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首页> 外文期刊>IEEE Transactions on Information Theory >Hammerstein System Identification With the Nearest Neighbor Algorithm
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Hammerstein System Identification With the Nearest Neighbor Algorithm

机译：Hammerstein系统的最近邻算法识别

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摘要

The nonlinear characteristic in a Hammerstein system, i.e., a system in which a nonlinear memoryless subsystem and a linear dynamic are connected in a cascade, is recovered with the nonparametric nearest neighbor regression estimate. The a priori information is nonparametric, both the nonlinear characteristic and the impulse response are completely unknown and can be of any form. Local and global properties of the estimate are examined. Whatever the probability density of the input signal, the estimate converges at every continuity point of the characteristic as well as in the global sense. We derive the asymptotic bias and variance of the proposed estimate. As a result, the optimal rate of convergence is established that additionally is independent of the shape of the input density. Results of numerical simulations are also presented.
机译：利用非参数最近邻回归估计值，可以恢复Hammerstein系统（即其中非线性无记忆子系统和线性动力学级联连接的系统）中的非线性特性。先验信息是非参数的，非线性特性和脉冲响应都是完全未知的，并且可以是任何形式。检查估计的局部和全局属性。无论输入信号的概率密度如何，估计值都会在特性的每个连续点以及全局意义上收敛。我们推导所提出估计的渐近偏差和方差。结果，建立了最佳收敛速度，该最佳收敛速度还独立于输入密度的形状。还提供了数值模拟的结果。

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