pudu

说明：  bp神经网络的源程序，很实用，很不错的！(bp neural network source code, very practical, very good!)

姚东等编著的Matlab命令大全，适合初学者使用。(YAO Dong-ed, such as the Matlab command Daquan, suitable for beginners to use.)

MATLAB 课本实例教程MATLAB 课本实例教程MATLAB 课本实例教程MATLAB 课本实例教程(MATLAB)

峭度相关最大化解卷积（MCKD），可以用来设计滤波器，提取噪声背景下的冲击成分(maximum related Kurtosis deconvolution (MCKD), can be used to design a filter to extract the impact of background noise component)

LTE 下行链路系统代码 含信道估计，编解码以及频偏估计模块(LTE downlink system including channel estimation code)

粒子滤波的matlab程序~ 希望对大家有用哈~(Particle filter matlab program)

说明：  对二维情况下的Pareto进行指标分析，主要分析超体积指标（Hypervolume），平均欧几里得距离（Mean Euclidian Distance）和均匀度（Spacing Index）(This paper analyzes the Pareto index in two-dimensional situation, mainly including the hypervolume, mean Euclidean distance and spacing index)

说明：  基于MATLAB/GUI的M-QAM调制解调及误码率,本文件是在MATLAB2009a版本上设计的，低于此版本的有可能会出错。 此文件中“M_QAM.m”是主文件，其他均为调用文件,该程序实现了MQAM的调制解调功能，并画出了每一步的波形 (Based on MATLAB/GUI s M-QAM modulation and demodulation and bit error rate, this document is designed MATLAB2009a version, this version is lower than might be wrong. This file is in the " M_QAM.m" is the main document, others are calling the file, the program implements the MQAM modulation and demodulation functions, and draw the waveform of each step)

用于智能天线中自适应的移动目标的跟踪，其中有卡尔曼滤波的源代码(for smart antenna adaptive tracking moving targets, including Kalman filtering source code)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.