A Fuzzy Transfer Reservoir Learning Machine Through Domain Enhancement on Multiple Sources

While transfer learning through source domain enhancement with the mix-up strategy on multiple sources is applied to reservoir computing (RC) related resource-constrained scenarios, this study aims at addressing two seldom-concerned phenomena: 1) the similarity degrees between the target domain and each of all the source domains may perhaps change after reservoir transformation so as to possibly change their similarity rankings <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">before</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after</i> that transformation; 2) the decision boundaries between classes may become more uncertain. In order to achieve this goal, a fuzzy transfer reservoir learning machine (FT-RLM) is proposed based on the well-known leaky integrator echo state network (LI-ESN). In particular, in order to determine which source domains should be enhanced by the mix-up strategy after reservoir transformation, with the theoretical derivation of the mix-up ratios for source domain selection, FT-RLM begins with the use of the mix-up strategy based on the calculated mix-up ratios for source domain enhancement. After that, in order to deal with uncertain decision boundaries between classes, FT-RLM takes the proposed transfer-learning-based fuzzy classifier called parametric-transfer-based Takagi–Sugeno–Kang fuzzy system (TSK-FS) which is trained on both the enhanced source domains and the target domain. Experimental results on real-world datasets validate the effectiveness of the proposed FT-RLM when faced with the above two phenomena in multiple source reservoir transfer learning scenarios under RC-related resource-constrained environments.