资源列表
direction finding algorithm
- 利用信号本身的结构特征，通过附加不同的稀疏约束，该模型利用过完备字典进行信号分解，使其表示成字典中若干原子的线性组合，从而获得数据的精简表示。(By using the structural characteristics of the signal itself and adding different sparse constraints, the model decomposes the signal into linear combinations of atoms in the dic
U-BAND software
- KT0616/KT0626 C语言程序(the KT0616/KT0626 C program source.)
QQ群排名等综合优化源码
- QQ群排名等综合优化源码QQ群排名等综合优化源码QQ群排名等综合优化源码QQ群排名等综合优化源码
Beast Super Signal
- Beast super signals for trading h1 and h4
图章制作软件
- 在线自动生成图章,超级好用,如果有设备可以直接刻章(Automatic Generation of Seals)
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
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
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- 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
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.)
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.)
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
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