0-Order-L2-Norm-Takagi-Sugeno-Kang Type Transfer Learning Fuzzy System
Shitong Wang · Dianzi xuebao · 2013
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.