Artificial　neural　networks　are　information　processing　systems　having　achieved　great　success　in　many　areas　such　as　speech　recognition,　image　processing　and　more.　In　this　paper,　we　describe　neural　network　approaches　to　learn　the　complex　behavior　of　high-frequency　electronic　circuits　through　learning.　The　training　data　which　embed　the　information　of　high-frequency　electronic　behavior　and　their　relationships　with　structural　parameters　are　obtained　by　electromagnetic　simulation.　We　address　the　issue　of　data　generation　expenses　for　training　neural　networks　by　incorporating　prior　knowledge　of　electronic　behavior　in　the　form　of　semi-analytical　equations　and　equivalent　circuits.　The　knowledge　based　neural　network　model　can　be　trained　with　less　data　while　retaining　neural　network　accuracy,　and　can　exhibit　good　tendency　of　electronic　behavior　even　used　outside　the　training　region.　©　2019　IEEE.