dynamic-programming

image processing lab

moment s zernike algorithm

The above file show the Independent componets analysis used in face recognisation

This paper presents a new and efficient approach for capacitor placement in radial distribution systems (RDS) that determine the optimal locations and size of capacitor with an objective of improving the voltage profile and reduction of power loss. The solution methodology has two parts: in part one the loss sensitivity factors (LSF) are used to select the candidate locations for the capacitor placement and in part two a new algorithm that employs Dijkstra Algorithm (DA) is used to estimate the optimal size of capacitors at the optimal buses determined in part one. The main advantage of the proposed method is that it does not require any external control parameters. The other advantage is that it handles the objective function and the constraints separately. The proposed method is applied to 33 & 69 RDS. The solutions obtained by the proposed method are compared with other methods. The proposed method has outperformed the other methods in terms of the quality of solution. OPTIMAL C

matlab based parameter indicator ckt

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.)

说明：  用MATLAB的神经网络工具箱实现三层BP网络(Using MATLAB)

蒙特卡洛方法 有趣的蒙提霍尔问题 求选中汽车的概率的模拟值向量(Monte Carlo method interesting Monty Hall problem seeking the analog value of the probability of the selected car vector)

关于目标追踪的一个源代码，比较了卡尔曼滤波和粒子子滤波的跟踪效果。 ，经测试可直接使用。 (A source code on the target track, and compare the tracking performance of the Kalman filter and particle sub-filter. Has been tested and can be used directly.)

PSO算法对PID控制器参数进行设置使得系统的ITAE性能指标最小(PSO algorithm PID controller parameters set so that the minimum system performance index ITAE)