mkmseed

comsol 案例，值得拥有，微纳光力案例(Comsol case, worth having)

Elman神经网络建立建筑物电力负荷预测模型中遇到的几个关键问题有，数据归一化处理、输入输出样本的选取、隐含层节点数的确定；分别建立Elman神经网络模型，并利用某栋建筑物实际历史电力负载数据进行预测，分析比较与实际数据值的预测精度，得出了一个有效的数据预测模型。(Several key problems encountered in building power load forecasting model based on Elman neural network are data normalization, selection of input and output samples, and determination of the number of hidden layer nodes. Elman neural network models are established respectively, and the actual historical power load data of a building are used for forecasting. Effective data prediction model.)

说明：  matlab应用程序，RBF复合PID控制的MATLAB程序(matlab applications, RBF complex PID control, MATLAB program)

Graphic user interfase who desing dynamic systems of second order from college assigment in Barranquilla, Colombia

MATLAB实现对该系统的潮流计算。需要MATLAB语言编制程序形成节点导纳矩阵、形成雅克比矩阵、用高斯消去法解修正方程式，并编写输出程序用以输出节点导纳矩阵、节点电压（包括电压的相角和幅值）、以及各支路的首端功率、末端功率和功率损耗(MATLAB implementation of the system flow calculation. MATLAB programming language requires the formation of the node admittance matrix, Jacobian matrix formed by modified Gaussian elimination method for solving equations, and write out procedures for the admittance matrix of the output node, the node voltage (including the voltage phase angle and amplitude), and the first end of each branch of power, end power and power loss)

最大信噪比的独立分量分析算法，包含CV、CA、Single、当前、恒转弯速率、转弯模型，可以实现模式识别领域的数据的分类及回归。( SNR largest independent component analysis algorithm, It contains CV, CA, Single, current, constant turn rate, turning model, You can achieve data classification and regression pattern recognition.)

说明：  matlab混合编程（书中程序） 第四章 (matlab mixed programming (program book) Chapter IV)

说明：  基于rbf神经网络控制油船航行，对于学习神经网络控制的同志具有参考价值(based on neural network control tanker navigation, learning neural network control of the reference value of comrades)

可以实现多智能体编队，即多个分散智能体聚集到一块且相互之间保持一定的距离向前运动(Can achieve multi-agent formations that gathered more than a dispersion agent, and maintain a certain distance from each other moving forward)

Face Detection using Gabor feature extraction and neural network by matlab