Due　to　the　large　uncertainty　in　the　municipal　solid　waste　incineration　(MSWI)　process,　the　furnace　temperature　of　the　MSWI　process　is　difficult　to　control　and　the　controller　is　updated　frequently.　To　improve　the　accuracy　and　reduce　the　number　of　controller　updates,　a　novel　event-triggered　control　method　based　correntropy　self-organizing　TS　fuzzy　neural　network　(ET-CSTSFNN)　is　proposed.　Firstly,　the　neurons　of　the　rule　layer　are　grown　or　pruned　adaptively　based　on　activation　intensity　and　control　error　to　meet　the　dynamic　change　of　the　actual　operating　condition.　Meanwhile,　the　performance　index　is　designed　based　on　the　correntropy　of　tracking　errors,　and　the　parameters　of　the　controller　are　adjusted　by　gradient　descent　algorithm.　Secondly,　a　fixed　threshold　event-triggered　condition　is　designed　to　determine　whether　the　current　controller　is　updated　or　not.　The　stability　of　the　control　system　is　proved　based　on　the　Lyapunov　stability　theory.　Finally,　the　furnace　temperature　control　experiments　are　conducted　based　on　the　actual　data　of　a　municipal　solid　waste　incineration　plant　in　Beijing.　The　experimental　results　show　that　the　proposed　ET-CSTSFNN　controller　shows　a　better　control　performance,　which　can　reduce　the　number　of　the　controller　update　significantly　while　achieving　accurate　furnace　temperature　control　compared　with　other　traditional　control　methods.