This　paper　describes　artificial　neural　network　approaches　to　convert　the　conventionally　expensive　process　of　electronic　modelling　into　an　automated　model　generation　process.　Artificial　neural　networks　are　trained　using　machine　learning　algorithm　to　learn　the　electronic　behaviour,　and　the　trained　neural　network　becomes　a　model　to　help　predict　the　electronic　device　behaviour.　The　automated　model　generation　algorithm　performs　adaptive　data　sampling　to　determine　the　amount　and　the　distribution　of　training　data　needed　to　train　neural　networks.　The　algorithm　also　determines　the　number　of　hidden　neurons　needed　to　achieve　a　compact　and　accurate　model.　Also　incorporated　into　the　automated　model　generation　method　is　an　efficient　interpolation　approach　to　make　the　process　much　faster.　The　objective　of　the　described　method　is　to　generate　a　compact　neural-network　based　model　with　better　accuracy　and　in　less　time　than　conventional　approach.　Examples　of　automated　modeling　of　radio-frequency　and　microwave　filters　used　in　wireless　electronic　systems　are　described　showing　the　advantage　of　this　technique.　©　2019　IOP　Publishing　Ltd.