三阶滑模控制.slx

适量拟合代码 

【实例简介】新型的EKF算法，用于估计电动汽车蓄电池的SOC

进行多元线性回归

KNN-master ├── README.md ├── data.mat ├── offline_data_uniform.mat └── positioning_simulation.m 0 directories, 4 files KNN定位误差消除算法，来源于GitHub

利用MATLAB中的GUI工具来制作串口通信上位机，利用上位机将数据利用绘图实时显示出来。

论文《As-Projective-As-Possible Image Stitching with Moving DLT》中的拼接算法，对于视差图像拼接具有一定的鲁棒性，但是对特征点数量及其分布均匀性有较高的要求。 The success of commercial image stitching tools often leads to the impression that image stitching is a “solved problem”.The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions;the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar.Such assumptions underpin the usage of 2D projective transforms or homographies to align photos. In the hands of the casual user,such conditions are often violated, yielding misalignment artifacts or “ghosting” in the results. Accordingly, many existing imagestitching tools depend critically on post-processing routines to conceal ghosting. In this paper, we propose a novel estimationtechnique called Moving Direct Linear Transformation (Moving DLT) that is able to tweak or fine-tune the projective warp toaccommodate the deviations of the input data from the idealized conditions. This produces as-projective-as-possible image alignmentthat significantly reduces ghosting without compromising the geometric realism of perspective image stitching. Our technique thuslessens the dependency on potentially expensive postprocessing algorithms. In addition, we describe how multipleas-projective-as-possible warps can be simultaneously refined via bundle adjustment to accurately align multiple images for largepanorama creation.

图像结构相似性评估指标的matlab代码

区域负荷功率曲线（不含电动汽车）

很详细的RD经典算法，适合初学者% 零斜视角情况下的单个点目标的时域性质:% % 在方位向，信号能量随着远离波束中心而逐渐衰减。% 当距离向调频率Kr为正时，相位等值线为双曲线；当距离向调频率Kr为负时，相位等值线为椭圆。% 图中多出来的“鞍点”和“靶点”表明相位等值线存在虚像，故仅给出相位图等价于将复数信号表示成实数信号。

在260个样本中随机抽出部分学生的身高和体重作为训练样本(男女生都有)，然后训 练 一 个 B P 神经网络，最后将剩下的样本输入网络进行测试，检 验 B P 网络的分类 性能 带你进入BP神经网络的经典案例