Best_Learn_Matlab_8.9901

电动机simulink模型，非常好用，内有控制程序(motor simulink model)

to binirazition pictures in matlab

matlab在物理化学科学的应用，有相关的统计分析和聚类模型(matlab in physical and chemical science and application of relevant statistical analysis and clustering model)

最小均方误差迭代算法和最小二乘算法的仿真程序。适用初学者(least mean square and recursive least square. appropriate for elementary learners.)

LMS三角定位迭代加权算法 LMS三角定位迭代加权算法 LMS三角定位迭代加权算法(LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm LMS triangulation iterative weighting algorithm)

利用扩展卡尔曼进行状态估计 状态向量四维 测量向量二维 误差为高斯白噪声(The use of extended Kalman state vector for state estimation of two-dimensional four-dimensional vector measurement error is Gaussian white noise)

说明：  个人写的一些matlab神经网络的函数和算法，希望大家喜欢(Some people write matlab neural network functions and algorithms, hope you like)

this code is to sample the signal

《MATLAB仿真技术与应用》是我见过该类型最好的一本书，这是我那最新版的配套光盘("MATLAB simulation technology and application," I have seen the best of this type of a book. This is my latest version of the matching CD)

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.