一种控制SRM转矩脉动减小的算法

提出了一种改进的通过使用CMAC(CerebellarModelArticulationController———脑模型关节控制器 )神经网络控制变磁阻式电动机 (SRM)转矩脉动减小的算法。该算法在保持原LMS算法优点的基础上 ,能确保电机以低速运转时最小化转矩脉动、铜耗及电机以高速运转时最小化转矩脉动、铜耗、电流的变化率均产生最佳的电流曲线。

基于Bregman联合聚类与加权矩阵分解的融合推荐算法

针对当前大数据背景下推荐系统中所存在推荐效率低下、扩展性差、推荐质量不高等问题,提出一种基于Bregman联合聚类与加权矩阵分解的融合推荐算法(CO-CWMA)。首先,通过Bregman联合聚类挖掘出多样、不同层次的低秩评分子矩阵,组合不同约束与距离的聚类结果训练得到子模型,进而在各个模型的子矩阵上并发地进行矩阵分解,最后将各个子模型进行均值融合,提高推荐质量、效率与扩展性。在矩阵分解阶段采用SVD++算法,基于每个子矩阵中的评分分布计算加权策略,给予高频评分较大权值,在梯度下降阶段利用学习率函数控制学习率的更新。实验结果表明,与三种基线算法相比,该算法在均方根误差(RMSE)与平均绝对误差(MAE)上均有明显降低,即推荐质量有较大提升。

Tunnel face reliability analysis using active learning Kriging model——Case of a two-layer soils

This paper is devoted to the probabilistic stability analysis of a tunnel face excavated in a two-layer soil. The interface of the soil layers is assumed to be positioned above the tunnel roof. In the framework of limit analysis, a rotational failure mechanism is adopted to describe the face failure considering different shear strength parameters in the two layers. The surrogate Kriging model is introduced to replace the actual performance function to perform a Monte Carlo simulation. An active learning function is used to train the Kriging model which can ensure an efficient tunnel face failure probability prediction without loss of accuracy. The deterministic stability analysis is given to validate the proposed tunnel face failure model. Subsequently, the number of initial sampling points, the correlation coefficient, the distribution type and the coefficient of variability of random variables are discussed to show their influences on the failure probability. The proposed approach is an advisable alternative for the tunnel face stability assessment and can provide guidance for tunnel design.