摘要
厌氧消化是可再生能源生产中一种具有前景的技术,沼气是由有机废物通过厌氧消化产生的生物能源,预测厌氧消化产生的沼气产量并进行管控是必要的。设计了一种具有短期记忆的多量子滤波器量子卷积神经网络,利用参数化变分量子电路接受数据“时间窗”以模拟短期记忆,并在多量子滤波结构中舍弃过多的线路迭代和参数数量使其具有更高的表达性。在量子线路框架中,设计了最优的卷积、池化层线路,能够更好地提取特征因子中的隐藏状态;同时对废物管理数据进行严格的预处理,通过指数平滑去除特征中趋势和季节性。该算法的精度达到了83.30%,比CNN模型精度提升了8%,RMSE和MAE值也均优于ANN、KNN、CNN等经典模型。
Anaerobic digestion is a promising technology in the production of renewable energy,in which biogas is the biological energy generated by anaerobic digestion of organic waste.It is necessary to predict and control biogas yield from anaerobic digestion.A multi-quantum filter quantum convolutional neural network with short-term memory is designed.The designed network utilizes the parameterized variational quantum circuit to accept the data’time window’so as to simulate the short-term memory,and discards too much circuit iteration and parameter number of the multi-quantum filter to make the filter more expressive.In the quantum circuit framework,the optimal convolution and pooling layer circuits are designed to better extract the hidden states in the feature factors.At the same time,the waste management data are strictly preprocessed,and the trend and seasonality in the characteristics are removed by exponential smoothing.The accuracy of the proposed algorithm reaches 83.30%,which is 8%higher than that of the convolutional neural network model(CNN).The root mean square error(RMSE)and mean absolute error(MAE)values are also better than those of artificial neural network(ANN),Knearest neighbor(KNN)and CNN classical models.
作者
董玉民
侯栋
耿馨雨
胡万斌
DONG Yumin;HOU Dong;GENG Xinyu;HU Wanbin(School of Computer and Information Science,Chongqing Normal University Shapingba Chongqing 401331)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2022年第5期651-659,共9页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(61772295,61572270)
重庆市教委科学技术研究项目(KJZD-M202000501)
重庆市技术创新与应用发展项目(cstc2020jscxlyjsAX0002)
重庆市技术预见与制度创新项目(cstc2021jsyj-yzysbAX0011)。
关键词
厌氧消化
多量子滤波器
短期记忆
变分量子电路
anaerobic digestion
multi-quantum filter
short term memory
variational quantum circuit
