BASK

几种dpsk mpsk调制编码算法的比特错误比率分析(Several dpsk mpsk modulation coding bit error ratio analysis)

基于图像中的目标对象，详细说明了boosting算法。目标识别在图像领域可使用分类器的方法解决。(Target based on image, detailed description of the boosting algorithm. Target recognition in the field of image classification methods can be used to solve.)

预测控制工具箱的应用教程，以及其他的一些可参考的文献(Predict the Control Toolbox application tutorial)

说明：  蚁群算法，云模型，逻辑斯蒂模型，SVM超平面聚类分析(Ant colony algorithm, cloud model, logistic model, SVM hyperplane clustering analysis)

EEG brain signal anlaysis codes. Extract event-related potential (ERP) compoents from brain signal. Estimate ERP waveforms, and the assoimated single-trial latencies and amplitudes. Estimate spectrum of on-going activities. Two algoithms, dVCA and AESO, are implemeneted. Could be useful for researchers and students in biomeical engineering.

matlab源码关于模糊K均值聚类算法，很好的，适合大家下载研究学习(matlab source code on the fuzzy K-means clustering algorithm, very good, for everyone to download research study)

kernel光滑滤波matlab源程序代码。fun为kern函数，在matlab文件中提供了四个函数：均匀函数，二次函数，一阶导数估计和二阶导数估计，详细请见算法文档。(kernel smooth filter matlab code)

binary tree explaination ebook

模拟2个独立窄带信号分别以20°，60°的方向入射到均匀线阵上信号间互不相关，与噪声相互独立，噪声为理想高斯白噪声，阵元数为10，阵元间距为入射信号波长的1／2，信噪比为20dB，快拍数分别为5，50，200。(Simulate two independent narrowband signals in the direction of 20 °, 60 ° of the incident on the ULA between signal uncorrelated with independent noise, the noise is ideal Gaussian white noise, the array element number 10, the array element spacing of the incident 1/2 wavelength of the signal to noise ratio of 20dB, the number of snapshots 5,50,200 respectively.)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)