Deep　neural　network　techniques　are　recently　recognized　as　powerful　tools　in　solving　complex　and　challenging　modeling　problems　of　microwave　components.　However,　direct　training　of　a　fully　connected　deep　neural　network　with　sigmoid　functions　using　the　backpropagation　(BP)　algorithm　is　difficult　because　of　the　vanishing　gradient　problem.　In　this　paper,　we　propose　a　novel　deep　neural　network　modeling　technique　with　batch　normalization　(BN)　to　address　the　vanishing　gradient　problem.　BN　layers　are　added　before　every　sigmoid　hidden　layer　of　the　deep　neural　network　to　normalize　the　inputs　of　each　sigmoid　hidden　layer　with　additional　scaling　and　shifting,　thus　overcoming　the　vanishing　gradient　problem.　Automated　model　generation　(AMG)　algorithm　is　also　utilized　to　automatically　determine　the　suitable　number　of　BN　layers　and　sigmoid　hidden　layers　during　deep　neural　network　training　process.　This　proposed　technique　is　illustrated　by　two　microwave　examples.