FramesSelectionInMulti-frameImageSuper-resolutionR

MULTIPLE INPUT MULTIPLE OUTPUT FOR WIRELESS COMMUNICATION

小波分析,包换小波降噪，分解和重构，频谱分析(Wavelet analysis, replacement wavelet noise reduction, decomposition and reconstruction, spectrum analysis)

简介：IFP_FCM,改进型FCM(模糊C均值)聚类算法,由Frank Hoppner与Frank Klawonn于2003年提出,解决了FCM算法对初始值设定较为敏感、训练速度慢、在迭代时容易陷入局部极小的问题。并附带了Box和Jenkins煤气炉数据模型辨识实例。 文件清单： IFP_FCM算法原始论文： Improved fuzzy partitions for fuzzy regression models.pdf 程序清单： IFP_FCM.m - IFP_FCM算法主函数 initifp_fcm.m - initifp_fcm子函数 stepifp_fcm.m - stepifp_fcm子函数 distifp_fcm.m - distifp_fcm子函数 IFP_FCM_T_S_box_test.m - IFP_FCM测试实例：Box和Jenkins煤气炉数据T-S模糊模型辨识 数据文件： box_data.mat - Box和Jenkins煤气炉数据(IFP_FCM, modified FCM (Fuzzy C-Means) clustering algorithm, in 2003, proposed by Frank Hoppner and Frank Klawonn, FCM algorithm to solve the initial value setting is more sensitive, slow training, while iterating easy to fall into local minimum problem. And comes with Box and Jenkins gas furnace model identification data instance.)

提取语音信号的lpc参数可调用lpcfit.m 程序(voice signal from the lpc parameters of available procedures lpcfit.m)

adaptive filter project two

经典蚁群算法 常用语解决tsp，多路径路由问题(Classical ant colony algorithm to solve common language tsp, multi-path routing problem)

MATHEMATICAL MODELS OF DISTRIBUTION CHANNELS 畸变信道数学模型。一本介绍畸变信道数学模型的外文书，对通信信道感兴趣的书友，可以看看。很好的一本英文书，一起分享。 (MATHEMATICAL MODELS OF DISTRIBUTION CHANNELS。A book about communication channel ,someone who is interest in DISTRIBUTION CHANNEL must have a look. It is really a good book,share with all of you. )

MPEG-7 shape1 partB 形状相似度测试库(MPEG-7 shape1 partB shape similarity test library)

对列车运行过程进行模糊预测算法优化控制并使用matlab进行仿真实验(Train running fuzzy prediction algorithm to optimize control and use matlab simulation experiments)

Li near r egr essi on, acti ve learning We arriv ed at the lo gistic regression model when trying to explicitly model the uncertainty about the lab els in a linear c la ss ifier. The same genera l modeling approach p e rmits us to use line ar predictio ns in var ious other co ntexts as well. The simplest of them is regress ion where the go al is to pr e dict a con tin uous resp onse y t ∈ R to e ach example ve ctor. Here to o fo cusing on linear predictions won’t b e inherently limiting as linear predictions can b e easily extended (ne xt lecture).