PID控制已经广泛的用于工业生产，但是对于PID参数的获得一直是该控制的难点。本例子为对PID控制有兴趣的人提供一种参考，该PID控制采用遗传算法。...

LinPacker是一个优化矩形布局的工具，是在具有遗传算法的半无限大小区完成这一操作的。对缩减工厂的库存是有用的。-LinPacker optimization is a rectangular layout of the tool, with a genetic algorithm in the semi-infinite size of the area to complete the operation. To reduce factory stocks will be useful.

matlab 自适应遗传算法源程序，用到了G-S正交化分解，大家可以用QR分解做一下，应该会使程序更简单一点．-Matlab adaptive genetic algorithm source code, used in the G-S orthogonal decomposition. We can do something QR decomposition, the process would be simpler 1:00.

自适应步长BP神经网络训练算法，采用最小误差和梯度下降法更新权值- BP neural network training anaysis， realized by using error feed back, gradient descent applied updating of synaptic weights

经典的粒子群源代码！详细的备注、严谨的语句是初学粒子群的好东西-The classical particle swarm source code! Detailed comments, rigorous statement is a good thing novice PSO

浙江大学编的神经网络入门教程，pdf格式。-Zhejiang University, made the neural network Tutorial, pdf format.

This code in this directory implements the binary hopfield network.Source code may be found in HOPNET.CPP. A sample training file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the screen and a file, ARCHIVE.LST.

针对TSP问题，提出了一种改进的遗传算法。在遗传算法中引入进化算法的思想，在此基础上提出顶端培育策略和分阶段策略，以求在保证群体多样性的同时加快收敛速度。-against TSP, an improved genetic algorithms. Genetic Algorithm evolutionary algorithm introduced the idea that the basis of this proposed strategy and cultivating top phased strategy groups in order to ensure diversity at the same time speed up the convergence.

遗传算法的一些经典程序收集-genetic algorithem2

使用MATLAB6.5实现的自适应学习速率反向传播网络-Matlab6.5 achieved using adaptive learning rate back-propagation network

The tca package is a Matlab program that implements the tree-dependent component analysis (TCA) algorithms that extends the independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we are looking for components that can be best fitted in a tree structured graphical model. The TCA model can be applied in any situation where the data can be assumed to have been transformed by an unknown linear transformation.