搜索资源列表
GaBp
- 用GA训练BP网络的权值、阈值从而优化神经网络-GA training BP network with the right value, the threshold in order to optimize the neural network
GA-.BP
- 通过遗传算法优化BP神经网络权系数，达到局部的最有、优-By genetic algorithm to optimize BP neural network weight coefficient to reach the most local, excellent
GA-BP
- BP神经网络 GA-BP 非常实用的源码-Very useful source of BP Neural Network GA-BP
BP-GA
- 一种实现话务预测预测算法，基于遗传算法优化神经网络，源码+数据，效果很好-A traffic forecast prediction algorithm based on genetic algorithm optimization neural network source+ data, the effect is very good
GA-BP
- 一个带数据的遗传优化神经网络算法，运行前在matlab输入A=load(D:\数据.txt) -A data genetic optimization of neural network algorithm, before running in MATLAB input A=load (D:\ data.Txt)
GA-TO-bp-NET
- 采用GA对神经网络的权值与阀值进行优化，得到效果更好的网络-GA uses neural networks for weights and thresholds are optimized to get better network
GABPMain
- 基于遗传算法的BP神经网络优化算法的MATLAB代码，用遗传算法优化BP网络计算。-BP neural network based on genetic algorithm (ga) optimization algorithm of MATLAB code, and use genetic algorithm to optimize BP network computing
GABP
- 基于C++，的GA优化BP神经网络算法，有详细注释-Based C++, the GA optimization BP neural network algorithm, detailed notes
GA-YOUHUAbpANN
- MATLAB中采用GA优化BP 神经网络的参数，具有一定的参考价值-MATLAB using GA optimization BP neural network parameters, has a certain reference value
GA-BP-matlab-socure-code
- matlab写的神经网络代码，其中含有遗传算法对神经网络的权值和阈值优化模块，解释比较详细，师兄的毕业设计部分代码-Written in Matlab neural network code, which contains the genetic algorithm of neural network weights and threshold optimization module, a more detailed explanation, senior graduation design p
ga_bp
- BP网络是一类多层的前馈神经网络，针对网络结构、初始连接权值和阈值的选择对网络训练的影响很大，但又无法准确获取，采用GA-BP对神经网络进行优化-GA-BP neural network optimization, pro test available
genetic-appndcation-application
- 介绍有关GA遗传算法结合BP神经网络的经典文章及应用-About genetic algorithm GA combined with the classic articles and application of BP neural network
GA_Test
- 这是用于遗传神经网络的matlab代码, 代码里面详细表述了怎么样建立BP神经网络的GA优化方法(this is the code for GA based BP neural network)
神经网络入门13课源码
- 神经网络入门13课源码 第一课 MATLAB入门基础 第二课 MATLAB进阶与提高 第三课 BP神经网络 第四课 RBF、GRNN和PNN神经网络 第五课 竞争神经网络与SOM神经网络 第六课 支持向量机( Support Vector Machine, SVM ) 第七课 极限学习机( Extreme Learning Machine, ELM ) 第八课 决策树与随机森林 第九课 遗传算法( Genetic Algorithm, GA ) 第十课 粒子群优化( Part