Chang, QinZhang, ZhenWei, FanyueWang, JianPedrycz, WitoldPal, Nikhil R.2024-05-192024-05-1920240950-70511872-7409https://doi.org10.1016/j.knosys.2024.111398https://hdl.handle.net/20.500.12713/5422Fuzzy neural network (FNN) plays an important role as an inference system in practical applications. To enhance its ability of handling uncertainty without invoking high computational cost, and to take variations in rules into consideration as well, we propose a new inference framework-nonstationary fuzzy neural network (NFNN). This NFNN is composed of a series of zero -order TSK FNNs with the same structure but using slightly perturbed fuzzy sets in the corresponding neurons, which is inspired from the non -stationary fuzzy sets and can mimic the variation in human's decision -making process. In order to obtain a concise and adaptive rule base for NFNN, a modified affinity propagation (MAP) clustering method is proposed. The MAP can determine the number of rules in an adaptive manner, and is used to initialize the rule parameters of NFNN, which we call Adaptive NFNN (ANFNN). Numerical experiments have been carried out over 17 classification datasets and three regression datasets. The experimental results demonstrate that ANFNN exhibits better accuracy, generalization ability, and fault -tolerance ability compared with the classical type -1 fuzzy neural network. In 15 of the 17 classification datasets, ANFNN achieves the same or better accuracy performance compared to interval type -2 FNNs with about half time consumed. This work confirms the feasibility of integrating simplestructured type -1 TSK FNNs to achieve the performance of interval type -2 FNNs, and proves that ANFNN can be a more accurate and reliable alternative to classical type -1 FNN.eninfo:eu-repo/semantics/closedAccessNonstationary Fuzzy SetNonstationary Fuzzy Neural NetworkFuzzy Neural NetworkClusteringConvergenceArticle288WOS:0011805775000012-s2.0-85184517760N/A10.1016/j.knosys.2024.111398Q1