A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

Abstract—This paper proposes a recurrent self-evolving inter-val type-2 fuzzy neural network (RSEIT2FNN) for dynamic sys-tem processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise re-sistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi–Sugeno–Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a lo-cal internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning per-formance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identi-fications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN. Index Terms—Dynamic system identification, online fuzzy clus-tering, recurrent fuzzy neural networks (RFNNs), recurrent fuzzy systems, type-2 fuzzy systems. I