人工智能方面一个专家系统的简单例子

一个bp神经元网络的c++实现，优化了算法，有可执行界面-A bp neural network c++ to achieve and optimize the algorithm, there is an executable interface

考虑3个神经元的hopfield网络，每个神经元有一个阀值和一个权重，定义存储在网络中的目标平衡点中为矩阵T的两个三指定存储在网络中的目标平衡点 -Consider three neurons hopfield network, each neuron has a threshold and a weight, the definition is stored in the network, the goal of equilibrium point for the matrix T of the two three-designated storage in the network

模式识别的源代码，针对大样本集中样本自动分类的程序，可以手动控制输入点！-pattern recognition source code, sample concentration on large samples of automatic classification procedure can be controlled manually input!

实现文字的识别 利用比较先进的神经网络笔者已经实验 绝对可行！-it is a good method to realize te recgniton of words.it is based on bp network,the author realized it!

Collection of 30+ AI Neural Network java applets with documentation.

遗传算法在TSP中的应用的原代码， 具体的代码见内。-Genetic algorithm source code applies in TSP with detailed code

粒子群算法的示例程序，不错。可用来进行函数优化、神经网络训练等。-PSO algorithm sample program, yes. Available for function optimization, neural network training.

用遗传算法进行排序-genetic sequencing algorithms

介绍了遗传算法的基本原理和求解流程, 详细阐述了Matlab 遗传算法工具箱的使用方法, 并通过使用遗传算法工具箱对一个典型的函数优化问题进行求解, 验证了该工具箱在解决函数优化问题上的有效性和实用性。-Introduce a genetic algorithm to solve the basic principle and flow in detail Matlab genetic algorithm toolbox to use, and by using genetic algorithm toolbox for a typical function optimization problems to solve, the validity of the toolbox in solving function optimization problems on the effectiveness and practicality.

针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.