微型电脑应用

为了提高企业财务风险的预测能力，应对快速经济发展背景下面临的机遇和挑战，提出基于改进稀疏降噪声自编码(Sparse De-noising Auto encoder, SDAE)神经网络的财务风险预警算法。算法中加入了降噪特性，优化了数据特征的鲁棒性，提升了SDAE模型的数据泛化能力。通过PSO算法优化权值和阈值的初始设置，进一步提高了模型的预测精度。实验结果表明，神经网络用于财务风险预测是有效的，改进SDAE财务风险预测模型在保持神经网络预测优势基础上，增强了模型的鲁棒性和初始权阈值设置的合理性，有效提高了预测模型的预测性能。

In order to improve the predictive ability of corporate financial risks, to cope with the opportunities and challenges faced by rapid economic development, a financial risk early warning algorithm based on improved sparse de-noising auto encoder(SDAE) neural network is proposed. Based on maintaining the prediction advantage of neural network, the robustness of data features is optimized and the data generalization ability of SDAE is improved through the addition of noise reduction characteristics, and the initial settings of weights and thresholds are optimized by the PSO algorithm, these further improve the prediction accuracy of the model. The experimental results show that, neural networks are effective for financial risk prediction, the improved SDAE model enhances the robustness of the model and the rationality of the initial weight threshold setting, while maintaining the neural network prediction advantage, effectively improving the predictive performance of predictive models.