Random　neural　networks　(RNNs)　prediction　model　is　built　with　a　specific　randomized　algorithm　by　employing　a　single　hidden　layer　structure.　Duo　to　input　weights　and　biases　are　randomly　assigned　and　output　weights　are　analytically　calculated,　it　is　widely　used　in　different　applications.　Most　of　RNNs-based　soft　measuring　models　assign　the　random　parameter　scope　to　default　range　[-　1,　1].　However,　this　cannot　ensure　the　universal　approximation　capability　of　the　resulting　model.　In　this　paper,　selective　ensemble　(SEN)-RNN　algorithm　based　on　adaptive　selection　scope　of　input　weights　and　biases　is　proposed　to　construct　soft　measuring　model.　Bootstrap　and　genetic　algorithm　optimization　toolbox　are　used　to　construct　a　set　of　SEN-RNN　models　with　different　random　parameter　scope.　The　final　soft　measuring　model　is　adaptive　selected　in　terms　of　the　best　generation　performance　among　these　SEN　models.　Simulation　results　based　on　housing　benchmark　dataset　of　UCI　and　dioxin　concentration　dataset　of　municipal　solid　waste　incineration　validate　the　proposed　approach.