In　this　paper,　a　hybrid　intelligent　tracking　control　approach　is　developed　to　address　optimal　tracking　problems　for　a　class　of　nonlinear　discrete-time　systems.　The　generalized　value　iteration　algorithm　is　utilized　to　attain　the　admissible　tracking　control　with　off-line　training,　while　the　on-line　near-optimal　control　method　is　established　to　enhance　the　control　performance.　It　is　emphasized　that　the　value　iteration　performance　is　improved　by　introducing　the　acceleration　factor.　By　collecting　the　input-output　data　of　the　unknown　system　plant,　the　model　neural　network　is　constructed　to　provide　the　partial　derivative　of　the　system　state　with　respect　to　the　control　law　as　the　approximate　control　matrix.　A　novel　computational　strategy　is　introduced　to　obtain　the　steady　control　of　the　reference　trajectory.　The　critic　and　action　neural　networks　are　utilized　to　approximate　the　cost　function　and　the　tracking　control,　respectively.　Considering　approximation　errors　of　neural　networks,　the　stability　analysis　of　the　specific　systems　is　provided　via　the　Lyapunov　approach.　Finally,　two　numerical　examples　with　industrial　application　backgrounds　are　involved　for　verifying　the　effectiveness　of　the　proposed　approach.