用于求解TSP问题的matlab程序，能直接运行

基于旋量的机器人运动学及动力学分析，是一种新的研究机器人运动学及动力学的方法-Based on spinor analysis of robot kinematics and dynamics

基于遗传算法的分词论文 基于遗传算法的分词论文-Segmentation Based on Genetic Algorithms PapersSegmentation Based on Genetic Algorithms PapersSegmentation Based on Genetic Algorithms Papers

MILK 提供了一个实现和比较multi-instance学习算法的环境，基于weka机器学习系统（本站可以下载）-MILK provides a comparison and achieve multi-instance learning algorithm, based on machine learning system weka (site can be downloaded)

采用的是康力山等人确定的实验参数。 对于n个城市的旅行商问题，其参数如下： 初始温度：t0=280, 每一个温度下采用固定的迭代次数L=100n, 温度的衰减系数alpha=0.92 算法停止的准则是当相邻两个温度得到的解变化很小时算法停止。-used the Stanozolol Hill were determined by the experimental parameters. N cities for the traveling salesman problem, the parameters are as follows : initial temperature : t0 = 280, each a fixed temperature of the iteration number L = 100n, temperature attenuation coefficient alpha = 0.92 algorithm to stop the criteria when two adjacent the temperature change was very Solution Algorithm stop.

简单的人工智能程序ELIZA的C源程序代码-Eliza simple artificial intelligence program of the C source code

最大概率分词法,这种分词算法能够较好的解决汉语分词中的歧义问题,但分词效率比最大匹配分词算法要低-greatest probability points accidence, Segmentation algorithm can be used to solve the Chinese word segmentation of Ambiguity, but Word efficient than the largest matching segmentation algorithm lower

数据挖掘中很不错的源码,一个fss算法的java源程序,有极好的可读性-data mining is a good source, a fss algorithm java source files, has an excellent readability

计算机故障智能诊断系统 用户输入计算机的故障症状 即可由系统得出故障原因和相关的处理方法-Diagnose the system in computer s trouble intelligence Users input the trouble symptom of the computer Can be had cranky reason and relevant treatment methods systematically

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.

利用OPenCV写的一个hough变换检测直线的程序-OPenCV written using a hough transform line detection procedure