We propose an approach to reconstruct spectrum using artificial neural networks (ANNs) instead of directly solving a matrix equation using calibration coefficients. ANNs are particularly effective in reconstructing spectra in noise environment by learning the relationship between inputs and outputs with large amount of data training. There are several different training methods for ANNs. Compared with scaled conjugate gradient algorithm and Levenberg–Marquardt algorithm, Bayesian regularization (BR) algorithm is demonstrated to be a better training algorithm for spectral reconstruction. We also compare the spectral reconstruction of BR algorithm and that of the traditional algorithms. Experimental results indicate that the spectral reconstruction of BR algorithm is nearly in line with that measured by a commercial spectrometer. Obvious deviations are occurred in the spectral reconstruction of the traditional algorithms due to inevitable background noise, rounding errors, and temperature variations. Therefore, spectral reconstruction using ANNs with a train method of BR algorithm is a more suitable choice for the disorder dispersion spectrometer.

中文翻译：

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利用人工神经网络优化无序色散光谱仪

我们提出了一种使用人工神经网络（ANN）重建频谱的方法，而不是使用校准系数直接求解矩阵方程。人工神经网络通过大量数据训练学习输入和输出之间的关系，在噪声环境中重建频谱方面特别有效。人工神经网络有多种不同的训练方法。与缩放共轭梯度算法和 Levenberg-Marquardt 算法相比，贝叶斯正则化（BR）算法被证明是一种更好的谱重建训练算法。我们还比较了BR算法和传统算法的谱重建。实验结果表明，BR算法的光谱重建与商用光谱仪测量的结果基本一致。由于不可避免的背景噪声、舍入误差和温度变化，传统算法的光谱重建会出现明显的偏差。因此，使用 ANN 和 BR 算法训练方法进行光谱重建是无序色散光谱仪更合适的选择。