ID	原文	译文
6074	在前馈控制器中,结合变刚度金属-橡胶缓冲弹簧、力矩测速反馈及梯度加载法,采用基于Sigmoid函数变平衡学习常数的权值调整算法,设计三维参考输入型神经网络结构。	In the feedforward controller, combined with the variable stiffness metal - rubber buffer spring, torque speed feedback and gradient loading method, based on the Sigmoid function to balance constant weight adjustment algorithm, neural network structure design of 3 d reference input type.
6075	在反馈控制器中,采用增量式PID控制解决积分项溢出问题,同时为神经网络提供训练学习样本,最后通过理论分析证明改进算法的收敛特性及闭环系统的稳定性。	In the feedback controller, using the integral item overflow incremental PID control to solve the problem, provide training for neural network learning samples at the same time, the last through the theoretical analysis proves the convergence property of the improved algorithm and the stability of the closed-loop system.
6076	仿真结果表明,该方法提高了系统的加载精度及在线实时控制能力,在一定程度上抑制了多余力矩干扰。	The simulation results show that this method is to increase the load of the system precision and on-line real-time control ability, to a certain extent, inhibit the surplus torque disturbance.
6077	在阵列失效单元压缩感知近场诊断方法中,缺乏观测矩阵是否满足约束等距特性的先验信息,因此采用l	Compression perception in array failure unit near field diagnosis method, the lack of the observation matrix is isometric characteristics meet the constraints of a priori information, so the l
6078	对于目标跟踪过程中的强机动问题,基于当前统计(current statistical,CS)模型和改进的强跟踪平方根容积卡尔曼滤波器(square-root cubature Kalman filter,SCKF),提出新的跟踪算法。	For strong maneuvering problem in target tracking process, based on the current statistics, the current statistical, CS) model and the modified strong tracking Kalman filter square root volume (the square - root cubature Kalman filter, SCKF), the new tracking algorithm is put forward.
6079	在CS模型和改进输入估计算法的基础上,引入加加速度估计,使得状态过程噪声与状态协方差矩阵相联系,实现模型的自适应调整。	In CS model and improve the input estimation algorithm, on the basis of introducing acceleration estimation, state of process noise associated with state covariance matrix, the implementation model of adaptive adjustment.
6080	从正交性原理出发,重新确定了渐消因子的引入位置,并提出了新的渐消因子计算形式,以克服传统渐消因子在雷达量测坐标系中的失效问题,从而构造强跟踪平方根容积卡尔曼滤波器。	From the orthogonality principle to determine the fading factor, the introduction of the position, and put forward a new form of fading factor calculation, in order to overcome the traditional fading factor to the failure problem in radar measurement coordinate system, so as to construct strong tracking kalman filter square root volume.
6081	另外,构造强机动检测函数,利用SCKF的输出来调整自适应CS模型中的机动频率。	In addition, the structure of strong maneuvering detection function, using SCKF output to adjust the maneuvering frequency of adaptive CS model.
6082	仿真结果表明,相比基于CS模型的多重渐消因子强跟踪SCKF算法、改进CS模型的强跟踪SCKF(SCKF-STF)算法和交互式多模型(interacting multiple-model,IMM)SCKF算法,所提算法具有更佳的目标机动适应性和跟踪精度;	Compared the simulation results show that the multiple fading factor model based on CS strong tracking SCKF algorithm, improved CS model of strong tracking SCKF (SCKF - STF) algorithm and the interactive multiple model (interacting multiple model, the IMM) SCKF algorithm, the proposed algorithm has better adaptability target maneuver and tracking precision;
6083	相比于IMM-SCKF算法,实时性有明显改善。	Compared to the IMM - SCKF algorithm, real-time performance has improved significantly.