In　RBF　neural　networks,　the　basis　functions　of　hidden　layers　are　often　clustered　by　K-means　algorithm.　However,　due　to　the　K-means　algorithm＇s　dependence　on　the　initial　cluster　center,　it　is　too　sensitive　to　noisy　data.　This　paper　proposes　an　RBF　neural　network　based　on　K-nearest　neighbors　optimized　clustering　algorithm　by　fast　search　and　finding　the　density　peaks　of　a　dataset(KNN-DPC).　First,　the　optimized　KNN-DPC　algorithm　is　used　to　cluster　data　with　too　many　noisy　points,　then　the　basis　function　center　of　RBF　neural　network　is　obtained,　finally,　the　RBF　neural　network　is　constructed.　The　accuracy　of　this　algorithm　is　verified　by　simulation　experiments,　and　the　results　show　that　the　algorithm　is　effective　and　practical.　©　2018　IEEE.