COUPLING OF TRANSIENT ELECTROMAGNETIC FIELD TO UNSHIELDED CABLE (over a ground p...

CR协作频谱感知仿真，感知算法仿真，非常详细(CR collaborative spectrum sensing simulation)

matlab初学者适用，很好的matlab学习资料，推荐！(matlab beginners apply matlab good learning materials, recommended!)

本文档可用来学些matlab编程，通过本文档可以快学了解matlab编程的相关内容。(This document can be used to learn some matlab programming, through this document can quickly learn to understand the correlation matlab programming content.)

Discrete Preisach hysteresis algorithm

搜集的SVD的一些小程序。matlab编写。(SVD collected some small programs. matlab prepared.)

线性调频信号，sar合成孔径雷达点目标成像，一维成像(Chirp signal, sar SAR point target imaging, a one-dimensional imaging)

说明：  小波神经网络实例，用一周的数据来预测未来一天的情况，使用MATLAB软件实现人工智能的方法。(A wavelet neural network example is used to predict the situation of the next day with one week's data. MATLAB software is used)

c est un code vectorisé de PSO : vectorised particle swarm optimisation algorithm

foeward featurrselection

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.