可用于bp,elman,rbf,grnn四种神经网络的预测命令

TSP的智能算法，里面包含最大最小蚁群算法，蚂蚁系统，蚁群系统

隐马尔科夫模型工具箱最新版本，可以广泛应用于识别等领域-HMM Toolbox latest version can be widely applied in areas such as identification

用C++实现了期望最大化算法，为了简化，矩阵使用对角矩阵-C achieve expectations maximize algorithm, in order to simplify the use of matrix diagonal matrix

自适应遗传算法，采用C编写，比较适应开始学习遗传算法的人学习-Adaptive genetic algorithm, using C write, compare the genetic algorithm to adapt to start learning to learn

fan_nncC语言神经网络程序包-fan_nncC language neural network package

用改进的神经网络MBP算法辨识 ，对具有随机噪声的二阶系统的模型辨识-improved neural network algorithm for identification of MBP, the random noise with the second-order system model

贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training

BP小波神经网络快速学习算法研究and 小波神经网络程序-BP wavelet neural network learning algorithm for rapid research and wavelet neural network procedures

微分进化算法DE是1995年由Rainer Storn和Kenneth Price首先提出。DE已被证明在求解过程中具有高效性、收敛性、鲁棒性等优点[5,6] 。它在许多优化问题中都表现出优于自适应模拟退火算法、POS 算法、GA算法的性能。DE利用实数值参数向量作为每一代的种群，它的自参考种群繁殖方案与其他优化算法不同。-DE was first proposed in 1995 by Rainer Storn and Kenneth Price and had been proven that it is efficient, convergent, and robust in the solution process. [5,6] In many optimize questions, its performance surpasses the adaptive simulated annealing algorithm, the POS algorithm, and the GA algorithm. DE gets each generation of population using the real value parameter vector, and the crucial idea of DE is a scheme for generating trial parameter vectors.

用Prolog求解传教士和野人问题 复习经典谓词演算中的归结原理，掌握人工智能程序设计语言Prolog，理解通过搜索求解问题实现人工智能的思想。-Using Prolog to solve the issue of missionaries and Savage Classical Predicate Calculus review summarized in principle, grasp the artificial intelligence programming language Prolog, to understand to solve problems by searching for the realization of the idea of artificial intelligence.