针对风电功率数据序列波动大、随机性强、非线性以及选取输入变量困难的问题,提出一种结合自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和双向长短期记忆网络(bidirec...针对风电功率数据序列波动大、随机性强、非线性以及选取输入变量困难的问题,提出一种结合自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和双向长短期记忆网络(bidirectional long short term memory,BiLSTM)的短期风电功率预测组合模型。通过CEEMDAN对原始功率数据序列进行分解及平稳化处理,并根据各分量样本熵值进行合并重构;利用偏自相关函数(partial autocorrelation function,PACF)计算各重构分量的滞后期数,以此确定各重构分量在BiLSTM网络模型中的最佳输入变量;根据各重构分量的预测值相加得到最终预测结果。实验结果表明,与几种传统的单一预测模型和组合预测模型相比,提出的模型具有更优的预测结果和更高的预测精度。展开更多
As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a mult...As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.展开更多
Text classification is a classic task innatural language process(NLP).Convolutional neural networks(CNNs)have demonstrated its effectiveness in sentence and document modeling.However,most of existing CNN models are ap...Text classification is a classic task innatural language process(NLP).Convolutional neural networks(CNNs)have demonstrated its effectiveness in sentence and document modeling.However,most of existing CNN models are applied to the fixed-size convolution filters,thereby unable to adapt different local interdependency.To address this problem,a deep global-attention based convolutional network with dense connections(DGA-CCN)is proposed.In the framework,dense connections are applied to connect each convolution layer to each of the other layers which can accept information from all previous layers and get multiple sizes of local information.Then the local information extracted by the convolution layer is reweighted by deep global-attention to obtain a sequence representation with more valuable information of the whole sequence.A series of experiments are conducted on five text classification benchmarks,and the experimental results show that the proposed model improves upon the state of-the-art baselines on four of five datasets,which can show the effectiveness of our model for text classification.展开更多
文摘针对风电功率数据序列波动大、随机性强、非线性以及选取输入变量困难的问题,提出一种结合自适应噪声完备集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和双向长短期记忆网络(bidirectional long short term memory,BiLSTM)的短期风电功率预测组合模型。通过CEEMDAN对原始功率数据序列进行分解及平稳化处理,并根据各分量样本熵值进行合并重构;利用偏自相关函数(partial autocorrelation function,PACF)计算各重构分量的滞后期数,以此确定各重构分量在BiLSTM网络模型中的最佳输入变量;根据各重构分量的预测值相加得到最终预测结果。实验结果表明,与几种传统的单一预测模型和组合预测模型相比,提出的模型具有更优的预测结果和更高的预测精度。
基金the National Natural Science Foundation of China(No.60905066)the Natural Science Foundation of Chongqing(No.cstc2018jcyjA0667)
文摘As optimization of parameters affects prediction accuracy and generalization ability of support vector regression(SVR) greatly and the predictive model often mismatches nonlinear system model predictive control,a multi-step model predictive control based on online SVR(OSVR) optimized by multi-agent particle swarm optimization algorithm(MAPSO) is put forward. By integrating the online learning ability of OSVR, the predictive model can self-correct and adapt to the dynamic changes in nonlinear process well.
基金supported by National Natural Science Foundation of China(61673079)Natural Science Foundation of Chongqing(cstc2018jcyjAX0160)。
文摘Text classification is a classic task innatural language process(NLP).Convolutional neural networks(CNNs)have demonstrated its effectiveness in sentence and document modeling.However,most of existing CNN models are applied to the fixed-size convolution filters,thereby unable to adapt different local interdependency.To address this problem,a deep global-attention based convolutional network with dense connections(DGA-CCN)is proposed.In the framework,dense connections are applied to connect each convolution layer to each of the other layers which can accept information from all previous layers and get multiple sizes of local information.Then the local information extracted by the convolution layer is reweighted by deep global-attention to obtain a sequence representation with more valuable information of the whole sequence.A series of experiments are conducted on five text classification benchmarks,and the experimental results show that the proposed model improves upon the state of-the-art baselines on four of five datasets,which can show the effectiveness of our model for text classification.
