KNN algorithm, the framework is very clear. Is a classic East

蚂蚁社区的源代码，用C++开发，很直观的演示-ant communities of source code, development using C, a very visual demonstration

用A*算法解决八数码问题的小程序，特别好的程序代码-With A* algorithm to solve the problem of a small digital 8 procedures, in particular, a good code

神经网络中的感知机的学习算法的c语言程序源代码-neural network of Perceptron learning algorithm in C language source code

人工神经网络的c++实现源码,含实现神经网络层的类,神经网络的类,中文详细注释, 并含有使用示例,学习神经网络的好源码-Artificial neural network c++ implementation source code, including achieve the type of neural network layer, the type of neural network, a detailed English notes, and contain the use of examples of good learning neural network source code

神经网络学习二维非线性函数 ,多维非线性辨识的MATLAB程序-neural network learning 2D nonlinear function, multi-dimensional nonlinear identification procedures MATLAB

本课题基于汉语普通话客观质量评价的模型、利用MOS主观评价方法和客观评价方法对语音样本进行评价。并分析客观评价方法的优缺点及在研究中遇到的问题和改进方法，其主要研究内容是利用MATLAB的建立语音客观评价模型，涉及到人工神经网络，数字信号处理等方面的内容。-The issue of objective quality evaluation based on the Mandarin model, using MOS subjective evaluation method and objective evaluation of speech samples evaluated. And analyze the advantages and disadvantages of an objective evaluation method of the problems encountered in the study and improvement of methods, the main research content is to use MATLAB model for the establishment of objective evaluation of speech, involving artificial neural networks, digital signal processing and other aspects.

2005年10月份自写的细菌分割、标号与面积统计程序。带测试图像-in October 2005 since the bacteria was divided, with the label area statistical procedures. With test images

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number

纯方位跟踪：目标为匀速直线运动模型，可以迅速收敛。-Bearing_only tracking