This　paper　addresses　convergence　issue　of　two　networked　iterative　learning　control　(NILC)　schemes　for　a　class　of　discrete-time　nonlinear　systems　with　random　packet　dropout　occurred　in　input　and　output　channels　and　modelled　as　0-1　Bernoulli-type　random　variable.　In　the　two　NILC　schemes,　the　dropped　control　input　of　the　current　iteration　is　substituted　by　the　synchronous　input　used　at　the　previous　iteration,　whilst　for　the　dropped　system　output,　the　first　replacement　strategy　is　to　replace　it　by　the　synchronous　pre-given　desired　trajectory　and　the　second　one　is　to　substitute　it　by　the　synchronous　output　used　at　the　previous　iteration.　By　the　stochastic　analysis　technique,　we　analyse　the　convergence　properties　of　two　NILC　schemes.　It　is　shown　that　under　appropriate　constraints　on　learning　gain　and　packet　dropout　probabilities,　the　tracking　errors　driven　by　the　two　schemes　are　convergent　to　zero　in　the　expectation　sense　along　iteration　direction,　respectively.　Finally,　illustrative　simulations　are　carried　out　to　manifest　the　validity　and　effectiveness　of　the　results.