To　avoid　the　computational　difficulty　in　solving　infinite　horizon　optimal　control　problem　with　Riccati　equation　for　time-varying　systems,　an　infinite　horizon　dynamic　optimal　control　method　is　developed　using　continuous　time　continuous　state　Hopfield　neural　networks　(CTCSHNN).　The　CTCSHNN　can　be　constructed　online　by　establishing　the　equivalent　relation　between　the　energy　function　of　CTCSHNN　and　the　performance　index　of　receding　horizon　control.　Theoretical　analysis　is　then　given　to　show　that　the　designed　CTCSHNN　has　stability,　and　the　receding　horizon　control　can　be　produced　directly　from　CTCSHNN＇s　stable　outputs.　Moreover,　the　closed　loop　optimal　control　in　infinite　horizon　can　also　be　implemented　by　integrating　the　method　with　rolling　optimization　strategy.　Finally,　simulation　experiment　shows　that　the　proposed　theoretical　design　is　effective,　and　the　closed-loop　optimal　control　in　infinite-horizon　is　feasible　by　using　receding　horizon　control　based　on　CTCSHNN.