SCS

遗传算法中的新的多点交叉算法用来优化选择最优值-Genetic algorithm in the new multi-point crossover algorithm

决策树算法 经典的ID3算法 用于决策树规则学习等等 在规则学习以及分类中有重要的作用-classic decision tree mode for classification and rule learning

一个用于地板厂家将不同长度的地板装箱的自动配比程序，输入各种型号的地板，会用智能算法计算出最佳装箱方案。程序帮助中有详细的说明。-floor for a different length of the manufacturers are packing the floor ratio of the automatic procedures importation of various models on the floor will use intelligent algorithms to calculate the optimal loading of the program. Program to help with detailed explanations.

经典BP算法的源程序，对人工神经网络中的bp有一定的帮助作用-BP algorithm is the classic source of artificial neural networks have certainly bp helpful

neural networks applications

exmat - The Expression Template Matrix Library,是矩阵运算模板库-exmat- The Matrix Expression Template Library, yes matrix calculation Template Library

LVQ(学习矢量量化)算法：它是Kohonen的有监督学习的扩展形式。融合了自组织和有导师监督的技术，学习方法是竞争的，但产生方式是有教师监督的，也就是说，竞争学习是在由训练输入指定的各类中局部进行。-LVQ (LVQ) algorithm : it is Kohonen of supervised learning the expansion of the form. The convergence of self-organization and supervision of the instructors, learning methods of competition, but there are ways teachers to supervise, meaning that the competitive learning is the training input from the designated categories of partial.

模式识别实例工程的源代码-example source code

Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its

L-M算法。除了动量法(基于梯度下降的训练算法)外，学习率自适应调整策略是BP算法改进的另一种途径，它利用Levenberg-Marquardt优化方法，从而使得学习时间更短。其缺点是，对于复杂的问题，该方法需要很大的存储空间。 -L-M algorithm. In addition to momentum (based on the gradient descent algorithm for training), learning rate adaptive strategy is to improve the algorithm BP Another approach, which uses Levenberg-Marquardt optimization method, which makes learning time is even shorter. Its weakness is that the complex problems that the method requires a lot of storage space.