发表机构
Sichuan University; Southwest University of Science and Technology; Hydrogen Energy and Multi-Energy Complementary Microgrid Engineering Technology Research Center of Sichuan Province; Institute of Acoustics, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Pontifical Catholic University of Rio de Janeiro (PUC-Rio)（四川大学; 西南科技大学; 四川省氢能及多能互补微网工程技术研究中心; 中国科学院声学研究所; 中国科学院大学; 里约热内卢天主教大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出高斯-三角函数泛函连接神经网络（GTFLN）滤波器及其优化版本OGTFLN，用于非线性系统辨识、回声消除和主动噪声控制，通过理论分析和仿真验证了其优于现有基准的性能。
AI 中文摘要
本文提出了一种基于高斯函数的三角函数泛函连接人工神经网络（GTFLN）滤波器，用于参数线性非线性滤波。与自适应指数TFLN（AETFLN）滤波器相比，GTFLN滤波器提供了平滑且局部化的基函数，并降低了计算复杂度，其建模优势通过平滑性、再生核希尔伯特空间、逼近误差和算子理论性质在理论上得以确立。为了最大化建模性能，推导了GTFLN滤波器的优化缩放参数，从而产生了优化的GTFLN（OGTFLN）滤波器。将最小均方（LMS）自适应应用于GTFLN和OGTFLN滤波器进行非线性系统辨识，分别产生了GTFLMS和OGTFLMS算法。此外，分析了GTFLN滤波器的理论稳态超额均方误差。仿真验证了理论分析的有效性，并展示了GTFLN和OGTFLN滤波器在非线性系统辨识和非线性声学回声消除中相对于参数线性基准的改进性能。基于GTFLN滤波器，提出了滤波-g LMS（FgLMS）算法用于非线性主动噪声控制。仿真结果表明，与基准相比，该算法在稳定性和降噪性能上均有改进。
英文摘要
This paper proposes a Gaussian function-based trigonometric functional link artificial neural network (GTFLN) filter for linear-in-the-parameters nonlinear filtering. Compared with the adaptive exponential TFLN (AETFLN) filter, the GTFLN filter provides smooth and localized basis functions with reduced computational complexity, where modeling advantages are theoretically established through the smoothness, reproducing kernel Hilbert space, approximation error, and operator theory properties. To maximize the modeling performance, an optimized scaling parameter for the GTFLN filter is derived, yielding the optimized GTFLN (OGTFLN) filter. Least mean square (LMS) adaptation is applied to the GTFLN and OGTFLN filters for nonlinear system identification, resulting in the GTFLMS and OGTFLMS algorithms, respectively. Moreover, the theoretical steady-state excess mean-square error of the GTFLN filter is analyzed. Simulations validate the effectiveness of the theoretical analysis and demonstrate the improved performance of the GTFLN and OGTFLN filters over the linear-in-the-parameters benchmarks in nonlinear system identification and nonlinear acoustic echo cancellation. Based on the GTFLN filter, the filtered-g LMS (FgLMS) algorithm is proposed for nonlinear active noise control. Simulations demonstrate improved stability and noise reduction performance compared to the benchmarks.
Comments16 pages, 9 figures
