When the information of partial data is missing

the classical fuzzy systems constructed based on this dataset will have the weak generalization abilities for this scene.In order to overcome this shortcoming

the fuzzy system with the transfer learning abilities

i.e.

transfer fuzzy system

is proposed.In the learning procedure

the transfer fuzzy system can learn not only from the data information in the current scene

but also from the existing useful historical knowledge.Based on this idea

a transfer learning mechanism.a specified

and L2-norm penalty based 0-order-TSK-transfer fuzzy system(0-L2-TSK-TFS)was proposed

and a transfer learning mechanism was introducde.The proposed method was verified by experiments on simulation data and real data

and shows better adaptability than traditional fuzzy modeling methods in the scene with information missing.