Simulink

matlab实现的LDA分类模型,可以直接调用(matlab lda for classifiy)

本案例通过对图像加入高斯噪声，利用sym4小波函数对该图像和信号进行三级分解，用小波阀值去噪的方法对该图像进行去噪，并给出了仿真结果。(This case through join Gaussian noise image, use the sym4 wavelet decomposition of the image and signal, the wavelet threshold to filter method for the image denoising is presented, and the simulation results.)

This code performs weigthed linear discriminant analysis. Weights are applied to observations.

imagem polar signature matlab

A SNR code with no ISI in optical communication

In wireless sensor network, residual energy of node is very important criteria. Life of wireless sensor network depend the energy of node. In most of the application, the nodes are battery operated. So it is very important to select the parameters like nodes deployment in network, size of packet, etc. In work,analyze node’s energy with respect to packet size, distance, time and also how much data can be transmitted. And finally we will conclude for the effective value of these parameters.

T形桥接网络是一种常用的滤波网络中，用MatLab求其状态空间模型，画出该电路的零——极点图。(T-shaped bridge network is a kind of filter commonly used network, use MatLab for its state-space model, draw the circuit zero- Pole map.)

说明：  EMD分解matlab小程序，里面包含有EMD工具箱，非常的好用。(EMD decomposes a small matlab program, which contains an EMD toolbox, which is very easy to use.)

基于智能控制的开关磁阻电动机调速系统 对开关磁阻的前景进行的分析(very useful)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)