Wu Bo, Zhou Fengbo
School of Mechanical and Energy Engineering, Shaoyang University, Shaoyang, China.
Hunan Province Key Laboratory of Southwest, Hunan Academician Workstation, School of Information Science and Engineering, Shaoyang University, Shaoyang, China.
Front Chem. 2024 Sep 5;12:1409527. doi: 10.3389/fchem.2024.1409527. eCollection 2024.
A novel neural network adaptive filter algorithm is proposed to address the challenge of weak spectral signals and low accuracy in micro-spectrometer detection. This algorithm bases on error backpropagation (BP) and least mean square (LMS), introduces an innovative BP neural network model incorporating instantaneous error function and error factor to optimize the learning process. It establishes a network relationship through the input signal, output signal, error and step factor of the adaptive filter, and defines a training optimization learning method for this relationship. To validate the effectiveness of the algorithm, experiments were conducted on simulated noisy signals and actual spectral signals. Results show that the algorithm effectively denoises signals, reduces noise interference, and enhances signal quality, the SNR of the proposed algorithm is 3-4 dB higher than that of the traditional algorithm. The experimental spectral results showed that the proposed neural network adaptive filter algorithm combined with partial least squares regression is suitable for simultaneous detection of copper and cobalt based on ultraviolet-visible spectroscopy, and has broad application prospects.
提出了一种新型神经网络自适应滤波算法，以应对微型光谱仪检测中光谱信号微弱和精度低的挑战。该算法基于误差反向传播（BP）和最小均方（LMS），引入了一种结合瞬时误差函数和误差因子的创新型BP神经网络模型来优化学习过程。它通过自适应滤波器的输入信号、输出信号、误差和步长因子建立网络关系，并为这种关系定义了一种训练优化学习方法。为验证该算法的有效性，对模拟噪声信号和实际光谱信号进行了实验。结果表明，该算法能有效对信号进行去噪，减少噪声干扰，提高信号质量，所提算法的信噪比高于传统算法3 - 4dB。实验光谱结果表明，所提神经网络自适应滤波算法与偏最小二乘回归相结合适用于基于紫外可见光谱的铜和钴的同时检测，具有广阔的应用前景。
