ID 原文 译文
18145 DdrEA通过与当前较优的NSGA-II/AD, RVEA, MOMBI-II等多个超多目标进化算法进行实验对比，实验结果表明该文算法性能明显优于对比算法，能够有效平衡种群的收敛性和多样性。 Comparing with several high-dimensional andmulti-objective evolutionary algorithms (NSGA-II/AD, RVEA, MOMBI-II), the experimental results show thatthe performance of the proposed algorithm DdrEA is better than that of the comparison algorithm, and theconvergence and diversity of the population can be effectively balanced.
18146 为了降低核仿射投影P范数(KAPP)算法的计算量和存储容量，提高在输入信号强相关时KAPP算法的收敛速度和稳态性能，该文提出基于高斯核显性映射的核归一化解相关APP(KNDAPP-GKEM)算法。 In order to reduce the computation complexity and storage capacity of the Kernel Affine ProjectionP-norm (KAPP) algorithm, and improve the convergence rate and steady-state performance of the algorithmwhen the input signal is strongly correlated, a Kernel Normalization Decorrelated Affine Projection P-normalgorithm based on Gaussian Kernel Explicit Mapping (KNDAPP-GKEM) is proposed.
18147 该算法利用归一化解相关方法预先解除输入信号的相关性； The correlation of the input signal is eliminated in advance by the normalized correlation method.
18148 利用高斯核显式映射方法近似得到显式核函数，消除了对历史数据的依赖，解决了KAPP算法因结构不断生长导致的计算量和存储容量过大的问题。 The explicit kernel function isapproximated by Gaussian kernel explicit mapping method, which eliminates the dependence on historical dataand solves the problem that the computation and storage capacity of the KAPP algorithm are too high due tothe continuous growth of structure.
18149 α稳定分布噪声背景下的非线性系统辨识仿真结果表明，在输入信号强相关时KNDAPP-GKEM算法收敛速度快，非线性系统辨识稳态均方误差小，训练所需时间呈线性缓慢增长，有利于实际非线性系统辨识的应用。 The simulation results of nonlinear system identification under α-stable distribution noise environment show that when the training data scale is large, the KNDAPP-GKEM algorithm still maintains a fast convergence rate and the low identification mean square error of nonlinear system.Moreover, its training time is linearly and slowly increased, which is more conducive to the practical application of nonlinear system identification.
18150 该文针对工业控制系统安全，提出面向数控系统(NCS)网络安全保护技术框架， For the security of industrial control system, a framework for Numerical Control System(NCS)network security protection technology is proposed.
18151 选用国产密码系列算法中的SM2, SM3, SM4算法，设计并建立了数控网络(CNC)认证与验证模型(AUTH-VRF)，分内外两层为数控网络提供安全防护。 The SM2, SM3 and SM4 algorithms in the domestic cryptographic algorithms are used to design and establish the AUTHentication and VRFfication (AUTH-VRF)model of the Computerized Numerical Control(CNC) network, which provides security protection for both internal and external sides.
18152 外层为数控网络设备间通信与传输进行安全认证实现网段隔离，内层验证通信协议完整性以确保现场设备接收运行程序的正确性与有效性； The external side conducts the security authentication for communication andtransmission between CNC network devices to achieve network segment isolation. The internal side verifiescommunication protocol integrity to ensure that the operating procedures received by the field devices arecorrect and valid.
18153 通过基于SM2, SM3, SM4算法设计和部署的外层防护装置，为分布式数控(DNC)设备与数控系统之间的通信提供身份认证与文件加密传输； The external protection device designed and deployed based on the SM2, SM3 and SM4algorithms provides identity authentication and file encryption transmission for communication between theDistributed Numerical Control(DNC) device and the CNC system.
18154 同时针对工业控制网络的S7Comm工业通信协议数据，通过SM3算法验证专有工业协议数据完整性。 At the same time, for the proprietaryindustrial communication protocol data in the CNC network, the SM3 algorithm is used to verify its integrity.