learnmatlab_sp3

这是有关MATLAB学习的一些编程技巧和使用的一个程序示例(MATLAB STUDY )

Overview The Simulink model shows an example how the Kalman Filter can be implemented in Simulink. The model itself is configured with a Gaussian process connected with a Kalman Filter. To directly use this model, one only needs to provide model prarameters including parameters of the Gaussian process, which are state space matrices, A, B, C, and D, initial state, x0, and covariance matrices, Q and R and similar parameters for the Kalman Filter, which can be in different values to mimic the model mismatch, plus the state covariance, P. The following examples show how this model can be used. The Kalman Filter can also be used as a standard model block to be connected with any other systems

说明：  模态分解用于旋转机械的故障诊断，应该可以用，我是粘贴复制的....(Modal decomposition is used for fault diagnosis of rotating machinery. It should be able to be used. I pasted and copied)

内部连个文件，一个是多天线阵列的方向图，一个是多天线v-blast仿真程序，对学习波束成形的朋友有用。(Even within the files, a multi-antenna array pattern, a multi-antenna v-blast simulation program, to learn useful for beamforming friends.)

matlab的 视觉 程序 可以带有一个高斯滤波(Matlab visual procedures with a Gaussian filter)

QPSK在加性高斯白噪声环境下，信噪比和误码率的仿真。 先产生一个四组符号的序列，再将该序列映射到对应的相位上。用相应的函数产生均值为零、方差为δ2的加性高斯噪声。 (QPSK the additive white Gaussian noise environment, and the signal-to-noise ratio of the simulation. First group have a four symbols of sequence, then this sequence mapped to the corresponding phase on. With the corresponding function have zero mean and variance of 未2 additive white Gaussian noise.)

潮流计算程序用matlab进行开发，支持多节点的潮流计算，并可设置迭代次数与精度(Flow calculation)

用生物免疫算法和遗传算法实现动态信道分配(Biological immune and genetic algorithms for dynamic channel allocation)

梯度下降法是最早最简单，也是最为常用的最优化方法。梯度下降法实现简单，当目标函数是凸函数时，梯度下降法的解是全局解。一般情况下，其解不保证是全局最优解，梯度下降法的速度也未必是最快的。梯度下降法的优化思想是用当前位置负梯度方向作为搜索方向，因为该方向为当前位置的最快下降方向，所以也被称为是”最速下降法“。最速下降法越接近目标值，步长越小，前进越慢。(The gradient descent method is the earliest and most simple and most commonly used optimization method. The gradient descent method is simple to realize. When the objective function is a convex function, the solution of the gradient descent method is a global solution. In general, the solution is not guaranteed to be the global optimal solution, and the gradient descent method is not necessarily the fastest. The optimization idea of gradient descent method is to use the current position negative gradient direction as the search direction, because the direction is the fastest descending direction of the current position, so it is also called the steepest descent method. The faster the slowest descent approach is closer to the target, the smaller the step, the slower the progress.)

This is for communication networksssssssssssssssss