资源列表
cs_explain_radar
- 压缩感知在雷达中的应用 很好的一个介绍 雷达成像中惯用的方法是匹配滤波，它之所以 能够处理低信噪比的问题，是因为它利用了回波数据的冗余信息。也就是目前雷达成像算法之所以成功的关键是具有足够 多的冗余信息。现在，在雷达成像中使用压缩感知恰好是反其道而行-Compressed Sensing(CS)theory is a great breakthrough of traditional Nyquist sampling theory，it accomplishes cornpressive
LVQ
- LVQ神经网络的分类——乳腺肿瘤诊断，判定良性还是恶性-LVQ neural network classification- diagnosis of breast cancer
Genetic-algorithm-instance
- 遗传算法实例，人工智能. -Instances of genetic algorithms, artificial intelligence
select
- 中位数选取法 两个城市中选最短距离，最优的方法-Median selection method
Genetic-algorithm
- 初步实现了简单的遗传算法的应用，对遗传算法的步骤有着明确的阐明-Initial realization of a simple genetic algorithm, has a clear elucidation of the steps of the genetic algorithm
postman
- 关于邮递员问题的最直接展示，其中涉及到了5个点，可以调整不同地点之间的距离值。直接运行，有完整代码。-About the problem of the most direct display, which involved five points, can adjust the distance between different places value. Direct operation, a complete code.
HACtest
- HAC 层次聚类算法，这个算法建立簇是进过特殊计算的，在合并之后进行评价-The algorithm for building clusters is computationally optimized. After merging the clusters only those matrix elements that are affected by merging are recalculated. The speed is increased in several hundred time
RadarEffectiveness
- 一种采用模糊等级评估雷达效能的新方法，值得一看的论文-A method to evaluate radar effectiveness based on fuzzy analytic hierarchy process
HACsources
- HAC 层次聚类算法，这个算法建立簇是进过特殊计算的，在合并之后进行评价-The algorithm for building clusters is computationally optimized. After merging the clusters only those matrix elements that are affected by merging are recalculated. The speed is increased in several hundred time
neural-PID.zip
- 单神经元PID的源程序，此程序简单易懂里面附有详细说明，对于初学者很管用。,Single neuron PID of the source, this procedure is simple to understand which accompanied by detailed notes, for beginners very useful.
GA3threshold
- 针对Bmp图片，采用多阈值最大化图像的信息熵为目标，采用多目标遗传算法（源码全）对图像进行多阈值变换，实现对图像的多阈值分割。（VC6.0实现）-Bmp images using multi-threshold image information entropy maximization as the goal, the use of multi-objective genetic algorithm (source full) image multi-threshold transform,
BP
- 先选择500个数据进行学习，学习方法是采用神经网络的BP算法，然后对未参与学习的数据进行数字识别-Select the first 500 data learning, learning neural network BP algorithm, then digital identification data that did not participate in the learning