VC establishment of integrated Savage and eight digital presentations. Savage pr...

一个比较好用的中文分词模块,是车东等人开发的,比较有名,java语言实现-a better quality of the Chinese word module, East cars were developed, more famous, java language!

神经网络的开发包，采用c++编写的，使网络资源，大家共享-neural network development kits, prepared by the use of c, network resources and share

神经网络的HP算法，这是神经网络入门的好程序-HP neural network algorithm, this neural network is a good entry procedures

遗传算法软件，在c环境下运行-genetic algorithm software, the operating environment c

这个软件可以对输入的象素矩阵进行识别。是根据《Neural Network Design》（Martin T. Hagan等）中的关于有监督的Heb学习编写的。使用JBuilder开发的界面，使用JCreator开发的核心。-the software can input pixel matrix identification. Based on "Neural Network Design" (Martin T. Hagan, etc.) on the supervision of a study prepared by the Heb. Using JBuilder development of the interface, use JCreator development of the core.

分布估计算法（EDA）讲义，29页PPT 1. 从GA到 EDA 2. 链接学习与模型 3. 连续EDA 4. EDA 与玻耳兹曼分布 5. 资源 -estimation of distribution algorithm（EDA）tutorial 1. from the GA to the EDA 2. Linking learning and models 3. Continuous EDA 4. EDA with Boltzmann distribution 5. other Resources

针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.

利用遗传算法求解TSP的源代码程序 可以精确求出50城市-The use of genetic algorithm to solve TSP program source code can be precisely calculated 50 cities

libsvm中文声明对流行的SVM软件libsvm的详细说明会对大家有所帮助

LM编程算法，在神经网络中有很大应用之处，神经网络源代码,及BP网络训练界面,其中的L-M算法非常实用 -LM programming algorithm, in a large neural network applications, the neural network source code, and the BP network training interface, in which the LM algorithm is very practical