Fuzzy　neural　networks　(FNNs),　with　suitable　structures,　have　been　demonstrated　to　be　an　effective　tool　in　approximating　nonlinearity　between　input　and　output　variables.　However,　it　is　time-consuming　to　construct　an　FNNwith　appropriate　number　of　fuzzy　rules　to　ensure　its　generalization　ability.　To　solve　this　problem,　an　efficient　optimization　technique　is　introduced　in　this　paper.　First,　a　self-adaptive　structural　optimal　algorithm　(SASOA)　is　developed　to　minimize　the　structural　risk　of　an　FNN,　leading　to　an　improved　generalization　performance.　Second,　with　the　proposed　SASOA,　the　fuzzy　rules　of　SASOA-based　FNN　(SASOA-FNN)　are　generated　or　pruned　systematically.　This　SASOA-FNN　is　able　to　organize　the　structure　and　adjust　the　parameters　simultaneously　in　the　learning　process.　Third,　the　convergence　of　SASOA-FNN　is　proved　in　the　cases　with　fixed　and　updated　structures,　and　the　guidelines　for　selecting　the　parameters　are　given.　Finally,　experimental　studies　of　the　proposed　SASOA-FNN　have　been　performed　on　several　nonlinear　systems　to　verify　the　effectiveness.　The　comparison　with　other　existing　methods　has　been　made,　and　it　demonstrates　that　the　proposed　SASOA-FNN　is　of　better　performance.