A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications

In this paper, a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN) is proposed for system modeling and noise cancellation problems. A TSCIT2FNN uses type-2 fuzzy sets in an FNN in order to handle the uncertainties associated with information or data in the knowledge base. The antecedent part of each compensatory fuzzy rule is an interval type-2 fuzzy set in the TSCIT2FNN, where compensatory-based fuzzy reasoning uses adaptive fuzzy operation of a neural fuzzy system to make the fuzzy logic system effective and adaptive, and the consequent part is of the TSK type. The TSK-type consequent part is a linear combination of exogenous input variables. Initially, the rule base in the TSCIT2FNN is empty. All rules are derived according to online type-2 fuzzy clustering. For parameter learning, the consequent part parameters are tuned by a variable-expansive Kalman filter algorithm to the reinforce parameter learning ability. The antecedent type-2 fuzzy sets and compensatory weights are learned by a gradient descent algorithm to improve the learning performance. The performance of TSCIT2FNN for the identification is validated and compared with several type-1 and type-2 FNNs. Simulation results show that our approach produces smaller root-mean-square errors and converges more quickly.

I. Introduction

Recently, Fuzzy Neural Networks (FNNs) have become popular in applications in control, identification, prediction, pattern recognition, and bioengineering. FNNs inherit their learning ability from neural networks and their inference technology from fuzzy systems and are used for solving the aforementioned characteristic behaviors [1]–[12], such as in the control of robot manipulators [4], temperature control [5], pattern classification [6], ventricular premature contraction (VPC) detection [7], energy conversion [8], and hardware implementation [9]. FNNs are an effective tool for dealing with complex nonlinear processes. Some well-known feedforward FNNs include an adaptive neuro-fuzzy inference system [2], an online self-constructing neural fuzzy inference network (SONFIN) [3], and fuzzy wavelet neural networks (FWNNs) [10]–[12].