Fuzzy Transfer Learning Algorithm with Knowledge-Task Matching

Fuzzy transfer learning has the potential to enhance the performance of a task in the target scene by leveraging knowledge from the source scene. However, the existence of scene discrepancies between the two scenes poses two major problems for fuzzy transfer learning: 1) the mismatch between the distribution of source knowledge and the target task, and 2) the mismatch between the learning ability of the target task and the source knowledge. To address these challenges, we propose a fuzzy transfer learning algorithm called Knowledge-Task Matching Fuzzy Transfer Learning (KTM-FTL), which aims to adaptively match the source knowledge and the target task. Firstly, we develop a knowledge matching mechanism that balances matching accuracy and diversity to reconstruct the knowledge from the source scene. This process enables the source knowledge to align more accurately with the requirements of the target scene's task. Secondly, we design a task matching mechanism that allows for learning the source knowledge at different levels of granularity. This approach enables the learning ability of the target Feedforward Neural Network (FNN) to match the complexity of the source knowledge, thereby improving its generalization performance. Thirdly, we conduct theoretical analysis of the proposed KTM-FTL, including an examination of its computational complexity and error bound. These analyses provide valuable insights for the successful application of KTM-FTL. Finally, we evaluate the performance of the proposed KTM-FTL on several benchmark problems as well as real-world problems. The experimental results demonstrate the significant improvements achieved by KTM-FTL when compared to state-of-the-art algorithms.