Abstract
This paper presents a variable step-size Least Mean Square (LMS) algorithm based on cloud model which is a new cognitive model for uncertain transformation between linguistic concepts and quantitative values. In this algorithm, we use the error differences between two adjacent iteration periods to qualitatively estimate the state of the algorithm, and translate it into a propriety step-size in number according to the linguistic description of basic principle of variable step-size LMS. Simulation results show that the proposed algorithm is able to improve the steady-state performance while keeping a better convergence rate. We also apply this new algorithm to the multiuser interference cancellation, and results are also satisfied.
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He, W., Li, D., Chen, G., Zhang, S. (2010). A Variable Step-Size LMS Algorithm Based on Cloud Model with Application to Multiuser Interference Cancellation. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_86
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DOI: https://doi.org/10.1007/978-3-642-16248-0_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
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