Please use this identifier to cite or link to this item:
|Title:||Rough set-based neuro-fuzzy system.||Authors:||Ang, Kai Keng.||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence||Issue Date:||2008||Source:||Ang, K. K. (2008). Rough set-based neuro-fuzzy system. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Neuro-fuzzy systems is a popular hybridization in soft computing that abstracts a fuzzy model from given numerical examples using neural learning techniques to formulate accurate predictions on unseen samples. The fuzzy model incorporates the human-like style of fuzzy reasoning through a linguistic model that comprises if-then fuzzy rules and linguistic terms described by membership functions. However, modeling data using neuro-fuzzy systems involves the contradictory requirements of interpretability versus accuracy. Prevailing research that focused on accuracy employed optimization that resulted in membership functions that derailed from human-interpretable linguistic terms. In addition, the modeling of high-dimensional data requires a large number of if-then fuzzy rules that exceeds human level interpretation. This thesis focuses on increasing interpretability without compromising accuracy using a novel hybrid intelligent Rough set-based Neuro-Fuzzy System (RNFS), which synergizes rough set-based knowledge reduction with neuro-fuzzy systems. RNFS directly addresses the problems with the following contributions.||URI:||https://hdl.handle.net/10356/2487||DOI:||10.32657/10356/2487||Rights:||Nanyang Technological University||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Theses|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.