极坐标图和三维图

基于人工鱼群算法的投影寻踪模型通用MATLAB源码(Artificial fish-swarm algorithm based on Projection Pursuit Model GM MATLAB source)

matlab统计分析，好多必须懂的基本知识。有数据实验，分析详细，好教材(matlab statistical analysis, a lot will have to know the basics. Experimental data is analyzed in detail, good materials)

说明：  小波变换在地磁脉动中的应用，我找的电子资料，希望对你有用(Wavelet transform in geomagnetic pulsations in the application of electronic information I find, I hope useful to you)

describe kalman filter for tracking

用高通滤波器实现的自适应干扰对消器，程序很好，很实用(High-pass filter implemented with adaptive interference canceller, the program very good, very practical)

运用matlab解多元多次方程方便快捷，实在不错啊 Multiple times using equation matlab convenient solution, it is good ah(Multiple times using equation matlab convenient solution, it is good ah)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

完成两个混合正弦信号的分离，分离是从频域的角度(Complete separation of the two mixed sine signal, separating from the perspective of frequency domain)

利用神经网络自校订、自学习功能在线整定PID(using neural network self-editing, self-learning function online tuning PID)

The Z-source converter (ZSC) is a new topology in power conversion, which has unique features that can overcome the limitations of VSI and CSI [5]. Figure 2.1 shows the ZSC implemented as a 3-phase DC/AC converter (inverter). Although DC/AC conversion is the most common application of the Z-source topology, it can also be applied to AC/DC and AC/AC power conversions [24-25]. The X shape impedance is the Z-source network which is composed of two split inductors and two capacitors to provide a coupling between the DC source and the inverter bridge