Artificial　neural　network　(ANN)　is　a　powerful　technique　in　the　microwave　modeling　area.　The　ANN　structure　adaptation　in　existing　automated　model　generation　(AMG)　algorithms　focuses　on　adjusting　either　the　number　of　hidden　layers　or　the　number　of　hidden　neurons,　or　adjusting　both　simultaneously　but　in　a　layer-by-layer　manner.　In　this　letter,　a　novel　batch-adjustment　algorithm　for　ANN　structure　adaptation　is　proposed　to　improve　the　ANN　modeling　efficiency.　We　introduce　a　cascaded　multilayer　ANN　structure　and　propose　a　new　training　algorithm　to　train　it.　The　ANN　structure　is　automatically　adjusted　by　adding　or　removing　the　redundant　hidden　layers　in　batch　and　makes　a　compromise　between　the　number　of　hidden　layers　and　the　number　of　hidden　neurons　simultaneously.　Compared　to　existing　algorithms,　our　proposed　algorithm　is　more　flexible　and　efficient　in　ANN　structure　adaptation　by　performing　fewer　times　of　ANN　training　during　the　model　development.　Two　microwave　modeling　examples　are　used　to　demonstrate　the　proposed　algorithm.