An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo i...An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function （PIRBF） and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network （MK-RPINN） for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.展开更多
文摘An accurate short-term wind speed prediction algorithm based on the efficient kernel ridge pseudo inverse neural network （KRPINN） variants is proposed in this paper. The use of nonlinear kernel functions in pseudo inverse neural networks eliminates the trial and error approach of choosing the number of hidden layer neurons and their activation functions. The robustness of the proposed method has been validated in comparison with other models such as pseudo inverse radial basis function （PIRBF） and Legendre tanh activation function based neural network, i.e., PILNNT, whose input weights to the hidden layer weights are optimized using an adaptive firefly algorithm, i.e., FFA. However, since the individual kernel functions based KRPINN may not be able to produce accurate forecasts under chaotically varying wind speed conditions, a linear combination of individual kernel functions is used to build the multi kernel ridge pseudo inverse neural network （MK-RPINN） for providing improved forecasting accuracy, generalization, and stability of the wind speed prediction model. Several case studies have been presented to validate the accuracy of the short-term wind speed prediction models using the real world wind speed data from a wind farm in the Wyoming State of USA over time horizons varying from 10 minutes to 5 hours.
文摘针对盾构姿态预测模型存在易过拟合、预测精度低的问题,提出一种基于融合注意力机制的盾构姿态组合预测模型。为强化有效特征的提取,抑制冗余特征信息的表达,引入基于选择性卷积核网络(selective kernel networks,SKNet)的特征注意力机制提取网络,消除固定尺寸卷积核带来的限制,并自适应形成带有注意力的特征映射。为更好地捕捉长期信息和特征模式,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)、门控循环单元(gated recurrent unit, GRU)得到2组隐含输出结果,再利用多头注意力机制,捕获组合模型输出的隐含特征与模型输出的盾构姿态之间的依赖关系,进一步提高预测模型对重要隐含特征的信息抓捕能力;同时,为解决地质勘察钻孔数据连续性差、精确性不足,难以应用于机器学习模型训练的问题,将基于人工先验知识的二级特征引入模型特征输入,提升模型对地层信息的感知能力。最后,基于广州地铁12号线官洲站—大学城北站盾构实例,对模型不同参数结构下的性能进行研究,并进行对比试验验证模型性能,采用可解释性试验评估特征对预测结果的影响。试验结果表明,相比其他预测模型,所提出的预测模型优越性更好,预测精度更高,解决了长时间序列高特征维度数据在传统模型下易过拟合且预测精度较低的问题。
