JEAN

说明：  MATLAB图像处理源程序汇总2：程序代码说明 P0201：MATLAB赋值 P0202：MATLAB中的for循环 P0203：MATLAB中的for循环和if条件 P0205：MATLAB图像处理的基本操作 P0206：MATLAB高级图像处理操作 P0207：根据RGB图像创建一幅灰度图像 P0208：二值图像的取反操作 P0209：用imshow函数显示图像 P0210：在同一个窗口内显示两幅图像(MATLAB image processing source Rollup 2: Description p0201 code: MATLAB assignment P0202: for loop P0203 in MATLAB: MATLAB in the for loop and if conditions P0205: MATLAB image processing operations P0206: MATLAB Advanced image processing operations P0207: RGB images to create a grayscale image P0208: binary image inverted operation P0209: imshow function to display the image P0210: two images displayed in the same window)

This recreation makes the filters more stable. For high sampling rates the time records are downsampled. For the low sampling rates the time records are upsampled. Depending on the Filter types. Iterative resampling is used in the Nth octave band time filter to improve the attenuation of the resample program. Also the filters are settled by preappending time data, then deleting the preappended data after filtering.

说明：  语音识别全过程的matlab程序，运行测试能出结果，希望对你们有用，实在没点数才把自己压箱底的东西拿出来,呜呜(The whole process of speech recognition matlab program, run the test to the result, I hope helpful for you, only then it did not point out what their own bottom pressure, the hum)

一个关于MDP的经典例子，关于赛车问题的。(an examples about MDP)

该源码为matlab6.5图形图像处理书籍的配套资料。(The source code for the graphic image processing books matlab6.5 supporting information.)

粒子群算法，自带测试函数 (Particle swarm optimization algorithm)

TDOA chan taylor 联合算法(TDOA chan taylor)

说明：  BP神经网络建立的定量分析模型，用于预测未知待测量(quantitative analysis)

说明：  采用改进的蚁群智能算法，得到车辆最短路径问题快速有效的局部最优解。(The improved ACO to obtain the shortest path problem vehicle fast and effective local optima.)

PLS - DN是一个有限的牛顿为解决非退化的分段线性系统的算法。 PLS - DN的展品可证明半迭代 财产即在全球范围内的精确解在有限数量的迭代算法收敛。被证明是收敛速度 至少前终止线性。 广泛的算法是在我们的AISTATS 2011纸描述：“一个非退化的分段线性系统的有限牛顿算法”。此演示包重新运行 在解决非负最小二乘法（NNLS）问题上的三个稀疏的设计矩阵，从哈威尔波音收集（达夫等人，1989年）第4.2节的实验。 随着PLS - DN，此演示包建议实施五 其他NNLS求解：LCP_Fisher_Newton，LCP_Lemke，PQN，TRESNEI，和SCD (PLS- DN is a finite non-degenerate Newton to solve the piecewise linear system algorithms. PLS- DN exhibits semi-iterative property to prove that the exact solutions worldwide in a limited number of iterative convergence. Proved to be terminated before the linear convergence rate at least. A wide range of algorithms in our AISTATS 2011 paper describes: " a non-degenerate sub-linear systems with limited Newton algorithm." This demo package to re-run in the settlement of non-negative least squares (NNLS) on the design of the three sparse matrices from the Harwell Boeing collection (Duff et al, 1989) Section 4.2 of the experiment. With PLS- DN, this demonstrates the implementation of five other NNLS package proposed solution: LCP_Fisher_Newton, LCP_Lemke, PQN, TRESNEI, and SCD)