Tabmatlab

matlab大全，专门用于图像处理和图像获取(matlab codes for graph management)

Artificial Neural Network. Madaline(MR1) code for matlab

实现产生伪随机序列的部件 —— 线性反馈移位寄存器单元。 SFlog2为扩频因子的底数为2的对数值，cycle为PN序列的周期，其值为2^SFlog2。initial_state为移位寄存器的初始状态，generator_polynomial_coefficient为生成PN序列所需的本原多项式，对应于移位寄存器的连接向量。(The realization of pseudo-random sequence generated components- LFSR unit. SFlog2 factor for the spread-spectrum base 2 of value, cycle for the PN sequence cycle, its value is 2 ^ SFlog2. initial_state shift register for the initial state, generator_polynomial_coefficient to generate the necessary PN sequence primitive polynomial, corresponding to the shift register connection vector.)

Car theory programming matlab (Car theory programming)

滑膜控制，实现滑模同结构控制以及分数阶与整数解的同步(Synovial control, synchronization with structure sliding mode control and Fractional and Integer Solutions)

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.)

用工字型差分格式解拉普拉斯方程，简洁具体，效率高 (Employment the fonts differential format solution of the Laplace equation)

状态子空间的MPC程序段，N4SID算法，可以直接在matlab中使用(The state subspace MPC block, N4SID algorithm, can be directly used in matlab)

psk技术实现，并有详细的程序，有助于通信软件的开发(psk technology, and the detailed procedures, contribute to the development of communications software)

Script generates list of owners for files in choose directory