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　antiforgetting　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　multilevel　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.