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Indexed by:期刊论文

Date of Publication:2020-10-01

Journal:IEEE TRANSACTIONS ON FUZZY SYSTEMS

Volume:28

Issue:10

Page Number:2287-2301

ISSN No.:1063-6706

Key Words:Uncertainty; Fuzzy logic; Fuzzy neural networks; Support vector machines; Computational modeling; Artificial neural networks; Fuzzy neural network (FNN); fuzzy rule-base classifier; heterogeneous type-2 fuzzy system; online learning

Abstract:Recently, evolving fuzzy systems have been proved to be effective in dealing with real-time data streams. However, their fixed structures are not flexible enough to address the structural variations triggered by the changing operating conditions or system states in complex industrial environments. A novel generalized heterogeneous interval type-2 (IT2) fuzzy classifier, named as GHIT2Class, is proposed in this paper, which is built upon a multivariable IT2 fuzzy neural network. To fully reflect the industrial data characteristics of uncertainty, this paper proposes an approach of constructing the uncertainty footprint with ellipsoidal rotation. A rule pruning method based on error and incentive intensity dynamic adjustment mechanism is reported in the process of modeling, and a corresponding rule recall mechanism is designed to avoid rules of catastrophic forgetting. In addition, the simultaneous update of the upper and lower bounds of IT2 fuzzy consequent parameters is designed to relieve the computing overhead of the fuzzy systems. The performance of the proposed GHIT2Class is experimentally validated by a number of synthetic datasets and industry study cases by using state-of-the-art comparative classifiers, where the proposed approach outperforms the others in achieving the best tradeoff between accuracy and simplicity.