资源列表
遗传算法优化BP网络(用于电力负荷预测预测）
- 遗传算法改进的bp神经网络精准预测符合数据(Precision prediction coincidence data of BP neural network improved by genetic algorithm)
classifier_cnn
- 利用MATLAB实现一个基于CNN的图像分类算法(Implementing an image classification algorithm based on CNN with MATLAB)
新版在线vip视频解析
- vip视频在线解析,乐视VIP电影,腾迅VIP电影,爱奇艺VIP电影,优酷VIP视频,免费在线视频网站,海量正版高清视频在线免费观看。(vipxiabanshipinjiangxi)
EtherCAT DS402 STM32 LAN9252
- EtherCAT从站，STM32+LAN9252开发板,实现DS402运动控制，Keil编译，实现EtherCAT从站功能，完善的PDO控制，SDO控制等，详细代码，
欧服正版
- 2005至2008年欧洲传奇2三职业源码(2005to2008three occupation source code)
基于极限学习机ELM的数据分类
- 针对数据分类问题，提出了基于极限学习机的分类方法，将数据样本分为训练样本和测试样本，并采用准确率指标进行评价。(Aiming at the problem of data classification, a classification method based on extreme learning machine is proposed. The data samples are divided into training samples and test samples, and the
基于极限学习机的预测
- 针对非线性预测问题，建立极限学习机的预测模型，将数据样本分为训练样本和测试样本，并采用误差指标进行评价。(Aiming at the problem of non-linear prediction, the prediction model of extreme learning machine is established. The data samples are divided into training samples and test samples, and the error i
rc522之51单片机1602显示以及上位机
- NFC之基于51单片机rc522的读写 ,看具体说明，已测试成功，(The reading and writing of NFC based on 51 single chip computer rc522 has been tested successfully.)
配平及小扰动线性化方程
- 配平及小扰动线性化方程，很简单的。。。。。。。。。。。。。。。(Equalization and small perturbation linearization equations are very simple.)
7
- 设计两台单片机系统间的串行通信 （1）甲方P1口连接8个LED灯； （2）乙方经串行通信输出数据至甲方，甲方根据所接收的数据，在8个LED灯实现流水灯显示； （3）需采用串行口方式1及中断方式进行数据的发送和接收。(Design of Serial Communication between Two Single Chip Microcomputer Systems (1) Party A's P1 port is connected with 8 LED lights; (2) Pa
基于遗传算法优化BP神经网络的非线性预测
- 针对BP神经网络的初始权值和阈值是随机选取的弊端，采用遗传算法寻优BP的初始权值和阈值，然后进行BP训练和测试。遗传算法包括编码 选择 交叉 和变异等操作(Aiming at the disadvantage that the initial weights and thresholds of BP neural network are randomly selected, genetic algorithm is used to optimize the initial weights and
采用BP神经网络进行非线性预测
- 该代码包括单隐含层BP和双隐含层BP。建立基于BP神经网络的预测模型，对数据进行随机排列，选取训练样本和测试样本，训练样本训练网络，测试样本进行验证(The code includes single hidden layer BP and double hidden layer BP. Establish a prediction model based on BP neural network, arrange the data randomly, select training sample