Airy-beam

计算上田阵子的最大利亚普诺夫函数的matlab源码程序, 是确定系统的常微分方程。(the matlab code is using to caculat duffing the max lyapunvo about determined duffing system .)

可以改变信噪比 分析在不同信噪比下 高斯白噪声中正弦信号的高阶谱情况 用于检测信号()

说明：  matlab中add函数的C++实现，即矩阵加法的实现，包含条件判断(matlab add functions in C++ implementation, the realization of the matrix addition, including conditional)

This program describe a stimulation which is in Part 1: No-Relay Transmitted power. In part 2 Relay Transmitted Power (Two-hop transmission) and in Part 3: Plot the graph Simple Relay , No Relay , Optimum Power level

并行stateflow的一个例子，是本人自己开发的，望大家能有所收获。(An example of parallel stateflow, is my own development, and hope we can gain something.)

典型二阶系统的模糊控制与传统PID控制的性能比较(typical second-order system with the traditional fuzzy control PID control of the performance comparison)

说明：  频域的模态参数识别方法之一，可以对振动信号进行分析处理，得出结构的固有频率，阻尼比，振型系数，方法很好，程序完整，(Frequency-domain modal parameter identification methods that can analyze the vibration signal processing, draw the structure of natural frequencies, damping ratio, mode shape coefficient, very good method, program integrity,)

说明：  应用MATLAB进行维特比译码，这对仿真很有用(Application of MATLAB to carry out Viterbi Decoder, which is useful in simulation)

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))

this program helps you to analyse pattern recognition classifiers using imported data