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 |