Deep　neural　networks　have　achieved　breakthrough　improvement　in　various　application　fields.　Nevertheless,　they　usually　suffer　from　a　time-consuming　training　process　because　of　the　complicated　structures　of　neural　networks　with　a　huge　number　of　parameters.　As　an　alternative,　a　fast　and　efficient　discriminative　broad　learning　system　(BLS)　is　proposed,　which　takes　the　advantages　of　flat　structure　and　incremental　learning.　The　BLS　has　achieved　outstanding　performance　in　classification　and　regression　problems.　However,　the　previous　studies　ignored　the　reason　why　the　BLS　can　generalize　well.　In　this　article,　we　focus　on　the　interpretation　from　the　viewpoint　of　the　frequency　domain.　We　discover　the　existence　of　the　frequency　principle　in　BLS,　i.e.,　the　BLS　preferentially　captures　low-frequency　components　quickly　and　then　fits　the　high　frequencies　during　the　incremental　process　of　adding　feature　nodes　and　enhancement　nodes.　The　frequency　principle　may　be　of　great　inspiration　for　expanding　the　application　of　BLS.　©　2012　IEEE.