In　recent　years,　emotional　neural　networks　(ENNs)　have　been　extensively　used　in　the　field　of　time　series　prediction.　As　a　variant　of　ENN,　the　radial　basis　emotional　neural　network　(RBENN)　is　chosen　as　the　prediction　model　of　time　series　in　this　paper,　because　it　has　a　special　type　of　structure　that　can　preprocess　the　interference　in　the　data.　However,　it　is　difficult　for　many　existing　methods　to　determine　network　structure　automatically　while　adjusting　network　parameters.　To　solve　this　problem,　an　RBENN　based　on　adaptive　inertia　weight　comprehensive　learning　particle　swarm　optimization　algorithm　(ADw-CLPSO-RBENN)　is　designed.　Firstly,　an　adaptive　inertia　weight　adjustment　strategy　based　on　the　CLPSO　algorithm　(ADw-CLPSO)　is　exploited　to　balance　the　global　and　local　search　ability　of　particles.　Secondly,　a　particle-variable　dimensional　learning　mechanism　(PVDLM)　is　developed　based　on　the　ADw-CLPSO　algorithm,　which　enables　particles　to　find　the　appropriate　network　structure　while　searching　for　the　optimal　parameter　solution.　Finally,　the　proposed　method　is　evaluated　in　two　time　series　and　a　real　wastewater　treatment　system.　The　simulation　results　demonstrate　that　the　proposed　ADw-CLPSO-RBENN　can　automatically　adjust　to　a　suitable　network　structure,　and　the　prediction　accuracy　is　also　better　than　other　methods.　Therefore,　the　proposed　method　has　higher　superiority　in　time　series　prediction.