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核数据分类回归源程序，可用于数据建模或模式识别(Source of nuclear data classification and regression, can be used for data modeling or pattern recognition)

This is DAC with ISIS Proteus simulation. A new version on this one is simple and powerfull for you.

带有MPPT功能的光伏阵列Matlab通用仿真模型(PV array with MPPT function Matlab generic simulation model)

The following fuzzy modeling exercises run in the MATLAB environment. The interaction required by the user is guided by instructions and editable fields menus. The user can change various parameters of a system, and execute modeling commands to verify the resulting system. In some exercises, it is also possible to manually edit the fuzzy system initially generated with the parameters given by the user, in order to enhance and fine tune the performance of the model.

说明：  用C-C时间序列插入构造李亚普诺夫计算方法(Time series with CC Insert method of calculating Lyapunov structure)

Artificial Intellegence: backpropagation

说明：  MATLAB确定几何边界区域函数，用来计算偏微分方程的部分计算源代码(MATLAB sure geometry region function is used to calculate the boundary, partial differential equations of the calculation source code )

Our aim is to solve a set of linear equations by the use of a single layer feedforward Neural Network

双极性SVPWM模型适合学习和仿真的基础模型( SVPWM model of double poles adapte for studying and simulation)

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)