资源列表
bppid
- 一种bp-pid调节器的算法实现，可进行对正弦或阶跃等信号的调节控制，并输出调节曲线，误差曲线，PID参数曲线等。 -A bp-pid regulator algorithm, can be sine or step on the regulation and control such signals, and the output tuning curves, the error curve, PID parameter curve.
SA_GA_PSO
- 模拟退火（SA）、遗传算法（GA）、粒子群优化（PSO）解决旅行商问题（TSP）实验 本文件包括源码，实验说明文档，实验总结PPT Have a happy experiment！-Simulated annealing (SA), genetic algorithms (GA), particle swarm optimization (PSO) to solve the traveling salesman problem (TSP) experiment of this docum
12k-Drive-End-Bearing-Fault-Data
- 人工神经网络算法，用于故障诊断，可以判断故障类型。-Artificial neural network algorithm for fault diagnosis, fault type can be judged.
GA-Jiaolicheng
- 一本不错的讲解遗传算法的入门书籍，希望对学习者有用-A good introductory explanation of genetic algorithms books, want to be useful for learners
Neurons-Control
- 利用单神经元控制算法，对单容水箱的液位进行控制。-Control algorithm using a single neuron, single tank liquid level control.
ffn
- 模糊神经网络，一个不错的模板，可以自己改编码改成所需要的-Neural network, a good template, you can change your code needs to change
matlab
- 使用遗传算法解决车间调度问题的matlab源代码-Shop scheduling using genetic algorithms to solve the problem of matlab source code
83525916chejiandiaodu
- 本算法是用标准遗传算法来优化车间调度，具有很好的实时性-This algorithm is a standard genetic algorithm to optimize scheduling, real-time with a good
TSP
- 通过C++语言的运用遗传算法解决TSP问题，源码写的比较清楚，详看代码-C++ language by using genetic algorithm to solve TSP problem, the source more clearly written, detailed look at the code
4
- ：本文较为系统地介绍了纸币面值数字图象识别的计算机仿真。文中详细介绍了模版 匹配、特征点匹配、神经网络等字符识别方法，并进行了比较。根据本文研究对象的特点，选择 了从分析数字本身的拓扑结构入手，根据字符投影分布的规律来判断和识别的方法，成功实现 了纸币面值的识别。-: This article notes a more systematic introduction to digital image recognition, face value of the computer si
matlablibsvm-2.81
- matlab编写的支持向量程序,主要由于预测libsvm-2.81.rar-Matlab support vector program, mainly due to the prediction libsvm- 2.81. Rar
fast-projective-clustering
- 一种子空间聚类的算法，此算法属于子空间聚类算法-a monte caro algorithm for fast projective clustering，C. M. Procopiuc, M. Jones, P. K. Agarwal and T. M. Murali， Proceedings of the 2002 ACM SIGMOD international conference on Management of data