MATLAB-neural-network

用来描述融合图像的熵值，标准偏差以及标准差的评价程序。(Images used to describe the entropy of fusion values, standard deviation and standard deviation of the evaluation procedures.)

GPSCA码跟踪，自己编写，可观察误码率，(GPSCA code tracking, I have written, the bit error rate can be observed,)

说明：  细胞生长结构可视化工具箱-MATLAB Toolbox1999.zip(cell growth structure visualization toolkit- MATLAB Toolbox1999.zip)

Predictive direct power control (P-DPC) applied to voltage source pulse-width-modulation (PWM) rectifier was introduced in this paper. Based on the model of power prediction, voltage vectors were selected according to minimizing power errors in a fixed time interval, which results in a constant switching frequency control for P-DPC. It simplifies the implementation of P-DPC procedure by selecting switching states based on the principle of space vector modulation (SVM). In order to eliminate power errors caused by control delay, a detailed control delay compensation scheme was designed. Experimental results based on a 2kW prototype were presented, which showing that P-DPC has a faster power response compared to standard voltage-oriented-control (VOC) techniques. Besides, the dynamic performance of the system is also improved significantly by control delay compensation.

gives introduction about matlab

10Multivariate refinement equations and convergence of subdivision schemes HJ97

FOR HELP AND DOCUMENT FOR POWER FOR HELP YOU

卡尔曼滤波在目标跟踪中应用仿真研究。 子函数能完成对运动目标位置的卡尔曼滤波跟踪。 主函数针对一具体假设完成跟踪，并且完成蒙特卡罗仿真。 情景假定：有一两座标雷达对一平面上运动的目标进行观测，目标在 0-600秒沿x轴作恒速直线运动，运动速度为15米/秒，目标的起始点为(-10000米，2000米)。雷达扫描周期T=2秒，x和y独立地进行观测，观测噪声的标准差均为100米。(This program described the Kalman filter algorithm according to a given scenario, simulation achieved the target tracking observation data and Kalman filter results, and the experimental results is studied using the Monte Carlo simulation. )

利用KALMAN函数对运动物体进行跟踪，有较好的效果 (make use of KALMAN function to track moving objects, which have a good result)

基于等式约束和不等式约束的非线性规划的混合型惩罚函数优化方法(Based on equality and inequality constrained nonlinear programming mixed penalty function optimization)