离散数学中

A star 算法类的实现(算法接口，针对迷宫问题设计)-algorithm to achieve the category (algorithm interface design issues against maze)

命题逻辑中的推理规则可以用来证明一个已经表示成命题公式的定理，但它还不便于机械化实施。因为其中的各条规则都未指明在推导的什麽步骤上应该引入什麽前提或中间结论，这些还有赖于人的经验技巧和才能。1960年王浩提出的算法，可以完全脱离人的经验和技巧，机械地构造每一步推导，证明一个命题逻辑定理的成立。 -Propositional logic in the inference rule can be used to prove that a propositional formula has been expressed as the theorem, but it is not easy to implement mechanization. Because the rules did not specify what steps in the derivation of what should be the introduction of the premise or intermediate conclusions, which also depends on the person s experience of the skills and talent. Wang Hao in 1960 proposed algorithm can be completely divorced from the person s experience and skills, a mechanical structure is derived for each step to prove that a propositional logic theorem establishment.

一种新的ica算法，主要是应用与盲分离算法，大家可以-ica a new algorithm, and are primarily used to blind separation algorithm, we can s

停车诱导系统中车位预测模型的研究 摘 要 研究城市停车诱导系统的停车车位占有率预测问题。首先提出墓于B P神经网络的车位占有预测模型， 同时将自适应 学习速率调整法和加入动量项方法用于改善基本B P神经网络， 优化了学习速率， 减少了训练过程的震荡趋势， 改善了网络的收效 隆。以此为基础实现了停车位的智能预测 0最后， 进行了多种方法比对实验-err

一个用C编写的SOM（自组织特征映射）神经网络源代码。-a C prepared by the SOM (self-organizing feature map) neural network source code.

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot

清华大学人工智能讲义-Tsinghua University, notes in artificial intelligence

人工智能神经网络算法的实现。注意可能由于实现方式不同而导致结果有点不一样，这是允许的。-Artificial neural network algorithm.

神经网络hopfield源码- Nerve network hopfield source code

改进的遗传算法的三个操作算子，包括选择、交叉和变异。-Improved three arithmetic operator in genetic algorithm including select,crossover and mutuation