由于微电网蓄电池工作时的电力特性具有明显的非线性和不规则性,依靠传统数学方法难以准确估计其荷电状态(state of charge,SOC)。针对上述问题,构建了BP神经网络拓扑结构,并采用增强型学习率自适应算法对网络的传统学习模式加以改进,...由于微电网蓄电池工作时的电力特性具有明显的非线性和不规则性,依靠传统数学方法难以准确估计其荷电状态(state of charge,SOC)。针对上述问题,构建了BP神经网络拓扑结构,并采用增强型学习率自适应算法对网络的传统学习模式加以改进,学习时神经网络模型中各神经元间权值得到合理调整,并且提高了误差收敛效率。仿真结果表明,估计结果在预设精度要求的范围之内,平均误差不超过4%,证明经过优化学习算法的BP神经网络模型对蓄电池荷电状态的精确估计是有效可行的。展开更多
Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabele...Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.展开更多
文摘由于微电网蓄电池工作时的电力特性具有明显的非线性和不规则性,依靠传统数学方法难以准确估计其荷电状态(state of charge,SOC)。针对上述问题,构建了BP神经网络拓扑结构,并采用增强型学习率自适应算法对网络的传统学习模式加以改进,学习时神经网络模型中各神经元间权值得到合理调整,并且提高了误差收敛效率。仿真结果表明,估计结果在预设精度要求的范围之内,平均误差不超过4%,证明经过优化学习算法的BP神经网络模型对蓄电池荷电状态的精确估计是有效可行的。
基金Project (20121101004) supported by the Major Science and Technology Program of Shanxi Province,ChinaProject (20130321004-01) supported by the Key Technologies R&D Program of Shanxi Province,China+2 种基金Project (2013M530896) supported by the Postdoctoral Science Foundation of ChinaProject (2014021022-6) supported by the Shanxi Provincial Science Foundation for Youths,ChinaProject (80010302010053) supported by the Shanxi Characteristic Discipline Fund,China
文摘Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.
