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SPIHT, a powerful image coder with full matlab code

Heat Equation with variable coefficients. Solution of the heat equation using point gauss method

灰色GM（1,1）预测模型，希望有用，大家一起学习学习。望大家能有所提高(Gray GM (1,1) forecasting model, hope useful, we learn together. Hope we can be improved)

刘宝碇《不确定规划及应用》第4章例4.2的matlab程序,58页。内容简单，但可以有效的帮助初学者理解随机模拟的原理。(Liu anchor " uncertain Planning and Applications" in Chapter 4 Example 4.2 matlab program, 58. Content is simple, but can effectively help beginners understand the principles of stochastic simulation.)

一个等温边界条件下计算墙角内温度场的小程序，用户输入墙角两侧温度，墙体热导率，设定网格尺寸后程序便会自动迭代求解并同时以矩阵和三维图形的形式输出运算结果。(A small program to calculate the temperature field inside the corner under the condition of isothermal boundary is introduced. When users enter the temperature on both sides of the corner, the thermal conductivity of the wall and the size of the mesh, the program will automatically solve the problem and output the results in the form of matrix and three-dimensional graphics.)

说明：  图论中最大流问题的matlab实现代码，求给定网络最大流(Maximum flow problem in graph theory implementation of matlab code, seeking maximum flow in a given network)

说明：  matlab里面使用三倍中误差法消除观测粗差，去除噪声。(matlab error method which uses three times in the elimination of Gross Errors, remove noise.)

峰值搜索 对数据进行直方图累积，在对直方图中的数据进行峰值搜索(Histogram peak search the data accumulated in the histogram peak data search)

A code to fit a curve on sampled data using moving least squares method. MLSM is generalized form of weighted least squares and firstly introduced to use in meshless Finite Element Method (FEM). The main feature of this method is that the weight function is updated from point to point which enables the user to control “closeness of fit”.This code is performed for 1D fitting and is tested on a sinusoid function.

说明：  禁忌（Tabu Search）算法是一种亚启发式(meta-heuristic)随机搜索算法，它从一个初始可行解出发，选择一系列的特定搜索方向（移动）作为试探，选择实现让特定的目标函数值变化最多的移动。为了避免陷入局部最优解，TS搜索中采用了一种灵活的“记忆”技术，对已经进行的优化过程进行记录和选择，指导下一步的搜索方向，这就是Tabu表的建立。(Tabu Search algorithm is a meta-heuristic random search algorithm. It starts from an initial feasible solution, chooses a series of specific search directions (movement) as a trial, and chooses to achieve the movement that makes the most changes in the value of a specific objective function. To avoid falling into local optimum, TS search uses a flexible "memory" technology to record and select the optimization process that has been carried out, and to guide the next search direction, which is the establishment of Tabu table.)