Fuzzy　neural　network　has　been　considered　as　an　effective　model　to　apply　in　many　applications.　However,　due　to　the　training　mode　based　on　minimizing　the　mean　squared　error,　the　typical　fuzzy　neural　network　suffers　from　poor　robustness　for　disturbances.　To　overcome　this　problem,　a　robust　fuzzy　neural　network　with　α-divergence　is　designed　and　analyzed　in　this　paper.　First,　a　cost　function　based　on　α-divergence　is　developed　to　describe　the　discrepancy　between　the　real　output　and　fuzzy　neural　network　output.　Then,　a　training　mode,　which　minimizes　the　above　function,　can　reduce　the　sensibility　of　disturbances　to　improve　the　robustness　of　fuzzy　neural　network.　Second,　an　adaptive　learning　algorithm　is　employed　to　adjust　the　parameter　of　fuzzy　neural　network.　Then,　the　proposed　fuzzy　neural　network　is　able　to　obtain　fast　convergence　in　the　learning　process.　Finally,　some　benchmarks　are　used　to　test　the　merits　of　fuzzy　neural　network.　The　simulation　results　illustrate　that　the　proposed　fuzzy　neural　network　can　achieve　good　robustness.　©　2021　IEEE.