用java语言写的遗传算法库，包含了实例。

Simon Haykin的 《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等-Simon Haykin "Neural NetWorks" examples of the original code, very classic. I believe very useful, especially in such SVM PCA

JESS开发模式，人工智能开发架构-JESS development model, the development of artificial intelligence structure

此为疾病诊断专家系统，用这种结构建立的知识库可以很方便地实现上面所提到的各种功能。这种结 构的最独特之处就是依靠相同的关键（疾病名或其代码）来实现一种疾病与其症候之 间的相互联系。疾病名与症候分别用二叉树进行排序。排序后可由疾病名而查到与该 疾病名有关的所有详细情况，也可由一种症候而查到哪些疾病有这种症候，以及该症 候可能引起各种疾病的概率。-This is the disease diagnosis expert system, using this structure to establish the knowledge base can easily realize the above mentioned features. The structure of the most unique is that relying on the same key (the disease or its code) to achieve a disease and its symptoms between. Name and symptoms of disease were treated with binary tree sort. Sort by the disease after the name of the disease were found with all the details of which can also be a symptom of the diseases that have found such a symptom, as well as the symptoms may lead to the probability of various diseases.

好不容易找到的外国博士论文,关于Text-independent speaker recognition-Hard to find foreign doctoral thesis on the Text-independent speaker recognition

遗传算法强制交叉算法-GA mandatory cross-Algorithm

矩形件排样优化问题是一个多目标优化问题，另一方面要考虑到生产时的下料效率-Rectangular pieces of nesting optimization problem is a multi-objective optimization problem, on the other hand, taking into account the production efficiency of the next expected

基于感知器神经网络处理复杂的分类问题-based perceptron neural network to handle complex classification problems

基于bp神经网络的货运量预测matlab实现-bp- based neural network is forecast to achieve Matlab

无先导粒子滤波器工具箱，并附带pf，kf，Ukf程序，用于跟踪估计-without leading particle filter toolbox and fringe pf, kf, Ukf procedures for tracking estimates

一种通过自组织竞争学习网络实现数据降维和可视化的单层神经网络模型。用此算法可以把输入空间的多维映射到低维的（一维或者二维）的离散网络上，并将保持相同性质的输入数据在映射到低维空间时的拓扑一致性。iris以及letter两个数据集进行分类-A competitive learning through self-organizing network for data dimensionality reduction and visualization of single-layer neural network model. Using this algorithm can be multi-dimensional input space is mapped to the low-dimensional (one-dimensional or two-dimensional) discrete network, and will remain the same as the nature of the input data is mapped to the low-dimensional space of topological consistency. iris as well as the letter the two data sets to classify