Hermite Functional Link Artificial-Neural-Network-Assisted Adaptive Algorithms for IoV Nonlinear Active Noise Control
Hermite Functional Link Artificial-Neural-Network-Assisted Adaptive Algorithms for IoV Nonlinear Active Noise Control
复制标题
基于Hermite函数链人工神经网络的车联网非线性主动噪声控制
DOI：
10.1109/jiot.2020.2989761
复制
发表时间：
2020-09
影响因子：
10.6
通讯作者：
Kai-Li Yin;Yifei Pu;Lu Lu-Lu
中科院分区：
文献类型：
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作者：
Kai-Li Yin;Yifei Pu;Lu Lu-Lu
The Internet of Vehicles (IoV) plays a central role in intelligent transportation systems. Components, such as motor and transmission in the vehicle may produce noise, which seriously affects comfort. Therefore, vehicle manufacturers attach great importance to active noise control (ANC) technology. However, such an ANC system may have some nonlinear distortions in practical, thereby the nonlinear ANC (NANC) system is warranted. Moreover, we consider using IoV for rational resource allocation and record historical data for fault diagnosis, early warning, etc. So far, no work on NANC in the IoV environment is reported. In this article, based on the Hermite polynomial, a class of functional link artificial neural network (FLANN) algorithms is developed for NANC. The first proposed algorithm, called filtered-h least mean ${\mathcal {L}}_{p}$ -norm (FhLMP), incorporates the ${\mathcal {L}}_{p}$ -norm to obtain reliable performance. To further enhance the performance, the recursive FhLMP (RFhLMP) and hyperbolic recursive FhLMP (HRFhLMP) algorithms are designed by formulating two recursive structures. The proposed RFhLMP algorithm takes the filter output as part of the input and is expanded by the Hermite FLANN. The HRFhLMP algorithm activates the output by a hyperbolic tangent function and then recursively returns the activated output to the filter input. Simulations verify the improvement of the proposed algorithms for the NANC system.
