Neural Network C Language

计算欧几里德距离C语言源代码,次源码经测试可有效运行,源码简单易懂-Calculation of Euclidean distance C-language source code, sub-source has been tested and can be effectively run, easy-to-read source

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.

《游戏编程中的人工智能技术》一书中4，5章源代码- Game programming in artificial intelligence technology, one source book, Chapter 4,5

tutorials for learning protege(open source softwar to design the ontology)

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

利用pso算法训练人工神经网络的源代码。-PSO algorithm for the use of training artificial neural network source code.

用VB编写得罗茨泵泄漏量计算，这可是花费了我半年得时间，贡献了！-VB prepared with a Roots pump leakage calculation, it is six months I have spent time and contributed!

基于梯度训练方法的rbf神经网络实现，采用随机训练及测试数据-Gradient-based training methods of rbf neural network, using a random training and test data

详细的介绍了匈牙利命名法，利于我们的代码规范-Detail the Hungarian naming method, the code will help us standardize the

粒子群算法的实例，应用于无线网络节点的动态布署等。图和表显示结果-Examples of particle swarm optimization applied to the wireless network nodes, such as dynamic deployment. Graph and table shows the results of