標題: An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination
作者: Lin, Chin-Teng
Pal, Nikhil R.
Wu, Shang-Lin
Liu, Yu-Ting
Lin, Yang-Yin
電控工程研究所
腦科學研究中心
Institute of Electrical and Control Engineering
Brain Research Center
關鍵字: Feature selection;fuzzy neural network;online structure learning;system identification;type-2 neural fuzzy systems (NFSs)
公開日期: 1-七月-2015
摘要: 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.
URI: http://dx.doi.org/10.1109/TNNLS.2014.2346537
http://hdl.handle.net/11536/127875
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2014.2346537
期刊: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume: 26
起始頁: 1442
結束頁: 1455
顯示於類別:期刊論文