The　broad　learning　system　(BLS)　has　been　identified　as　an　important　research　topic　in　machine　learning.　However,　the　typical　BLS　suffers　from　poor　robustness　for　uncertainties　because　of　its　characteristic　of　the　deterministic　representation.　To　overcome　this　problem,　a　type-2　fuzzy　BLS　(FBLS)　is　designed　and　analyzed　in　this　article.　First,　a　group　of　interval　type-2　fuzzy　neurons　was　used　to　replace　the　feature　neurons　of　BLS.　Then,　the　representation　of　BLS　can　be　improved　to　obtain　good　robustness.　Second,　a　fuzzy　pseudoinverse　learning　algorithm　was　designed　to　adjust　the　parameter　of　type-2　FBLS.　Then,　the　proposed　type-2　FBLS　was　able　to　maintain　the　fast　computational　nature　of　BLS.　Third,　a　theoretical　analysis　on　the　convergence　of　type-2　FBLS　was　given　to　show　the　computational　efficiency.　Finally,　some　benchmark　and　practical　problems　were　used　to　test　the　merits　of　type-2　FBLS.　The　experimental　results　indicated　that　the　proposed　type-2　FBLS　can　achieve　outstanding　performance.　©　2013　IEEE.