We propose a normalized least mean squares algorithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted. Simulations show a competitive performance in comparison with other solutions, and validate our theoretical analysis.

中文翻译：

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一种参数选择简单的变步长自适应算法

我们提出了一种可变步长的归一化最小均方算法。与其他解决方案不同，它的计算成本低，只有三个易于选择的参数，并且可以轻松预测其稳态性能。与其他解决方案相比，模拟显示了具有竞争力的性能，并验证了我们的理论分析。