yuyinshouji

我所见过的最实用的matlab编程实用教程，内容涉及到数据处理的方方面面，还有生成各种图表的方法，都有例程(I' ve seen the most useful and practical tutorial matlab programming, content related to all aspects of data processing, as well as the method generates a variety of charts, there is routine)

matlab扩展编程程序例子，有HMM算法的语音识别，对于初学语音识别的来说很有用(Matlab extension programming examples, a HMM speech recognition algorithm, for beginners is very useful for speech recognition)

说明：  simulink的PMSM svpwm模型。(simulink model of the PMSM svpwm.)

介绍卡尔曼滤波的，我看过，真的很不错的。希望可以给控制，电子，信号的朋友分享 (Introduced the Kalman filter, and I have seen, really good. Wish to give control, electronics, signal friends Share)

本程序是用粒子群算法优化神经网络算法的MATLAB源程序(This MATLAB procedure is to use particle swarm algorithm to optimize neural network algorithm )

you can extract the data of a figure in matlab .

产生射频噪声，噪声调频，噪声调幅等几种压制干扰的源代码( Noise AM, FM, RF interference noise suppression, such as several source code)

电力系统配电网络计算潮流的程序，可以使用的。(Power flow calculation in electric power system)

Calculates photonic band structure for either an fcc or diamond lattice of dielectric spheres of dielectric constant epsilon_a in a dielectric background of dielectric constant epsilon_b.(Calculates photonic band structure for ei ther'isnt an fcc or diamond lattice of dielectric v. eres of dielectric constant in a diel epsilon_a ectric background of dielectric constant epsi lon_b.)

粒子滤波算法在非线性系统中得到了广泛的应用，其精度取决于目标概率函数和重要性函数是否相近，并且样本退化问题也影响了算法的性能。针对粒子滤波算法中样本退化的问题，本文提出一种基于奇异值分解的粒子滤波算法。该算法通过使用奇异值分解方法得到的重要性概率密度函数更接近于目标概率分布，降低样本退化的影响，提高了滤波器的精度，然后在列车组合定位系统的数学模型中应用该算法进行仿真实验。 (Particle filter algorithm for nonlinear systems is widely used, its accuracy depends on whether the target probability function and importance of the functions are similar, and sample degradation also affects the performance of the algorithm. For particle filter algorithm sample degradation problem, this paper proposes a particle filter algorithm based on singular value decomposition. The algorithm by using singular value decomposition of the importance of the probability density function method to get closer to the target probability distribution, reduce sample degradation, improve the accuracy of the filter, and then apply the algorithm in combination mathematical model train positioning system simulation experiments. The simulation )