fuzzyPID

说明：  利用MUSIC算法进行TOA估计，可用于无线传感器网络节点定位(TOA estimation by MUSIC algorithm for wireless sensor network node localization)

有关MIMO方面的各种仿真源代码，值得一看。特推荐(MIMO simulation on various aspects of the source code, worth a visit. Special Recommendation)

说明：  是一个dcdc反激式开关电源设计运用matlab的simulink模块，有具体的波形和数据设置(Flyback switching power supply)

返回系数估计向量；stats返回各种参数估计；’wfun’指定一个加权函数；tune为调协常数；’const’的值为’on’(默认值)时添加一个常数项；为’off ’时忽略常数项(Return coefficient estimate vector stats back to a variety of parameter estimation ' wfun' to specify a weighting function tune for the co-ordination constant ' const' value ' on' (default value) add a constant term for the ' off' time ignore the constant term)

matlab 小波分析算法之一，可以很方便的处理图像数据(matlab wavelet analysis algorithm, one can easily handle the image data)

说明：  这是模型参考自适应的一个m文件，希望有用(This is a model reference adaptive m file, hope that useful)

PID算法控制器的仿真,连续信号的PID控制器仿真(PID algorithm controller simulation, a continuous signal simulation of PID controller)

学习matlab的简单范例，通过在电脑上运行，可以快速上手matlab(Learning matlab simple example, by running on the computer, you can quickly get started matlab)

对仿真信号进行快速傅里叶变换，再进行经验模态分解，再对每个分量求FFT(Simulation signal fast Fourier transform, and then empirical mode decomposition, and then seek FFT for each component)

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)