Artificial　neural　network　(ANN)　model　development　for　microwave　components　principally　includes　two　parts　of　work,　i.e.,　data　sampling　and　model　structure　adaptation.　In　existing　various　ANN　modeling　methods,　the　model　structure　adaptation　process　mainly　focuses　on　adjusting　the　number　of　neurons　within　each　hidden　layer　of　ANN　while　keeping　the　number　of　layers　unchanged.　To　make　the　ANN　modeling　process　more　flexible　and　efficient,　an　automated　multilayer　neural　network　structure　adaptation　method　with　l(1)　regularization　is　proposed　in　this　letter.　We　propose　a　new　ANN　model　structure　combining　multilayer　perceptron　(MLP)　and　additional　connections　between　the　output　layer　and　each　hidden　layer/input　layer.　A　new　training　scheme　with　l(1)　regularization　is　proposed　to　automatically　determine　the　final　model　structure　with　user-desired　model　accuracy.　Using　the　proposed　model　structure　adaptation　method,　both　the　number　of　layers　and　the　number　of　neurons　within　each　layer　of　the　final　ANN　model　can　be　adaptively　determined　to　address　different　needs　for　different　microwave　modeling　problems.　The　proposed　method　is　demonstrated　by　two　microwave　filter　modeling　examples　in　which　the　model　development　process　achieves　a　time　saving　of　at　least　40%　over　existing　methods.