matlab16neuro network

bp人工神经网络算法实现，用java编写程序代码，应用案例是港口吞吐量预测-bp artificial neural network algorithm, using java programming code, the application case is the throughput of the port prediction

利用Fisher分类器对数据进行分类，并画出决策面（含测试数据）-Fisher classifier using the data classification, and face painted in decision-making (including test data)

EM algorithm with a Rauch-Tung-Striebel smoother and an M step,内有说明-EM algorithm with a Rauch-Tung-Striebel"s moother and an M step, which is described

关于模式识别的经典书籍，英文版的，很值得看-On Pattern Recognition classic books, in English, is worth look

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

隐马尔科夫模型工具箱windows可执行版本，可以广泛应用于识别等领域-HMM Toolbox windows executable version can be widely applied in areas such as identification

基于信息熵的免疫算法是一种修正的免疫算法，方法并不复杂，对于特定问题效果较好-Entropy-based immune algorithm is a modified immune algorithm, methods are not complicated, for the specific problem better

支持向量机Windows Svm-Support Vector Machines of Windows Svm

调用过程 CM = Confusion_matrix(train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB(DP, test_targets, CM) DP，三维数组，(i,j,k)为第k个样本的DP矩阵 targets 为 0 1 2 -process called CM = Confusion_matrix (train_predicts, train_targets) [combining_predicts, errorrate] = combining_NB (DP, test_targets, CM) DP, three-dimensional array (i, j, k) for the k samples of DP matrix targets for 0 1 2

多步预测,设计简单规范,基于信息建模,具有很强的抗干扰性和鲁棒性.-Multi-step prediction, the design of simple norms, based on information modeling, which has strong anti-jamming and robustness.