title: An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.
abstract: We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.
authors.forename: Chin-Teng
authors.initials: CT
authors.lastname: Lin
authors.forename: Nikhil R
authors.initials: NR
authors.lastname: Pal
authors.forename: Shang-Lin
authors.initials: SL
authors.lastname: Wu
authors.forename: Yu-Ting
authors.initials: YT
authors.lastname: Liu
authors.forename: Yang-Yin
authors.initials: YY
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001336:Automobiles; D002617:Chemical Industry; D003198:Computer Simulation; D017143:Fuzzy Logic; D000069550:Machine Learning; D016571:Neural Networks, Computer; D016011:Normal Distribution; D009862:Online Systems