多目标优化算法NSGA2

【实例简介】机器学习中的多示例神经网络BP算法源代码 【核心代码】BP_MIP.m BPMIP ├── BP_MIP.m ├── generate_Pos_Neg_Bags.m ├── min_max_norm.m ├── molecule_num.txt ├── musk1.txt ├── shengcheng_NBags.m ├── shengcheng_PBags.m ├── shengcheng_testBags.m ├── shengcheng_trainBags.m └── update_net_cl.m 0 directories, 10 files

目录一、贝叶斯分类器.....................................................................3二、分类流程图.........................................................................3三、最大似然估计.....................................................................4四、分类的三个过程.................................................................4五、具体代码实现.....................................................................5六、结果展示.............................................................................11

【实例简介】光伏最大功率追踪.slx

【实例简介】匹配滤波器仿真

卡尔曼滤波在目标跟踪上的运用(三维).m

【实例简介】 预测信号由二阶线性预测模型产生， .其中，是均值为零，方差为1的高斯白噪声序列，a1=1.558,a2=-0.81。分别利用LMS算法、格型LMS算法、RLS算法、LSL算法来估计线性预测模型参数a1和a2 一.实验目的 1.预测信号由二阶线性预测模型产生，.其中，是均值为零，方差为1的高斯白噪声序列，a1=1.558,a2=-0.81。分别利用LMS算法、格型LMS算法、RLS算法、LSL算法来估计线性预测模型参数a1和a2； 2.利用LMS算法和RLS算法将一个叠加有噪声的信号实现噪声消除，恢复原始信号。

阵列信号处理中的角度和时延联合估计（JADE）算法MATLAB程序

复合材料刚度计算

播放音乐

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.