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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Multikernel Least Mean Square Algorithm
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Multikernel Least Mean Square Algorithm

机译：多核最小均方算法

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摘要

The multikernel least-mean-square algorithm is introduced for adaptive estimation of vector-valued nonlinear and nonstationary signals. This is achieved by mapping the multivariate input data to a Hilbert space of time-varying vector-valued functions, whose inner products (kernels) are combined in an online fashion. The proposed algorithm is equipped with novel adaptive sparsification criteria ensuring a finite dictionary, and is computationally efficient and suitable for nonstationary environments. We also show the ability of the proposed vector-valued reproducing kernel Hilbert space to serve as a feature space for the class of multikernel least-squares algorithms. The benefits of adaptive multikernel (MK) estimation algorithms are illuminated in the nonlinear multivariate adaptive prediction setting. Simulations on nonlinear inertial body sensor signals and nonstationary real-world wind signals of low, medium, and high dynamic regimes support the approach.
机译：引入了多核最小均方算法来自适应估计矢量值的非线性和非平稳信号。这是通过将多元输入数据映射到时变矢量值函数的希尔伯特空间来实现的，希尔伯特空间的内积（内核）以在线方式组合在一起。所提出的算法配备有确保有限字典的新颖的自适应稀疏化准则，并且计算效率高并且适用于非平稳环境。我们还展示了拟议的向量值重现内核Hilbert空间充当多核最小二乘算法类特征空间的能力。在非线性多元自适应预测设置中阐明了自适应多核（MK）估计算法的优势。对低，中和高动态范围的非线性惯性人体传感器信号和非平稳现实世界风信号的仿真均支持该方法。

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