山东农业大学学报（自然科学版）2025，Vol.56Issue(1)：93-100,8.DOI:10.3969/j.issn.1000-2324.2025.01.010
基于WOA-BP组合模型的芦笋价格预测研究
Asparagus Price Prediction Based on WOA-BP Combination Model
摘要
Abstract
As a high-value vegetable,the price trend prediction of asparagus is of great significance for market analysis and decision making.Asparagus price is affected by many factors,so the key to improve the accuracy of price prediction is to deeply analyze these factors.Asparagus price is affected by many factors,so the key to improve the accuracy of price prediction is to deeply analyze these factors.n this paper,we propose a combined model based on the combination of Whale Optimization Algorithm(WOA)and Back Propagation Neural Network(BP).In this study,Principal Component Analysis(PCA)is firstly used to reduce the dimension of the influencing factors,and then the multi-dimensional feature set after principal component analysis and the one-dimensional feature set after data fusion are respectively input into the BP neural network before and after optimization for prediction analysis.By comparing and analyzing the prediction performance of the models under different inputs,the experimental results show that the model optimized by WOA algorithm significantly improves the prediction effect.Specifically,compared with the traditional BP model,the WOA-BP combined model has the Root Mean Square Error(RMSE)increased by 2.431,the Mean Absolute Error(MAE)increased by 2.553,the Mean Absolute Percentage Error(MAPE)increased by 5.606,and the Coefficient of Determination(R²)increased by 0.131.In addition,compared with the BP-fusion model,the WOA-BP-fusion model has RMSE increased by 1.926,MAE increased by 1.638,MAPE increased by 5.539,and R² increased by 0.101.The results show that the WOA-BP combined model can more effectively capture the relationship between the input features and the asparagus price series after data fusion,significantly improve the prediction accuracy,and enhance the generalization ability and robustness of the model.The WOA optimization algorithm not only improves the prediction accuracy of the BP model,but also significantly enhances the responsiveness of the model to price changes in the data fusion process.关键词
鲸鱼优化算法/组合模型/主成分分析/多源数据融合Key words
Whale optimization algorithm/Composite model/Principal component analysis/Multi-source data fusion分类
信息技术与安全科学引用本文复制引用
杨洁,王俊美,张超..基于WOA-BP组合模型的芦笋价格预测研究[J].山东农业大学学报（自然科学版）,2025,56(1):93-100,8.基金项目
基于农业大数据芦笋价格数据库开发(381724) （381724）
肥城人工智能机器人及智慧农业服务平台(381387) （381387）
