waveletexample

粒子群优化算法。PID控制器的整定，matlab源码。(Tunning of PID controller using Particle Swarm Optimization Author: Wael Mansour (wael192@yahoo.com) MSc Student, Electrical Enginering Dept, Faculty of Engineering Cairo University, Egypt)

一些通信仿真实例,是我自己一直在用的东西，查了好久的，希望有用(Some test,helpful)

通信中伪随机码的产生，可以配置任意多项式和初值。(m sequence generation.)

预测控制里面MAC算法的实现，简单明了，欢迎交流(programming for prediction control)

matlab的基本教学源码，虽然简单但是浅显易懂。(matlab source code of the basic teaching, although a simple but easy to understand.)

小波神经网络的源程序: 1.构造的非线性函数: 位于nninit_test.m 2.直接用WNN逼近非线性:Wnn_test.m, (内部调用小波函数) 3.遗传算法优化后逼近 :GA_Wnn_test.m (内部调用遗传算法的,初始化,适应度,解码函数)-genetic algorithm optimization WNN source : 1. Construction of the nonlinear function : nninit_test.m at 2. WNN directly with nonlinear approximation : Wnn_test.m. (internal called wavelet function) 3. Genetic Algorithm optimization approach : GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions) (Wavelet neural network source code: 1. Construction of the nonlinear function: at nninit_test.m 2. Wnn the direct use of nonlinear approximation: Wnn_test.m, (internal call wavelet function) 3. Genetic algorithm optimized approximation: GA_Wnn_test. m (internal call genetic algorithms, initialization, fitness, decoding function)-genetic algorithm optimization WNN source: 1. Construction of the nonlinear function: nninit_test.m at 2. WNN directly with nonlinear approximation: Wnn_test.m. ( internal called wavelet function) 3. Genetic Algorithm optimization approach: GA_Wnn_test.m (internal called genetic algorithms, initialize, fitness and decoding functions))

以227家公司的数据为基础进行kmeans clustering分组，用以预测50家未知公司的效益(Use 227 the companies data as the basis for kmeans clustering to predict the effectiveness of the 50 unknown companies)

MATLAB系统仿真验证与工程应用研讨会会议资料,具有较高的参考价值(MATLAB system simulation and data engineering workshop meeting, with a high reference value)

finite difference time domain 1d code

连续阻尼物体运动的频谱，并画出了频谱曲线(Continuous spectrum damping the movement of objects, and plot the spectrum curve)