This　paper　proposes　a　novel　identification　method　and　a　simplification　scheme　for　T-S　fuzzy　neural　networks,　which　consists　of　two　steps.　The　first　step　refers　to　the　structure　design　based　on　an　incremental　clustering　approach　whose　basic　ideas　are　that　the　structure　identification　of　fuzzy　neural　networks　is　guided　by　the　attenuation　of　output　approximation　error　in　each　cluster　and　processed　by　a　recursive　refined　clustering　iteration　with　the　input　space　clustering　and　sub-clustering　as　the　main　steps.　Once　the　structure　of　a　T-S　fuzzy　neural　network　is　identified　by　the　incremental　clustering　approach,　its　parameters　are　further　learned　and　refined　by　the　Levenberg-Marquardt　optimization　algorithm.　The　second　step　refers　to　the　structure　simplification　including　removing　redundant　fuzzy　rules　and　merging　highly　similar　fuzzy　rules.　Furthermore,　the　performances　of　several　similarity　calculating　methods　are　analyzed　and　discussed,　which　provides　a　basis　for　the　selection　of　the　appropriate　similarity　analysis　and　effective　calculating　method　for　the　merging　of　fuzzy　rules　and　system　simplification.　That　is,　the　given　performance　analysis　provides　the　methodology　basis　and　design　guide　for　structure　simplification　based　on　similarity　analysis　and　merger　of　fuzzy　rules.　Several　experiments　are　implemented　to　illustrate　the　feasibility　and　effectiveness　of　the　proposed　approach.　(C)　2019　Elsevier　B.V.　All　rights　reserved.