A Generalized Heterogeneous Type-2 Fuzzy Classifier and Its Industrial Application

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.

I. Introduction

In industrial production process, it is very significant for real-time production state monitoring and analyzing to identify online; the feature points from a large number of collected process data [1]–[3]. The existing online classification algorithms with fixed structure, such as support vector machine (SVM), evolving fuzzy systems (EFSs), etc., cannot effectively adapt to complex industrial systems since its dynamics may vary smoothly or abruptly due to the change of various operating conditions or running states [4], [5].