EEMD

esh generation is one of the most critical aspects of engineering simulation. Too many cells may result in long solver runs, and too few may lead to inaccurate results. ANSYS Meshing technology provides a means to balance these requirements and obtain the right mesh for each simulation in the most automated way possible. ANSYS Meshing technology has been built on the strengths of stand-alone, class-leading meshing tools. The strongest aspects of these separate tools have been brought together in a single environment to produce some of the most powerful meshing available.

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IDW插值法，距离倒数乘方格网化方法是一个加权平均插值法，可以进行确切的或者圆滑的方式插值。方次参数控制着权系数如何随着离开一个格网结点距离的增加而下降。对于一个较大的方次，较近的数据点被给定一个较高的权重份额，对于一个较小的方次，权重比较均匀地分配给各数据点。 计算一个格网结点时给予一个特定数据点的权值与指定方次的从结点到观测点的该结点被赋予距离倒数成比例。当计算一个格网结点时，配给的权重是一个分数，所有权重的总和等于1.0。当一个观测点与一个格网结点重合时，该观测点被给予一个实际为 1.0 的权重，所有其它观测点被给予一个几乎为 0.0 的权重。换言之，该结点被赋给与观测点一致的值。这就是一个准确插值。 距离倒数法的特征之一是要在格网区域内产生围绕观测点位置的"牛眼"。用距离倒数格网化时可以指定一个圆滑参数。大于零的圆滑参数保证，对于一个特定的结点，没有哪个观测点被赋予全部的权值，即使观测点与该结点重合也是如此。圆滑参数通过修匀已被插值的格网来降低"牛眼"影响。 (Inverse Distance to a Power)

matpower算例程序程序及详细的说明文件，方便改进算法(matpower study program procedures and detailed documentation to facilitate improved algorithm)

matlab 可视化 微分方程 物理 算法 超越方程 科学计算 数学变换 最值问题 图像增强 图形界面 线性方程 元胞自动机 晶体生长 金融(physical algorithm matlab visualization in scientific computing differential equations of mathematical transformations beyond the most value problem of linear equations graphical image enhancement of cellular automata Crystal Growth Finance)

多目标优化算法moea/d算法代码的源代码，遗传算法，进化算法。(Multiobjective Evolutionary Algorithm Based on Decomposition)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)

混沌问题的理论以及具体实例分析，以及得出的具体结论等等。(Chaos theory and analysis of specific examples and specific conclusions drawn and so on.)

鱼眼图像校正算法的matlab实现，自己做的小程序(Fisheye image correction algorithm matlab implementation, small program)