Affiliations: [a] Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China | [b] Henan Collaborative Innovation Center for Aviation Economy Development, Zhengzhou, Henan, China
Correspondence:
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Corresponding author: Yongqing Wang, Department of Computer Science and Applications, Zhengzhou Institute of Aeronautical Industry Management, Zhengzhou, Henan, China. E-mail: [email protected].
Abstract: In order to solve the problems faced in the transfer learning of artificial intelligence system modeling technology, a new method of TSK transfer learning fuzzy system was proposed to enhance knowledge transfer. The two key problems of the precursor learning and the post learning of the TSK type transfer fuzzy system were solved by using this method. And also a novel transfer fuzzy clustering method was proposed for solving the problem. At the same time, a post learning mechanism was proposed to enhance the ability of knowledge transfer which effectively improved the performance of the final model. The experimental results showed that the proposed method integrated the transfer clustering and the transfer fuzzy system modeling successfully, making the modeling process of the fuzzy system more intelligent with better learning ability. In addition, the proposed method provided a new research idea for the development of transfer learning in the field of intelligent modeling.
Keywords: Transfer learning, brain-computer integration, artificial intelligence, intelligent modeling
