Multilayer Stacked Evolving Fuzzy System Combined With Compressed Representation Learning

作者：Hui Huang, Hai-Jun Rong, Zhao-Xu Yang, Chi‐Man Vong · 发表于：IEEE Transactions on Fuzzy Systems · 年份：2023 · DOI：10.1109/tfuzz.2023.3347699 · 被引用次数：6 · 研究领域：Neural Networks and Applications、Machine Learning and ELM、Face and Expression Recognition

In order to process the high-dimensionally complicated problems, the intelligence systems need to go deeper to learn high-level data representation. In this article, based on the stacked generalization principle, a multilayered stacked learning system is proposed. Like the deep networks, the proposed system is organized in a layer-by-layer way with evolving fuzzy systems (EFSs) as its base-building units. Each EFS is designed as an autoencoder (AE) to learn the simpler data representation, then multiple EFS-based AEs are stacked in a feedforward manner for learning more complex data representation. Since there exists the redundant or irrelevant information in the new data representation, which may limit high generalization, a novel feature compressing layer is followed by each EFS-based AE to refine the new data representation and reduce the feature dimension via very sparse random projection (VSRP). The proposed system is featured in the following merits: 1) more expressive and complex data representation can be learned in a stacked multilayer architecture; 2) manually tuning the structure of each AE is avoided in every layer since the EFS can self-adapt both its structure and parameters online; 3) the resulting system can avoid overfitting and obtain higher generalization by removing redundant or irrelevant information via VSRP; 4) the steady-state error analysis of the proposed system is studied based on the separable approximation property, which guarantees the learning c...