Umat_ElasticPlastic_Explicit

读入红细胞图片，通过中值滤波，开运算算，闭运算，和贴标签等方法获得细胞个数 (Read into the red blood cell picture, opening operation count median filtering, and closing operation, and the labeling methods such as cell number)

用Roe格式的一阶迎风差分方法解决欧拉方程的黎曼问题(Solves the Riemann problem for the Euler equations using Roe s first-order upwind method )

Step response of vehicle

基于Levenberg-Marquardt的曲线/面拟合(C library for Levenberg-Marquardt least-squares minimization and curve fitting)

针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。(TSP problem solving for the traditional algorithms such as genetic algorithms, particle swarm optimization accuracy and solving the scale, there is a certain lack of a dynamic programming algorithm-based particle swarm optimization algorithm. The algorithm using dynamic programming method to achieve the evolution of information exchange between the particles and the particles, and particle swarm particle scale-free information guide network topology map to connect. The simulation results show that this method can effectively reduce the error rate and improve the solution precision, while maintaining a low computational complexity, good soundness.)

模糊控制的C语言实现，双输入单输出，采用查表法进行模糊推理，去模糊方法采用重心法，易于单片机实现(C language realization of fuzzy control, double input and single output, using look-up table method for fuzzy reasoning, using center of gravity method for deblurring, easy to realize by single chip computer)

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.)

数值分析里面的：不动点迭代法、牛顿迭代法、共轭梯度法和最速下降法(Conjugate Gradient)

使用梯度下降法、随机梯度下降法和牛顿法实现的逻辑回归算法。(Using a gradient descent method, stochastic gradient descent algorithm logistic regression method and Newton' s method to achieve.)

说明：  九维的卡尔曼滤波跟踪算法，包括xyz三方向和各方向的位置，速度，加速度。(Nine-dimensional Kalman filter tracking algorithm, including the three xyz direction and the location of the direction, velocity, acceleration.)