves a high-level interpretability with competitive accuracy in an integrated framework focusing on leveraging the trade-off between accuracy and interpretability from the design phase and throughout the iterative optimization process. To realize this objective, rough set theory is incorporated to perform knowledge reduction and genetic algorithm is incorporated to search for pseudo optimal solutions.