ID 原文 译文
17265 针对具有无标度特性的网络模型，用FW-PSO算法对网络拓扑进行优化，在不同的攻击策略下分别从动态抗毁性和静态抗毁性分析优化前后网络的性能。 For the scale-free network model, FW-PSO algorithm is used to optimize the network topology. Under different attackstrategies, the performance of the network before and after the optimization is analyzed from dynamic andstatic invulnerability respectively.
17266 仿真实验表明，与其他同类算法相比，经过该文所提算法优化后的无线传感网络的动态和静态抗毁性能都有明显提升。 Simulation results show that, compared with other similar algorithms, the dynamic and static invulnerability of wireless sensor network optimized by the proposed algorithm has obvious advantages.
17267 脉冲噪声会导致非负算法在迭代过程中存在过大的误差值，进而破坏算法的稳定性使其性能严重下降，对此该文提出一种基于Sigmoid框架的非负最小均方算法(SNNLMS)。 Impulsive noise causes nonnegative algorithms to yield excessive error during iterations, which willdamage the stability of the algorithm and causes performance degradation. In the paper, a NonNegative LeastMean Square algorithm based on the Sigmoid framework (SNNLMS) is proposed.
17268 该算法将传统的非负代价函数嵌入Sigmoid框架中得到新的代价函数，新的代价函数具有抑制脉冲噪声影响的特性。 The algorithm embeds the conventional nonnegative cost function into the Sigmoid framework to receive a new cost function. In the paper, a NonNegative LeastMean Square algorithm based on the Sigmoid framework (SNNLMS) is proposed. The newcost function has the characteristics of suppressing the impact of impulse noise.
17269 此外，为了增强SNNLMS算法在稀疏系统识别问题上的鲁棒性，该文还提出基于反比例函数的反比例Sigmoid非负最小均方算法(IP-SNNLMS)。 In addition, in order to enhancethe robustness of the SNNLMS algorithm under sparse system identification, the Inversely-ProportionalSigmoid NonNegative Least Mean Square (IP-SNNLMS) is proposed based on the inversely-proportionalfunction.
17270 仿真结果表明SNNLMS算法有效地解决了脉冲噪声造成的失调问题； Simulation results demonstrate that the SNNLMS algorithm effectively solves the problem ofmisadjustment caused by impulsive noise.
17271 IP-SNNLMS增强了算法鲁棒性，改进了算法在稀疏系统识别问题中收敛速率上的缺陷。 IP-SNNLMS enhances the robustness of the algorithm and improvesthe defect of the convergence rate of the SNNLMS algorithm under the sparse system identification.
17272 卷积压缩感知是近年来兴起的新型压缩感知技术。 Convolutional compressed sensing emerging in recent years is a new type of compressed sensingtechnology.
17273 卷积压缩感知选用循环矩阵作为测量矩阵，其采样可以简化为卷积的过程，因此大大降低算法复杂度。 By using cyclic matrix as measurement matrices, the sampling in convolutional compressed sensingcan be simplified into convolution process, thus the complexity of the algorithm is greatly reduced.
17274 该文基于分圆类构造适用于卷积压缩感知的测量矩阵，测量值通过利用确定性序列循环卷积信号，然后进行随机2次采样获得。 In this paper, a construction of measurement matrices for convolutional compressed sensing based on cyclotomic classes is proposed. The measurements are obtained by using the circulate convolution signal of the deterministicsequence and then by random subsampling.