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A global optimization method for continuous time adaptive recursive filters
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DOI：10.1109/97.774864.png)
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摘要
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A major drawback of recursive adaptive filters based on gradient methods is that convergence to global minimum is not always achieved. This is due to a nonconvex mean square error (MSE) performance surface. This letter develops a continuous-time least mean square algorithm that converges to the global minimum with probability one.
Keyword:
adaptive recursive filters
global optimization
stochastic approximation
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9.6
论文数:
1.1W
被引数:
1.7W
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