Automated　model　generation　(AMG)　has　become　a　popular　technique　for　systematically　developing　neural　network　models　by　avoiding　manual　trial-and-errors.　However,　when　the　initial　number　of　hidden　neurons　is　far　from　the　optimal　value,　the　existing　AMG　methods　usually　take　a　relatively　large　amount　of　CPU　time　to　find　the　optimal　structure.　To　deal　with　this　problem,　for　the　first　time,　Bayesian-based　formulation　is　introduced　into　the　AMG　method.　The　proposed　Bayesian-based　AMG　method　can　efficiently　determine　the　minimum　number　of　hidden　neurons　with　maximum　accuracy　during　the　model　development　process.　The　proposed　method　can　greatly　reduce　CPU　time　for　model　generation　in　comparison　with　the　existing　AMG　technique.　A　microwave　filter　example　is　used　to　demonstrate　the　proposed　method.