Name
Abstract | ||
|---|---|---|
LMS (Least Mean Square) algorithm has become a very commonly used algorithm in the field of adaptive filtering due to its many advantages such as easy calculation, easy application and strong robustness. The main idea in the LMS algorithm is to automatically adjust the parameter values of the filter to minimize its Mean Square Error (MSE). The traditional fixed-step LMS algorithm has the problem that the error accuracy and the convergence speed cannot be coordinated, so many improved variable-step LMS algorithms have emerged. This article introduces several new improved variable-step LMS algorithms through examples. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CACRE54574.2022.9834206 | 2022 7th International Conference on Automation, Control and Robotics Engineering (CACRE) |
Keywords | DocType | ISBN |
improved variable-step LMS algorithm | Conference | 978-1-6654-6669-1 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin Xie | 1 | 0 | 0.34 |
| Haili Zhao | 2 | 0 | 0.34 |
| Chengjun Tian | 3 | 0 | 0.34 |
| Yuyu Wang | 4 | 0 | 0.34 |