To　improve　the　accuracy　and　robustness　of　stochastic　configuration　networks　(SCNs)　for　resolving　multi-target　regression　tasks,　this　paper　proposes　a　robust　modeling　approach　based　on　improved　stochastic　configuration　networks.　A　parallel　implementation　of　SCN　models　is　designed　to　incrementally　generate　the　hidden　nodes,　which　enhances　the　diversity　of　hidden　layer　mapping　through　information　superposition　and　spanning　connection.　We　employ　an　elastic　net　regularization　model　to　sparsely　constrain　the　model　parameters　to　characterize　the　correlation　among　multiple　targets.　Then,　the　mixture　Laplace　distributions　are　used　as　the　prior　distribution　of　each　target　modeling　error,　and　the　output　weights　of　the　SCN　model　are　re-evaluated　by　maximizing　a　posteriori　estimation　to　enhance　model＇s　robustness　with　respect　to　some　uncertainties　presented　in　training　samples.　The　modelling　performance　of　the　proposed　solution　is　tested　on　six　standard　datasets　and　the　historical　data　of　a　municipal　solid　waste　incineration　process.　The　experimental　results　show　that　the　proposed　modeling　technique　has　advantages　in　terms　of　both　the　prediction　accuracy　and　the　robustness.