资源列表
5beed36fda984d1e05d681101a49a37e
- 实现遗传算法的应用，初步理解遗传算法的原理和怎样编程。(The application of genetic algorithm is applied to understand the principle of genetic algorithm and how to program it.)
BP人工神经网络负荷预测模型的L_M训练算法
- BP人工神经网络负荷预测模型的L_M训练算法(L_M training algorithm for load forecasting model of BP artificial neural network)
BP神经网络L_M优化算法在地下水动态预测中的应用
- BP神经网络L_M优化算法在地下水动态预测中的应用(Application of BP neural network L_M optimization algorithm in groundwater dynamic prediction)
trkvj
- Suppressed carrier type differential phase modulation, Independent component analysis algorithm reduces the raw data noise, For feature reduction, feature fusion, correlation analysis.
PSIM_Professional_Version_9034
- psim 懂的人拿走不谢，最容易构建的仿真软件。(PSIM understands people who take away the most easily constructed simulation software.)
简单PSO
- pso算法的改进与优化，即对粒子群算法惯性权重w与学习因子参数的约束。(pso algorithm is improved and optimized, that is, the constraints of the inertial weight w and the learning factor parameters of the particle swarm algorithm.)
lssvm
- 最小二乘支持向量机回归，四个插入数据分别为训练输入、训练输出、测试输入、测试输出。工具包+程序(Least squares support vector regression (SVM), the four inserted data are training input, training output, test input and test output)
network_learn
- 简单地实现全神经网络，适合深度学习的基础入门。(Simple implementation of the whole neural network, suitable for in-depth study of basic entry.)
成功libsvm-3.1-[FarutoUltimate3.1Mcode]
- matlab 工具箱svm，具体添加操作可以参照百度，很容易找到(SVMlab toolbox on the instructions for use method descr iption (English), and use this tool kit to achieve a time series forecasting papers you want to help.)
PNN网络代码
- 概率神经网络(Probabilistic Neural Network)是由D.F.Speeht博士在1989年首先提出，是径向基网络的一个分支，属于前馈网络的一种。它具有如下优点：学习过程简单、训练速度快；分类更准确，容错性好等。从本质上说，它属于一种有监督的网络分类器，基于贝叶斯最小风险准则。(Probabilistic neural network was first proposed by Dr. D.F.Speeht in 1989. It is a branch of radial
pso工具箱及使用简介
- PSOt为PSO的工具箱，该工具箱将PSO算法的核心部分封装起来，提供给用户的为算法的可调参数，用户只需要定义好自己需要优化的函数(计算最小值或者最大值)，并设置好函数自变量的取值范围、每步迭代允许的最大变化量(称为最大速度，Max_V)等，即可自行优化。(PSOt is the toolbox for PSO, which encapsulates the core part of the PSO algorithm, providing the user with adjustable pa
RBF遗传优化
- RBF网络能够逼近任意的非线性函数，可以处理系统内的难以解析的规律性，具有良好的泛化能力，并有很快的学习收敛速度，已成功应用于非线性函数逼近、时间序列分析、数据分类、模式识别、信息处理、图像处理、系统建模、控制和故障诊断等。(RBF network can approximate any nonlinear function, regularity can handle within the system to parse, has good generalization ability and