In　this　paper,　a　neural　network-aided　adaptive　Kalman/UKF　integrated　filter　was　studied　for　the　nonlinear　alignment　of　inertial　navigation　system.　First　a　more　robust　learning　algorithm　for　neural　network　based　on　the　Kalman/UKF　integrated　filter　is　derived.　Since　it　gives　more　accurate　estimate　of　the　linkweights,　and　the　convergence　performance　is　improved.　This　algorithm　is　then　extended　further　to　develop　an　adaptive　Kalman/UKF　integrated　filter　algorithm　for　state　estimation　of　the　nonlinear　system.　In　this　algorithm,　the　neural　network　is　used　to　approximate　the　uncertainty　of　the　system　noise.　And　the　Kalman/UKF　integrated　filter　is　used　for　both　neural　network　online　training　and　state　estimation　simultaneously.　Simulation　results　show　that　the　neural　network-aided　adaptive　Kalman/UKF　integrated　filter　algorithm　is　very　effective　for　nonlinear　initial　alignment　of　inertial　navigation　system.　Not　only　the　initial　alignment　accuracy　can　surely　be　obtained,　but　also　the　time　consumption　of　each　run　is　reduced　considerably.　Consequently,　an　available　and　practicable　algorithm　for　the　nonlinear　initial　alignment　of　inertial　navigation　system　is　discovered.