1213234356579

说明：  MATLAB关于遗传算法的几个经典实例，需要的就下吧(MATLAB Genetic Algorithm on a few classic examples of needs on the next bar)

说明：  状态空间模型　加强ＭＡＴＬＡＢ学习　很有用的 (state-space model to strengthen MATLAB very useful learning)

自己编写的一段matlab程序，主要是用于EMD信号消噪(a sention of programme above signal denoising using EMD method,which is used for learning the method in "noise filtering using empirical mode decomposition")

一种最大Lyapunov指数的改进算法，以交通流混沌判别为例(Improved algorithm for maximum Lyapunov exponent, for example in order to determine the chaotic traffic flow)

一篇计算中断概率的仿真程序，对于初学者是个不错的参考。(An interrupt probability calculation simulation program, for beginners is a good reference.)

元胞自动机 nasch 模型！单条道路上模拟 的(a cellular automaton，nasch model on one lane)

The alogrithm employed in this toolbox for determining Lyapunov exponents is according to the algorithms proposed in [1] A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano "Determining Lyapunov Exponents from a Time Series," Physica D, Vol. 16, pp. 285-317, 1985. [2] J. P. Eckmann and D. Ruelle, "Ergodic Theory of Chaos and Strange Attractors," Rev. Mod. Phys., Vol. 57, pp. 617-656, 1 The algorithm given in [1] is used for first-order systems while the QR-based algorithm proposed in [2] is applied for higher order systems. by Steve W. K. SIU, July 5, 1998. (The alogrithm employed in this toolbox for determining Lyapunov exponents is according to the algorithms proposed in [1] A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano, "Determining Lyapunov Exponents from a Time Series," Physica D, Vol. 16, pp. 285-317, 1985. [2] J. P. Eckmann and D. Ruelle, "Ergodic Theory of Chaos and Strange Attractors," Rev. Mod. Phys., Vol. 57, pp. 617-656, 1985. The algorithm given in [1] is used for first-order systems while the QR-based algorithm proposed in [2] is applied for higher order systems. by Steve W. K. SIU, July 5, 1998. )

说明：  利用Matlab语言实现拉格朗日插值计算和复化辛普森，对于数值分析有一定的帮助(matlab LANGRE xinpusen)

这是很多歌窗函数的示例，包含矩形窗、布莱克曼窗等等(chuang hanshu )

此代码利用高斯牛顿迭代法，对非线性函数进行参数辨识。可以对传递函数通过阶跃响应实验得到的数据进行迭代获得传递函数的参数(The code uses the Gauss-Newton iteration method to identify the parameters of the non-linear function. The parameters of the transfer function can be obtained by iterating the data obtained from the step response experiment of the transfer function.)