based perceptron neural network to handle complex classification problems

AI 人工智能 在C#环境下实现 9宫格 算法-AI

神经网络模式识别及其实现，第九章。 内含：HOPFIELD和LAM -pattern recognition and neural network to achieve, chap. Intron : LAM and HOPFILED

:一书的配套源程序，结合数据挖掘和机器学习的知识,以java语言实现了具有代表性的各类数据挖掘方法.例如:classifier中的ZeroR.OneR.NaiveBayes.DecisionTable.IBK.C45,还有聚类,数据预处理等-: lt; Lt; Data Mining-- Practical Machine Learning Technology and java achieve gt; Gt; A matching the source, combining data mining and machine learning, the knowledge, java language to a representative of the various types of data mining. For example : the classifier ZeroR . OneR.NaiveBayes.DecisionTable.IBK.C45, clustering, data pretreatment

纯方位跟踪：目标为匀速直线运动模型，可以迅速收敛。-Bearing_only tracking

包含FuzzyKMeans, FuzzyGK, FuzzyR等算法，拓展了weka（本站可以下载）-Contains algorithm such as FuzzyKMeans, FuzzyGK, FuzzyR. Extends weka(it is can be download form this site)

回溯法求01背包问题,c语言版本，由于时间关系没有将代码进行很好的整理，所有代码都在一个文件中-Retrospective Method 01 knapsack problem, c language versions, due to time not finishing a very good code, all code in one file

在对神经网络了解一点点后就可看懂这个，介绍MATLAB神经网络工具箱运用

人工智能动物推理系统程序-animal AI reasoning system procedures

本书就使用matlab实现bp神经网络进行了较为详细的说明，书中包含许多具体的实例说明，希望能为大家带来一定的帮助。-book on the use of Matlab bp neural network to achieve a more detailed description of the book contains many specific examples, we hope to bring help to a certain extent.

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