simulation_of_power_network

动力学教程中的数值方法，包括中心差分法，newmark法，wilson法(Tutorial dynamics of numerical methods, including the central difference method, newmark Act, wilson Law)

混沌matlab源代码 实际例子,仅供参考(Chaos matlab source code examples for reference only)

说明：  本文件中有多个matlab内核函数的源代码，可以方便的查阅在matlab内核运行的代码，从而提高个人matlab编程技巧。(This file is about the core code of matlab,can help reader and learner to improve programming in matlab Environment.)

切削力分析切削力分析切削力分析切削力分析(cutting forcecutting forcecutting force)

基于人工神经网络的常用数字信号调制，可以广泛的应用于数据预测及数据分析,虚拟力的无线传感网络覆盖，研究生时的现代信号处理的作业，LCMV优化设计阵列处理信号，多姿态，多角度，有不同光照。( The commonly used digital signal modulation based on artificial neural network, Can be widely used in data analysis and forecast data, Virtual power wireless sensor network coverage, Modern signal processing jobs when the graduate, LCMV optimization design array signal processing, Much posture, multi-angle, have different light.)

how to use doc to matlab file

folyd算法的matlab，很好用~非常适合在图论中的应用~(folyd algorithm matlab, with very good ~ very suitable for application in Graph Theory ~)

熵权法的matlab实现，熵权”理论是一种客观赋权方法，它借用信息论中熵的概念。熵权是在给定评价对象集后各种评价指标值确定的情况下，各指标在竞争意义上的相对激烈程度，从信息角度考虑，它代表该评价指标在该问题中提供有效信息量的多寡程度，作为一种客观综合评价方法，它主要是根据各指标传递给决策者的信息量大小来确定其权数。(Matlab implementation of entropy weight method)

螺旋扫描区域全覆盖算法，区别以往算法，极大程度优化路径规划。(Spiral scanning region full coverage algorithm)

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)