Matlab

说明：  基于CRAFT的多阶段柔性设备布局优化算法 　　多阶段设备布局下的总成本包括设备拆装成本、物料搬运成本以及区域利用成本,问题的目标是最小化总成本。首先,在分析问题的基础上,建立基于多阶段设备布局优化目标的数学模型 其次,采用改进的CRAFT启发式算法优化单阶段设备布局 进一步,提出基于单阶段设备布局算法的多阶段启发式算法,优化多阶段设备布局 最后,数值实验表明算法优化后总成本降幅达28.57％. (CRAFT-based multi-stage flexible equipment layout optimization algorithm under the multi-stage equipment, the layout of the total cost including equipment disassembly cost, material handling costs as well as regional use of cost, the goal is to minimize the total cost. First of all, the analysis of the problem on the basis of the equipment based on the layout of multi-stage mathematical model of optimization objectives Secondly, the adoption of improved CRAFT heuristic algorithm to optimize the layout of single-stage device to further, based on equipment layout algorithm for single-stage multi-stage heuristic algorithm, optimize the layout of multi-stage equipment Finally, numerical experiments show that the optimized algorithm for the total cost of 28.57 percent decrease.)

说明：  为了提高认知无线电中感知用户对授权用户(主用户) 的检测概率,提出一种新的基于循环 谱统计量的主用户信号检测算法. 该算法利用循环谱离散频域平滑方法,对信号的循环谱在循环频 率处建立合理的检测统计量,然后求解得到对应不同虚警概率的判决门限,以使感知用户能检测信 噪比更低的授权用户信号.(To increase the detection probability of sensing users for licenced users (primary signals) in the cognitive radio , it put s forward a new primary users signal method based on cyclic spect rum statis2 tics. By using discrete f requency smoothing of cyclic spect rum , this algorithm establishes the reason2 able detection statistics on the cyclic f requency of signal cyclic spect rum , then solves and obtains the decision threshold correspond to different false alarm probability , as a result , making the sensing users can detect licenced users signal under lower signal in noise ratio (SNR) .)

example for matlab code

模式识别，线性分类器。经调试可用，希望可以帮到需要的人。(Pattern recognition, linear classifier. After debugging is available, the desire to help people in need.)

maximum power point traking p and o

说明：  this is 13 node distribution network with DGs and relays.

Gesture Recognition using Neural Network

MATLAB教案，一些MATLAB的命令。比较全面。(MATLAB)

这个是自适应卡尔曼滤波的程序，可以不用已知观测方程以及状态方程的噪声，能够在滤波时学习出(This is an adaptive Kalman filtering program that can do the known equations of state and noise measurement equation can learn while filtering out)

Among the high-complexity methods, two methods dominate the literature Kalman filtering and Mixture of Gaussians (MoG). Both have their advantages, but Kalman filtering gets slammed in every paper for leaving object trails that can t be eliminated. As this seems like a possible deal breaker for many applications, I went with MoG. Also, MoG is more robust, as it can handle multi-modal distributions. For instance, a leaf waving against a blue sky has two modes—leaf and sky. MoG can filter out both. Kalman filters effectively track a single Gaussian, and are therefore unimodal: they can filter out only leaf or sky, but usually not both.