搜索资源列表
SuperBP
- 超级棒的一个神经网络结合遗传算法的程序，可用来解决大部分复杂优化问题-These codes are made of the genetic and BP network, which can solve almost every diffciult problems
genetic
- 遗传算法用于神经网络BP和支持向量机SVM参数的优化-GA was used to optimize BP and SVM( support vector machine) parameters.
GA-BP
- 该程序包含基于BP算法的神经网络，以及智能遗传算法，并利用遗传算法对神经网络结构进行优化，最后利用优化后的神经网络进行数据预测。-This package contains the algorithm based on BP neural networks, genetic algorithms and intelligent, and the use of genetic algorithm to optimize the structure of the neural network. Fi
geiyie_v73
- 研究生时的现代信号处理的作业，遗传算法无功优化，BP神经网络的整个训练过程。- Modern signal processing jobs when the graduate, Genetic algorithm based reactive power optimization, The entire training process BP neural network.
hangsun
- BP神经网络的整个训练过程,采用波束成形技术的BER计算，遗传算法无功优化。- The entire training process BP neural network, By applying the beam forming technology of BER Genetic algorithm based reactive power optimization.
etic-algorithm
- 基于bp神经网络，采用遗传算法优化，运用matlab完成识别分类的工作，自带实例-Using genetic algorithm to optimize BP neural network, with examples
To-predict
- matlab预测程序集合，适用于数学建模大赛，直接数据导入就可以用。包括灰色模型预测程序2个，gm10，greymodel，高斯混合模型mixture_of_gaussians，以及BP神经网络优化模型，遗传算法优化，Genetic，粒子群算法优化，PSO-Matlab to predict collection, suitable for mathematical modeling contest, data import can use directly. Including gray mo
GAMETHODSEID
- 遗传算法及粒子群算法优化的BP神经网络，用于多输入多输出的神经网络预测模型 -Network Predictive Genetic Algorithm and Particle Swarm Optimization based BP neural network for multiple-input multiple-output neural
yichuanyouhuaBP
- 利用遗传算法优化的bp神经网络的模型，这种模型结合了两种算法的优点，更有利-Using genetic algorithm optimized BP neural network model, this model combines the advantages of the two algorithms, more favorable
main
- 基于遗传算法优化的bp神经网络训练main函数，包括数据的导入，归一化，训练，权阈值的保存等-Based on genetic algorithm optimization of bp neural network training main function, including data import, normalization, training, the preservation of the threshold, etc.
GA-BP优化
- 主要用于遗传算法进行神经网络学习，可直接运行(the program is used to solve BP in GA, and it can setup)
案例4
- 遗传算法优化，BP神经网络，非线性函数拟合(Genetic algorithm optimization, BP neural network, nonlinear function fitting)