Echo　state　network　(ESN)　is　one　of　the　most　well-known　types　of　reservoir　computing　because　of　its　outstanding　performance　when　chaotic　time　series　prediction　is　conducted.　However,　sometimes　it　works　poorly　because　the　reservoir　connectivity　and　weight　structure　are　created　randomly.　To　solve　this　problem,　we　propose　a　modified　ESN　based　on　contribution　rate　algorithm.　By　pruning　uninmportant　connections　without　loss　of　majoy　information,　the　proposed　method　can　not　only　optimize　the　network　structure,　but　also　improve　the　generalization　performance　of　network.　Experimental　results　and　performance　comparisons　demonstrate　that　the　modified　ESN　outperforms　the　ESN　without　optimization.　©　2017　IEEE.