Anti-Forgetting Incremental Learning Algorithm for Interval Type-2 Fuzzy Neural Network

Sample property drift is an essential issue for interval type-2 fuzzy neural networks (IT2FNNs). When the samples with fresh properties appear, IT2FNN invariably suffers from catastrophic forgetting due to the modification of its numerous parameters. To solve this problem, an anti-forgetting incremental learning algorithm is proposed to update IT2FNN. First, a double-displacement indicator (DDI) is designed to detect when catastrophic forgetting occurs caused by property drift. It integrates the indicators from the feature and target spaces to avoid missing detection of property breakpoints. Second, a multi-level learning objective is developed to perceive catastrophic forgetting. The convergence, diversity and stability criteria of fuzzy rules are embedded into the objective to improve the compatibility of IT2FNN for different properties. Third, an adaptive hierarchical update strategy (AHUS) is proposed to update the parameters of IT2FNN. With AHUS, the parameters are shared among samples with different properties, which can alleviate catastrophic forgetting. Finally, some experiments have verified that the performance of the presented method is superior to other methods in dynamic system identification.