Based　on　continuous　Hopfield　neural　network　(CHNN),　a　new　alternative　is　developed　for　solving　linear　quadratic　(LQ)　optimal　control　problem　of　discrete-time　systems.　In　this　method,　the　LQ　performance　index　is　transformed　into　the　energy　function　of　CHNN,　and　the　control　sequence　into　the　output　vector　of　the　neurons　of　CHNN.　As　a　result,　solving　LQ　dynamic　optimization　problem　is　equivalent　to　operating　associated　CHNN　from　its　initial　state　to　the　terminal　state.　The　stable　output　vector　of　CHNN　represents　the　optimal　control　sequence.　Because　CHNN　works　in　parallel　and　is　of　real-time　characteristic,　the　present　method　is　easier　to　satisfy　the　requirement　of　real-time　control　and　will　be　promising　in　application.　©　2003　IEEE.