基于主成分分析和深度反向传播神经网络的汽油质量预测方法

Proceedings of the 5th International Conference on Computer Science and Application Engineering Pub Date : 2021-10-19 DOI:10.1145/3487075.3487129

Zihao Wang, Huawen Yang, Liang Chen, Wen-Xin Chen

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引用次数: 1

摘要

提出了一种将主成分分析与深度反向传播神经网络模型相结合的方法来解决高纬度预测问题。应用该方法建立了汽油精制产品质量预测模型。仿真结果验证了该方法的有效性。

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A Principal Component Analysis and Deep Back-Propagation Neural Network-based Approach to Gasoline Quality Prediction

This paper proposes an approach that combines principal component analysis with a Deep Back-Propagation Neural Network model to solve high-latitude prediction problems. The approach is applied to establish a product quality prediction model for gasoline refinement. The simulation results have demonstrated effectiveness of the approach.

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Proceedings of the 5th International Conference on Computer Science and Application Engineering

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