为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯...为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯变异策略、螺旋波动搜索等改进FLA。首先用体感温度公式对气象数据进行特征增强处理,其次通过IFLA对CNN-BiLSTM网络进行超参数优化,最后由CNNBiLSTM对数据进行特征提取并输出负荷预测结果。通过对2022年3月湖南某地居民用电负荷数据集进行仿真实验,实验结果表明,IFLA-CNN-BiLSTM预测模型输出的均方根误差为1.305、平均绝对误差为0.882、平均绝对百分数误差为2.558%、决定系数分别为0.989,验证了该模型在实际应用环境下的泛化性及可靠性。展开更多
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol...Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.展开更多
The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,wh...The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,whilst usually labor-intensive and time-consuming.This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method.The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method.The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.展开更多
文摘为准确预测电力负荷对优化发电和调度计划的影响,提升经济效益,保障电网安全运行,提出一种基于体感温度和改进菲克定律算法(improved Fick’s law algorithm,IFLA)优化CNN-BiLSTM的短期电力负荷预测模型。采用Logistic映射、柯西-高斯变异策略、螺旋波动搜索等改进FLA。首先用体感温度公式对气象数据进行特征增强处理,其次通过IFLA对CNN-BiLSTM网络进行超参数优化,最后由CNNBiLSTM对数据进行特征提取并输出负荷预测结果。通过对2022年3月湖南某地居民用电负荷数据集进行仿真实验,实验结果表明,IFLA-CNN-BiLSTM预测模型输出的均方根误差为1.305、平均绝对误差为0.882、平均绝对百分数误差为2.558%、决定系数分别为0.989,验证了该模型在实际应用环境下的泛化性及可靠性。
基金Natural Science Foundation of Shandong Province,China(Grant No.ZR202111230202).
文摘Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.
基金This work was supported in part by the National Natural Science Foundation of China(Grant 61663039)the National Natural Science Foundation of China(Grant 51775185)Equipment and materials for the research were provided by the Natural Science Foundation of Ningxia Hui Autonomous Region(Grant 2020AAC03008).
文摘The loss-on-drying method has been widely used as a standard approach for measuring the moisture content of high-moisture materials such as solid and semi-solid foods.Loss-on-drying method provides reliable results,whilst usually labor-intensive and time-consuming.This paper presents a novel algorithm for predicting the moisture content of meats based on the loss-on drying method.The proposed approach developed a drying kinetics model of meats based on Fick’s Second Law and designed a prediction algorithm for meat moisture content using the least-squares method.The predicted results were compared with the official method recommended by the Association of Official Analytical Chemists(AOAC).When the moisture content of meat samples(beef and pork)was varied from 69.46%to 74.21%,the relative error of the meat moisture content(MMC)calculated by the proposed algorithm was 0.0017-0.0117,the absolute errors were less than 1%.The testing time was about 40.18%-56.87%less than the standard detection procedure.
