palabos

说明：  插补算法 步进电机控制算法 很好的资料(PWM 1234567891010101010101010101)

为了进一步提高直接序列／跳频（ＤＳ／ＦＨ）扩频系统的抗干扰能力，基于小波包变换结合递归最小二乘算法设计 了一种变换域自适应干扰抑制算法，该算法采用小波包分解定位窄带干扰，递归最小二乘算法抑制窄带干扰。通过蒙特卡 罗仿真分析在增加抗干扰模块后，ＤＳ／ＦＨ系统工作在准静态时，在不同信噪比条件下抗窄带干扰性能。仿真结果表明：该 算法具有较强的自适应性以及抗窄带干扰能力，其性能优于传统的直接置零法，适用于多音干扰下的恶劣通信环境。(In order to further improve the anti-jamming capability of direct sequence/frequency-hopping spread spectrum system (DS/FH), based on wavelet packet transform combined with recursive least squares algorithm design a transform domain adaptive interference suppression algorithm, the algorithm uses wavelet packet decomposition positioning narrowband interference, narrowband interference suppression recursive least squares algorithm. By Monte Carlo simulation analysis to increase the anti-jamming module DS/FH system work in the quasi-static, in different SNR anti narrowband interference performance. Simulation results show that: the algorithm has strong adaptability and resistance to narrowband interference ability, its performance is superior to the traditional method of direct zero, poor communication environment suitable for multi-tone interference.)

MDVRP_遗传算法 可以运行 希望对大家有帮助(a solution to MDVRP ,i hope it can help you)

CS算法OMP该进算法，对傅里叶与 的信号进行稀疏采样(Compressive Sensing OMP)

【ansys基本建模算例】带法兰油缸的有限元分析，计算相关应力即变形([Examples] basic modeling ansys finite element analysis with flanged cylinder, calculating the stress that is associated deformation)

说明：  克里金、反距离插值算法_栅格图等值线填充等(Kriging, inverse distance interpolation algorithm _ isogram filled grid map, etc.)

求解流场二维无粘欧拉方程，中心差分，人工耗散(Solving the flow field in two-dimensional inviscid Euler equation, central difference, artificial dissipation)

利用遗忘因子递推最小二乘法做的参数估计，介绍还算详尽(Forgetting factor recursive least squares method using the parameter estimates do)

是ABAQUS有限元模拟软件的子程序，主要研究损伤断裂(a subroutine of ABAQUS for damage and fracture )

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 different learning rates in different parameter directions and retains the gradients of previous iterations, is very good. It is suitable for sparse data.)