To　solve　the　problem　that　subnetwork　output　cannot　be　optimally　integrated　in　a　modular　neural　network　(MNN),　this　paper　proposes　an　adaptive　particle　swarm　optimization　algorithm　for　dynamic　MNN　(APSO-DMNN).　First,　the　method　identifies　the　distribution　of　samples　and　updates　the　training　parameters　based　on　data　potential.　Second,　the　MNN　activates　the　corresponding　subnetworks　according　to　the　input　data.　Calculate　the　weights　based　on　an　APSO　algorithm,　which　can　dynamically　optimize　the　contribution　of　the　output.　Then,　the　inertia　weights　in　the　APSO　algorithm　are　adjusted　by　a　nonlinear　function　in　order　to　avoid　being　trapped　into　local　optimal　values.　Finally,　the　proposed　APSO-DMNN　can　be　obtained　based　on　the　optimal　integration　and　dynamic　adjustment.　Comparisons　with　other　algorithms　indicate　that　the　proposed　method　is　more　effective　in　modeling　and　predicting.