mtoolboxmatlaa

inverter control pwm technique

通过建立状态空间模型，使用simulink仿真，其中包括了kalman滤波(Through the establishment of the state space model, using the simulink simulation, including the kalman filter)

这是一个用MATLAB编的模拟一个小球的下落过程 ,很不错,希望和大家分享.(This is an addendum to the MATLAB simulation of a small ball on the whereabouts of course, very good, and he hopes to share with you.)

matlab 使用界面法处理大量数据 有数据拟合 误差率计算 画图(matlab interface method using a data processing large amounts of data fitting error rate calculation drawing)

基于matlab的GPS整周模糊度解算的LAMBDA搜索法(GPS integer ambiguity resolution of the LAMBDA search method)

this program describes the golay codes : encoder and decoder

matlab program based on newton method, nonlinear equations

粒子群算法程序，源代码等，希望对大家有帮助(Particle swarm algorithm, source code, etc., we hope to help)

雷达回波信号的高精度一维距离像分析，功能较完整，适合初学者(the high-resolution range profile analysis of radar echos, which is complete and is suitable for beginners)

The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order (The main features of the considered identification problem are that there is no an a priori separation of the variables into inputs and outputs and the approximation criterion, called misfit, does not depend on the model representation. The misfit is defined as the minimum of the l2-norm between the given time series and a time series that is consistent with the approximate model. The misfit is equal to zero if and only if the model is exact and the smaller the misfit is (by definition) the more accurate the model is. The considered model class consists of all linear time-invariant systems of bounded complexity and the complexity is specified by the number of inputs and the smallest number of lags in a difference equation representation. We present a Matlab function for approximate identification based on misfit minimization. Although the problem formulation is representation independent, we use input/state/output representations of the system in order)