ID 原文 译文
2653 然后利用非平衡参数方法对基础的标签置信度矩阵进行改进，构建出一个非平衡的标签补全矩阵， Secondly, the basic label confidence matrix is improvedto construct non-equilibrium label completion matrix by the non-equilibrium parameter.
2654 最后为了学习获得更加准确的标签置信度矩阵，将非平衡化的标签补全矩阵与核极限学习机进行联合学习，依此解决多标签分类问题。 Finally, in order to learn to obtain amore accurate label confidence matrix, the non-equilibrium label completion matrix is introduced with the kernel extremelearning machine to solve the multi-label classification problem.
2655 提出的算法在公开的多个基准多标签数据集中的实验结果表明，KELM-NeLC 算法较其他对比的多标签学习算法有一定优势，使用统计假设检验进一步说明所提出算法的有效性。 The experimental results of the proposed algorithm in the o-pening benchmark multi-label datasets show that the KELM-NeLC algorithm has some advantages over other comparative multi-label learning algorithms and the statistical hypothesis test further illustrates the effectiveness of the proposed algo-rithm.
2656 将分段 Logistic 映射作为局部混沌映射，引入到二维耦合映像格子模型中，构造了一种具有复杂动力学特性的混沌模型。 Using the piecewise logistic map (PLM ) as a local chaotic map for the 2D coupled map lattices(CML), this paper proposes a chaotic model with complex dynamic behavior.
2657 从密码学应用出发，深入分析了该模型中参数设置对 Lyapunov 指数、分岔、遍历区间和概率密度分布等特性的影响。 The influence of some parameters to themodel features, such as the Lyapunov exponent(LE), bifurcation, ergodicity, and probability density distribution, is ana-lyzed from the view of cryptographic applications.
2658 分析的结果为将该模型应用于保密通信的参数设置提供了理论依据。 The analysis results provide the theoretic evidence for the model to con-figure parameters in the secure communication.
2659 在此基础上，通过引入状态值偏移量，解决了该模型状态值概率密度分布不均的问题， Moreover, an offset is introduced to adjust the status value of lattices, which improve the ununiformity of probability density of status value.
2660 研究结果表明本文模型具有良好的性能，为将其应用于混沌保密通信方案设计提供了基础与条件。 The research results show that the proposed modelhas good performance, and provide good foundation and conditions for the research of designing secure communication scheme based on this model.
2661 终点碳含量是决定钢质量的关键因素，是转炉炼钢过程中需要控制的核心变量之一。 The final carbon content is the key factor in determining the quality of steel, and is one of the core varia-bles to be controlled in the process of converter steel-making.
2662 本文建立了一种基于莱维飞行的鲸鱼优化算法(Levy Whale Optimization Algorithm，LWOA)和最小二乘向量机(Least Squares SupportVector Machine，LSSVM)的钢水终点碳含量综合预测模型。 Based on the Levy whale optimization algorithm (LWOA)and least squares support vector machine (LSSVM), a comprehensive prediction model of carbon content at the end of thesteel-making process is established.