case10year

卡尔曼滤波matlab编程，很好，他可以实现惯性元件数据的融合。(kalman filter matlab editor)

TGM.m \传统的Galerkin方法 MD.m \时滞惯性流形方法 brownian.m \演示布朗运动 randomwalk.m \ 演示随机游走 tumor.m \ 演示tumor演化(TGM.m traditional Galerkin method MD.m Delays inertial manifold method brownian.m demo Brownian motion randomwalk.m demo random walk tumor.m demo tumor evolution)

之前找不到合适精度的log2x函数的定点化源码，经过实现并验证后，发上来大家一起学习(fixed version for log2x)

可以用于科学计算的库，数值计算库，只要将头文件包含即可。(Can be used for scientific computing libraries, numerical libraries, header files will be included as long as you can.)

主要用于三次样条拟合，对离散点进行拟合插值，(Three order spline fitting)

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani 型模糊推理规则控制器控制倒立摆系统稳定的基础上, 设计了一种更有效率的基于Sugeno 型模糊推理规则的模糊神经网络控制器。该控制器使用BP 神经网络和最小二乘法的混 合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani 型控制器的仿真对比, 表明该Sugeno 型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。(In order to improve the three-level control of inverted pendulum system response speed and stability, in the design of Mamdani-type fuzzy inference rules of the system controller to control the stability of inverted pendulum on the basis of a more efficient design based on Sugeno-type fuzzy inference rules of fuzzy neural network controller. The controller is the use of BP neural network and hybrid least squares training algorithm parameters can be accurately summed up the amount of input and output fuzzy membership function and fuzzy logic rules. Mamdani-type controller with a simulation comparison shows that the Sugeno-type fuzzy neural network controller for the three-tier control of inverted pendulum system with good stability and fast, as well as a higher control precision.)

python机器学习预测分析核心算法的例程(Python machine learning prediction and analysis of core algorithm routines)

车间调度问题主程序,需要进行自主参数设计(Shop scheduling problem the main program, the need for self-Parameter Design)

ABAQUS的二次开发，非常适合学习VDLOAD的同志们学习，来自廖公云老师的《ABAQUS有限元软件在道路工程中的应用》(Learning from Liao Gongyun teacher "ABAQUS finite element software ABAQUS secondary development, very for learning VDLOAD, comrades in road works")

source code for simulating a DFA in compilers