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

来源：IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(7): 1442-1455.

DOI：10.1109/TNNLS.2014.2346537

• Feature selection
• fuzzy neural network
• online structure learning
• system identification
• type-2 neural fuzzy systems (NFSs)

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.

• 出版日期2015-7