FAPOP: Feature analysis enhanced pseudo outer-product fuzzy rule identification system

Most existing neural fuzzy systems either overlook the importance of feature analysis; or it is performed as a separate phase prior to the design stage of the systems. This paper proposes a novel neural fuzzy system, named Feature Analysis Enhanced Pseudo Outer-Product Fuzzy Rule Identification System (FAPOP), which integrates its design with feature analysis. The objective is two-folds; namely, (1) to improve the interpretability of the system by identifying features relevant to its computational structure; and (2) to improve the accuracy of the system by identifying features relevant to the application problem. The proposed FAPOP model is subsequently employed in a series of benchmark simulations to demonstrate its efficiency as a neural fuzzy modeling system, and excellent performances have been achieved.