In　this　paper,　a　MISO　fuzzy　neural　network　algorithm　is　presented.　This　algorithm　consists　of　the　excellences　of　fuzzy　algorithm　and　neural　network　algorithm.　In　the　parameter　learning　phase　it　changes　the　parameters　based　on　the　Lyapunov　stability　theory　to　ensure　the　stability.　Meanwhile,　it　didn＇t　need　to　seek　the　whole　minimum　value　when　it　modifies　the　parameters.　So　the　algorithm　can　reach　the　stability　result　more　quickly　than　the　conventional　fuzzy　neural　algorithm.　The　analyses　of　theory　prove　the　stability　of　the　algorithm.　Then　we　use　this　algorithm　to　control　the　dissolved　oxygen　in　wastewater　treatment　process,　and　compares　with　the　conventional　fuzzy　neural　algorithm.　The　results　of　simulations　show　the　superiority　of　this　algorithm　and　nicer　robustness　in　the　process.