In　order　to　solve　the　problem　the　sub-network　output　can　not　be　optimally　integrated　in　a　modular　neural　network(MNN),　this　paper　proposeds　a　dynamic　MNN　based　on　the　particle　swarm　optimization(PSO)　algorithm.　Firstly,　the　distribution　of　samples　can　be　identified　and　the　center　of　datas　can　be　updated　by　computing　the　data　density.　Secondly,　the　corresponding　sub-networks　are　activated　according　to　the　input　datas,　then　the　output　weights　are　calculated　by　the　best　contribution　degrees　which　are　computed　via　the　PSO　algorithm.　Finally,　a　dynamic　neural　network　is　completed　to　optimize　the　integrated　output　of　the　MNN.　Based　on　the　approximating　experiments　of　the　non-linear　function　and　time-series　prediction,　it　is　proved　that　the　number　of　sub-networks　can　be　adjusted　dynamically,　and　the　integrated　weights　of　the　neural　network　can　be　optimized　by　using　the　PSO　algorithm.　Comparisons　with　other　algorithms　demonstrate　that　the　proposed　method　is　more　effective　in　terms　of　the　accuracy　and　adaptive　ability.　©　2018,　Editorial　Office　of　Control　and　Decision.　All　right　reserved.