Name
Abstract | ||
|---|---|---|
The classical fuzzy system modeling methods have been typically developed for the single task modeling scene, which is essentially not in accordance with many practical applications where a multi-task problem must be considered for the given modeling task. Although a multi-task problem can be decomposed into many single-task sub-problems, the modeling results indeed tell us that the individual modeling approach will not be very suitable for multi-task problems due to the ignorance of the inter-task latent correlation between different tasks. In order to circumvent this shortcoming, a multi-task Takagi-Sugeno-Kang fuzzy system model is proposed based on the classical L2-norm Takagi-Sugeno-Kang fuzzy system in this paper. The proposed model cannot only take advantage of independent information of each task, but also make use of the inter-task latent correlation information effectively, resulting to obtain better generalization performance for the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multi-task fuzzy system model in multi-task modeling scenarios. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.ins.2014.12.007 | Inf. Sci. |
Keywords | Field | DocType |
multi task learning | Neuro-fuzzy,Fuzzy classification,Defuzzification,Fuzzy set operations,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy control system,Fuzzy associative matrix,Fuzzy number,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
298 | C | 0020-0255 |
Citations | PageRank | References |
9 | 0.48 | 40 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yizhang Jiang | 1 | 382 | 27.24 |
| Zhaohong Deng | 2 | 647 | 35.34 |
| Fu Lai Chung | 3 | 1534 | 86.72 |
| Shitong Wang | 4 | 1485 | 109.13 |