人工智能经典问题TSP问题的C语言实现，内含源程序和调试过程生成的文件...

水平有限，仅供参考！望大家多提宝贵意见！-limited, and for reference purposes only! Hope you will provide more valuable opinions!

基于java实现的遗传算法的源代码，利于初学者了解GA以及如何用java实现。-Java-based genetic algorithm realize the source code, which will help beginners learn how to use GA as well as realize java.

遗传算法在工程优化计算上的应用的MATLAB程序。-genetic algorithm optimization calculation of the MATLAB program.

遗传算法神经网络，遗传算法神经网络遗传算法神经网络-genetic algorithm neural networks, genetic algorithms neural networks, genetic algorithm, neural network

给出了FIR滤波器设计的C语言程序，可以根据设计要求，灵活地调整参数，得到需要的滤波器参数。-given FIR filter design in C language program, according to the design requirements, flexibility to adjust parameters and the filter parameters.

无先导粒子滤波器工具箱，并附带pf，kf，Ukf程序，用于跟踪估计-without leading particle filter toolbox and fringe pf, kf, Ukf procedures for tracking estimates

c++语言实现基于遗传算法的TSP问题，是学习遗传编程的很好的例子-c++ language implementation of the TSP problem based on genetic algorithms, genetic programming to learn a good example of

使用java构建神经网络的开源项目教程，是java爱好者学习神经网络的难得教材。虽然是英文教材，但是语言简单，易懂。书中前几章对于神经网络基本原理的讲解十分通俗易懂，利于初学者对相关知识的理解和吸收，即使不是java编程者，也可以从中受益匪浅。 -Using the java open source project to build neural networks tutorial java lovers are rare neural network learning materials. Although the materials are in English, but the language simple, easy to understand. The book for the first few chapters explain the basic principles of neural networks is very user-friendly, which will help beginners to understand and absorb the relevant knowledge, if not the java programmers can also be benefited from.

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

本程序是具有进化能力的人工生命floy，你会看到越来越具有智能的floy会自发的产生。 -this evolutionary process is the ability of artificial life floy, you will see more intelligent the floy will spontaneously emerge.