资源列表
龙格库塔法求解延时微分方程matlab
- matlab利用龙格库塔放法计算延时微分方程 龙格库塔 延时微分方程 matlab(Matlab uses Runge-Kutta method to calculate delay differential equation matlab)
ADAM
- ADAM （Adaptive Moment Estimation）是另外一种自适应学习率算法，它结合动量梯度 下降法，在不同参数方向上采用不同学习率，保留前几次迭代的梯度，能够很好 的适应于稀疏数据。(ADAM (Adaptive Moment Estimation) is another adaptive learning rate algorithm, which combines momentum gradient. The descent method, which uses di
coeff-schemes
- 有限差分法构造差分格式，输入基架点个数，输出各点前的系数(The finite difference method constructs the difference scheme, inputs the number of base points and outputs the coefficients before each point.)
SVMcgForRegress
- 支持向量机中的支持向量回归函数对数据进行预测(Support Vector Regression Function in Support Vector Machine to Predict Data)
temperature_control
- 利用Abaqus子程序UAMP实现PID控制。(PID control is implemented using the Abaqus subroutine UAMP.)
Desktop
- 非线性光学书后程序，分布求解，参考作用研究问题不同，方程不同(Nonlinear Optics Post-book Procedure)
遗传算法实例
- 用matlab展现遗传算法的计算过程。例子为求0.711*t1-0.084*(a0*t4*t1/(t2-t1+t3))+0.7*(t4-t3)+0.091*t2+0.162*(a0*t2*t4/(t2-t1+t3))这个看起来很复杂的式子的最小值,各个变量的约束条件在代码里写了出来。(Matlab is used to show the calculation process of genetic algorithm. The example is to find the minimum val
1
- 此代码为FLUENT模拟TIG焊电弧的UDF，包括能量、动量、UDS的源项等(This code is UDF of FLUENT simulating TIG welding arc, including energy, momentum, source term of UDS, etc.)
qp
- 实现二次规划，分别给出了等式约束的二次规划和一般的二次规划，还有示例程序(In order to realize the quadratic programming, the quadratic programming with equality constraints and the general quadratic programming are given respectively, as well as the example program)
MCMC
- 目标参数分布情况很复杂，我们想求相关的目标参数（f（x））很难，所以想通过MCMC从目标函数采取样本估计我们想要的结果，大致流程构造一条马尔可夫链去逼近目标函数，从其稳态装下抽取样本。(The distribution of target parameters is very complicated, and it is difficult for us to find the relevant target parameters (f (x)), so we want to use MCMC
solver_mod
- 相场基础书籍的配套计算代码，对于学习相场法的基础同学来说极其有用(The supporting calculation code of the phase field basic book is extremely useful for the students who are studying the basic method of the phase field method.)
PSO_yueshu
- 带有不等式/等式约束的加速粒子群算法（apso），主要通过罚函数来进行约束，速度较快，可处理带约束问题(Accelerated particle swarm optimization (APSO) with inequality / equality constraints, which is mainly constrained by penalty function, is fast and can deal with constrained problems)