关于QZSI MPC的控制的文章

谱分析；功率谱 通过matlab对功率谱生成进行编码，从而简单快捷的生成功率谱图。(power respectrum)

用粒子群算法解决0/1背包问题，解决在背包体积有限的情况下，如何最大化的实现有效资源分配(Solve knapsack problem with particle swarm algorithm to solve the limited volume of the backpack, how to maximize the efficient allocation of resources to achieve)

基于yule_walker方程的空间谱估计算法，对学习空间谱估计与天线阵列的朋友会有帮助：）(yule_walker equation based on the spatial spectrum estimation algorithm right learning space spectrum estimation antenna array and friends will be helpful :))

ksvd字典学习算法，matlab环境下运行(ksvd dictionary leanrning algorithm, run in matlab)

通过layout生成activity。需要在Constants类里面配置项目路径等信息。(Generate activity by layout.Users need to write project path in the class of Constants.)

为matlab中求解非线性方程组用到的惩罚函数法，相关研究者可以学下(Penalty function method for solving nonlinear equations used for matlab related researchers can learn)

由matlab仿真的,超宽带信号中估计带宽程序(By matlab simulation, ultra-wideband signal bandwidth procedure estimated)

线性卷积要求两个输人序列具有持续时间，但在实际应用中，可能遇到输人序列具有无限持续时间的情况。在这种情况下，分段卷积是一种有效的解决方案。文章讨论了计算分段卷积的两种方法。仿真试验表明，分段卷积方法正确且高效。(The linear convolution needs two finite-time input sequences, but in the actual application, two infi- nite-time input sequences are possible. Under this kind of situation, the partition convolution is an effective solu- tion. In this paper, two methods of the partition convolution are discussed. The simulation experiment indicatesthat the partition convolution is correct and effective.)

神经网络处理污垢数据的matlab程序的lavbview实现(The interface of fouling thermal resistance LabVIEW)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)