fish-kong/CSDN-code-link fish-kong/CSDN-code-link

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fish-kong/CSDN-code-link
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Repository Details

由于CSDN博客里面不能直接上代码链接，涉嫌营销推广，因此建一个github仓库用于整理这些代码链接

CSDN-code-link

由于CSDN博客里面不能直接上代码链接，因此建一个github仓库用于整理这些代码链接

1）深度极限学习机用于回归及其优化

1.MATLAB麻雀优化DELM权重用于回归：https://mbd.pub/o/bread/YZmam5xq
2.MATLAB粒子群PSO优化DELM权重用于回归：https://mbd.pub/o/bread/YZmam51q
3.MATLAB旗鱼SFO优化DELM权重用于回归：https://mbd.pub/o/bread/YZmbkppv
4.MATLAB蜉蝣MA优化DELM权重用于回归： https://mbd.pub/o/bread/YZmckp1t
5.MATLAB遗传GA优化DELM权重用于回归：https://mbd.pub/o/bread/YZmckp5p
6.MATLAB蝙蝠BAT优化DELM权重用于回归：https://mbd.pub/o/bread/YZmckp9q
7.MATLAB模拟退火SA优化DELM权重用于回归：https://mbd.pub/o/bread/YZmck5Zr
8.MATLAB禁忌搜索TS优化DELM权重用于回归：https://mbd.pub/o/bread/YZmck5Zs
9.MATLAB增强碰撞体ECBO优化深度极限学习机DELM回归：https://mbd.pub/o/bread/YZmck5dp
10.MATLAB灰狼GWO优化DELM权重用于回归：https://mbd.pub/o/bread/YZqXm5tv
11.MATLAB鲸鱼WOA优化DELM权重用于回归：https://mbd.pub/o/bread/YZqXm5tx
12.Python的麻雀SSA优化DELM权重用于回归：https://mbd.pub/o/bread/YZyUm5ht

2）深度极限学习机用于分类及其优化

0.MATLAB深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1y
1.MATLAB粒子群优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZxq
2.MATLAB麻雀优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZxt
3.MATLAB蝙蝠优化深度极限学习机分类:https://mbd.pub/o/bread/YZuUmZxx
4.MATLAB蜉蝣优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1q
5.MATLAB禁忌搜索优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1s
6.MATLAB模拟退火优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1t
7.MATLAB旗鱼优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1v
8.MATLAB物体碰撞优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1w
9.MATLAB遗传优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ1x
10.Python麻雀优化深度极限学习机分类：https://mbd.pub/o/bread/YZuUmZ5y

3）MATLAB深度核（混合核）极限学习机用于分类及其优化

单核
1.MATLAB粒子群优化深度核极限学习机分类：https://mbd.pub/o/bread/YZuUm5xs
2.MATLAB贝叶斯优化深度核极限学习机分类：https://mbd.pub/o/bread/YZuUm5xw
多核：
1.MATLAB原子轨道优化深度混合核极限学习机的轴承故障诊断(AOS-DHKELM)：https://mbd.pub/o/bread/mbd-YpiYl5ts
2.MATLAB天鹰优化器优化深度混合核极限学习机的轴承故障诊断(AO-DHKELM)：https://mbd.pub/o/bread/mbd-YpiYl5tt
3.MATLAB气味体优化深度混合核极限学习机的轴承故障诊断(SAO-DHKELM)：https://mbd.pub/o/bread/YpiYl5tx
4.MATLAB灰狼优化深度混合核极限学习机的轴承故障诊断(GWO-DHKELM)：https://mbd.pub/o/bread/YpiYl5xp
5.MATLAB算术优化算法优化深度混合核极限学习机的轴承故障诊断(AOA-DHKELM)：https://mbd.pub/o/bread/YpiYl5xr
6.MATLAB粒子群优化深度混合核极限学习机的轴承故障诊断(PSO-DHKELM)：https://mbd.pub/o/bread/YpiYl5xs
7.MATLAB蜉蝣优化深度混合核极限学习机的轴承故障诊断(MA-DHKELM)：https://mbd.pub/o/bread/YpiYl5xt
8.MATLAB鲸鱼优化深度混合核极限学习机的轴承故障诊断(WOA-DHKELM)：https://mbd.pub/o/bread/mbd-YpiYl5tr
9.MATLAB麻雀优化深度混合核极限学习机的轴承故障诊断(SSA-DHKELM)：https://mbd.pub/o/bread/YpiYl5xu

4）时间序列分解+优化算法优化回归模型

1.MATLAB的EMD+模拟退火优化DBN：https://mbd.pub/o/bread/YZuUm55w
2.MATLAB的EMD结合麻雀优化LSTM：https://mbd.pub/o/bread/YZuXmJ1x
3.MATLAB基于变分模态分解与麻雀优化最小二乘支持向量机的短期电力负荷预测：https://mbd.pub/o/bread/YZuUmZty
3.MATLAB基于EEMD分解与改进鲸鱼优化最小二乘支持向量机的短期电力负荷预测：https://mbd.pub/o/bread/YpWal5dt

5）轴承故障诊断

1.MATLAB的麻雀优化DBN权重故障诊断：https://mbd.pub/o/bread/YZqTm5Zy
2.基于MATLAB的深度自动编码器的无监督轴承异常检测：https://mbd.pub/o/bread/YZmbkppw
3.Torch基于小波时频图与MLP-Mixer的轴承故障诊断：https://mbd.pub/o/bread/YZmblZZu
4.Torch基于小波时频图与VIT vision transformer的轴承故障诊断：https://mbd.pub/o/bread/YZmbl5Zw
5.Torch基于FFT频谱与小波时频图的双流CNN轴承故障诊断模型：：https://mbd.pub/o/bread/YZqYk5tx
6.MATLAB基于小波时频图与CNN的轴承故障诊断:https://mbd.pub/o/bread/YZqYk5pv
7.融合CNN(2D-CNN与1D-CNN融合)与SVM的滚动轴承故障诊断（python,tensorflow1.x）:https://mbd.pub/o/bread/YZqZlJ9x
7.融合CNN(2D-CNN与1D-CNN融合)与SVM的滚动轴承故障诊断（python,tensorflow2.x）:https://mbd.pub/o/bread/mbd-YpiclZ1p
8.MATLAB的HHT包络谱+堆栈降噪自编码SDAE轴承故障诊断：https://mbd.pub/o/bread/YpiYl59u
9.Torch基于小波时频图与Swin transformer的轴承故障诊断：https://mbd.pub/o/bread/Y5aWmZZu

6）深度学习+注意力机制用于负荷预测

1.双向LSTM注意力机制负荷预测：https://mbd.pub/o/bread/YZqTm5ps
2.鲸鱼WOA优化双向LSTM注意力机制负荷预测tensorflow1.x：https://mbd.pub/o/bread/YZqTm5tp
3.鲸鱼WOA优化双向LSTM注意力机制负荷预测tensorflow2.x.keras：https://mbd.pub/o/bread/YpaWmp1r
4.基于注意力机制的 CNN-BiGRU 短期电力负荷预测方法tensorflow1.x：https://mbd.pub/o/bread/YZqTmJ1u
5.基于注意力机制的 CNN-BiGRU 短期电力负荷预测方法tensorflow2.x.keras：https://mbd.pub/o/bread/YpiclZ5u
6.基于自注意力机制self-attention的LSTM多变量负荷预测tensorflow2.x.keras:https://mbd.pub/o/bread/Y52bmJpy
<<<<<<< HEAD 7.基于trasformer的多变量时间序列预测tensorflow2.x.keras:https://mbd.pub/o/bread/Y56Vl5Zp

372fa4d4789ec9f77f20bfc01944165166577d5e

7）深度学习LSTM超参数优化时间序列预测

1.改进PSO优化LSTM负荷预测（python3.6 tensorflow1.x框架）：https://mbd.pub/o/bread/YZmbmJhy
2.布谷鸟CS优化LSTM负荷预测（python3.6 tensorflow1.x框架）：https://mbd.pub/o/bread/YZmbmZlw
2.布谷鸟CS优化LSTM负荷预测（python3.6 tensorflow2.x框架）：https://mbd.pub/o/bread/Y52XmJhr
3.麻雀SSA优化LSTM负荷预测（python3.6 tensorflow1.x框架）：https://mbd.pub/o/bread/YZmbmZpr
4.鲸鱼WOA优化LSTM负荷预测（python36，tensorflow1.x）：https://mbd.pub/o/bread/YZqTmJ5q
4.鲸鱼WOA优化LSTM负荷预测（python36，tensorflow2.x）：https://mbd.pub/o/bread/YpaWmp1r
5.基于重要性分析与麻雀优化LSTM的回归分析(py36，tensorflow2.x框架)： https://mbd.pub/o/bread/YZqTk5hx
6.MATLAB量子粒子群QPSO优化LSTM负荷预测：https://mbd.pub/o/bread/YZmbmZZr
7.MATLAB灰狼GWO优化LSTM负荷预测：https://mbd.pub/o/bread/YZmbmZdr
8.MATLAB鲸鱼WOA优化LSTM负荷预测：https://mbd.pub/o/bread/YZqTmJtw
9.MATLAB基于同步挤压小波降噪与贝叶斯优化长短时记忆网络的时间序列预测：https://mbd.pub/o/bread/YZmck5pr
10.MATLAB基于同步挤压小波降噪与改进麻雀优化长短时记忆网络的时间序列预测：https://mbd.pub/o/bread/YZmbmZpt
11.MATLAB算术优化算法AOA优化LSTM时间序列预测：https://mbd.pub/o/bread/YZ6VlJxq
12.MATLAB黏菌优化SMA优化LSTM时间序列预测：https://mbd.pub/o/bread/YZ6Xmp1w
13.MATLAB蝴蝶优化BOA优化LSTM时间序列预测：https://mbd.pub/o/bread/mbd-YpaXlJlq
14.MATLAB均衡优化器EO优化LSTM时间序列预测：https://mbd.pub/o/bread/YpaXlJlp
15.MATLAB人工大猩猩部队优化GTO优化LSTM时间序列预测：https://mbd.pub/o/bread/YZuUm5xv
16.MATLAB蜜獾优化算法HBA优化LSTM时间序列预测：https://mbd.pub/o/bread/YpaZl5Zy
17.MATLAB哈里斯鹰优化算法HHO优化LSTM时间序列预测：https://mbd.pub/o/bread/YpaZl5ds
18.MATLAB鮣鱼优化算法ROA优化LSTM时间序列预测：https://mbd.pub/o/bread/YpaZl5dx

8） 多核学习

混合核极限学习机回归
1.MATLAB贝叶斯优化混合核极限学习机用于回归预测 ：https://mbd.pub/o/bread/YZmck5dx
2.MATLAB粒子群PSO优化混合核极限学习机用于回归预测：https://mbd.pub/o/bread/YZqZmJ5v
3.MATLAB鲸鱼WOA优化混合核极限学习机用于回归预测：https://mbd.pub/o/bread/YZqZmZZs
4.MATLAB遗传GA优化混合核极限学习机用于回归预测：https://mbd.pub/o/bread/YZqZmZdp
5.MATLAB麻雀SSA优化混合核极限学习机用于回归预测：https://mbd.pub/o/bread/YZqZmZdv

混合核极限学习机分类
6.MATLAB贝叶斯优化混合核极限学习机用于分类：https://mbd.pub/o/bread/YZmck5lw （注意是分类哈） 混合核支持向量机
7.MATLAB贝叶斯优化混合核支持向量机用于回归预测：https://mbd.pub/o/bread/YZmblZZs （注意是混合核支持向量机）
8.MATLAB贝叶斯优化混合核支持向量机用于分类：https://mbd.pub/o/bread/YZmck5hq

9）Torch版本智能算法优化深度学习超参数

1、Pytorch的遗传优化DBN超参数回归：https://mbd.pub/o/bread/YZuXmJlq
2、Pytorch的遗传优化DBN超参数分类：https://mbd.pub/o/bread/YZuXmJpq
3、Pytorch的鲸鱼WOA优化1DCNN超参数分类：https://mbd.pub/o/bread/Y5ublZ5q

10)matlab的cnn超参数优化

1.MATLAB麻雀优化CNN超参数分类：https://mbd.pub/o/bread/YZuYmpxu
2.MATLAB麻雀优化CNN超参数回归：https://mbd.pub/o/bread/YZuZkppy
3.MATLAB灰狼优化CNN超参数回归：https://mbd.pub/o/bread/Y5uZkplr
4.MATLAB鲸鱼优化CNN超参数回归：https://mbd.pub/o/bread/Y5aWmZZr

11）最优特征筛选

1.python基于特征选择（PSO+CTree）的网络入侵检测：https://mbd.pub/o/bread/YZuZkp9p
2.python基于鲸鱼woa优化的变分模态分解vmd超参数选择：https://mbd.pub/o/bread/mbd-Y5uamZxx
3.matlab基于人工大猩猩部队GTO优化的共振稀疏分解RSSD超参数选择：https://mbd.pub/o/bread/Y5uam5hs
4.matlab基于鲸鱼优化WOA优化的共振稀疏分解RSSD超参数选择：https://mbd.pub/o/bread/Y52Ymp1s
2.python基于白鲸bwo优化的变分模态分解vmd超参数选择：https://mbd.pub/o/bread/Y52Ym51r

12）自定义layer

基于tf2.0的小波长短时记忆网络:https://mbd.pub/o/bread/YZuZkp5v

13)matlab的cnn-lstm超参数优化

1.MATLAB人工大猩猩部队GTO优化CNN-LSTM用于多变量负荷预测：https://mbd.pub/o/bread/mbd-Y5aTkpty
2.MATLAB蜜獾优化算法HBA优化CNN-LSTM用于多变量负荷预测：https://mbd.pub/o/bread/Y5aWmZlr
3.matlab的算术优化算法AOA优化cnn_lstm多变量时间序列预测：https://mbd.pub/o/bread/Y5aWmZhr

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23

2D-DCAE-RESNET-for-image-de-noising

提出基于误差学习的二卷卷积降噪自动编码器，采用tensorflow1.x 框架，详情移步我的csdn博客https://blog.csdn.net/qq_41043389/article/details/105037283
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24

Network-intrusion-detection-based-on-feature-selection-PSO-CTree-

Aiming at the KDD data set using onehot encoding for feature value conversion, there is a problem of redundant features. This paper proposes to use particle swarm optimized algorithm combined with decision tree to achieve feature selection and detection classification.
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25

IPSO_GRU-Regression

Using tensorflow1.x/python to implement a IPSO_GRU for regression，IPSO's here, it was mainly uesd to optimize hyper-parameters includess learning rate ,hidden_layer's number.
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26

EEMD-IWOA-LSSVM-for-power-prediction

（MATLAB CODE） Establishing a time series forecasting model for PV power prediction based on LSSVM，Due to the autocorrelation of the original power data sequence, the predicted value and the actual value lag, so EEMD(EMD/CEEMD) is used to decompose the original sequence, and then the decomposed components are modeled in turn. To further mention accuracy, the improved whale-optimization-algorithm （IWOA）is used to optimize the lssvm。 (MATLAB代码)采用最小二乘支持向量机LSSVM建立光伏功率预测的时间序列预测模型，由于数据本身的自相关性，导致得到的预测值与实际值存在滞后。针对这个问题，首先对光伏功率序列进行EEMD得到imf分量，然后对各分量进行LSSVM建模。最后为进一步提高精度，采用改进的鲸鱼优化算法优化的lsvvm的核参数与惩罚参数，需要的可以加我qq2919218574 ，程序是matlab代码
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27

Imporved-weed-algorithm-to-optimize-BP-network

Adopt improved weed algorithm to optimize the weight of BP network and achieve better regression fitting
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28

CS-LSTM-for-powerload-forecast

python3.3 tensorflow1.x，Using LSTM to construct a time series forecast model for short-term power forecast, and using Cuckoo Search(CS) algorthim to optimize the iteration,learning rate, hidden-layer nodes.
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29

Rotation-box-dimension-tool-and-dimension-file-adjustment

Rotation box dimension tool and dimension file adjustment
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30

Time-frequency-graph-transformation-toolbox

Time-frequency graph transformation toolbox
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31

EMD-SA-DBN-for-Wind-speed-forecast

Establishing a time series forecasting model for wind speed prediction based on DBN，Due to the autocorrelation of the wind speed sequence, the predicted value and the actual value lag, so EMD is used to decompose the wind speed sequence, and then the decomposed components are modeled in turn. To further mention accuracy, the simulated annealing algorithm is used to optimize the DBN。
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32

SSA-DBN-Regression

麻雀优化DBN用于回归，最近写了好多麻雀优化的，我就不写博客了，需要买代码直接加我qq2919218574。Sparrow optimized DBN for regression. Recently I wrote a lot of sparrow optimized ones. I won’t write a blog anymore. If you need to buy the code and please add me qq2919218574 directly.
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33

BiLSTM-attention-for-power-forecast

python3，tensorflow1.x，利用双向长短时记忆网络加注意力机制构建时间序列预测模型，然后用功率数据集进行验证，需要的加我qq2919218574，还可以用各自优化算法进行该模型的超参数寻优，收费的哟
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34

sparrow-search-algorithm-for-kelm-regression

The sparrow search algorithm is a new heuristic search algorithm proposed in 2020. I use python to write and use it to optimize the extreme learning machine and the nuclear extreme learning machine to complete the regression task.
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35

GA-DBN-for-multi-output-regression

Use DBN to complete the multi-input multi-output regression task, and at the same time, use the genetic algorithm to optimize the hidden layer parameter selection
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36

Exchange-rate-forecast-based-on-EMD-and-BP

BP is used for time series modeling of exchange rate prediction. For the correlation problems in the sequence, EMD is used to decompose the original sequence, then each subsequence is decomposed to establish a BP model, and finally the prediction results of each subsequence are added as the final the result of
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37

GA-DBN-Classification

Using MATLAB and DeepBeliefNetworksToolbox to implement a GA-DBN for classification task, Genetic algorithm（GA） is used to optimize the neuron's number of each hidden-layer
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38

WOA-LSTM-time-series-forecast

python3.3 tensorflow1.x，Using LSTM to construct a time series forecast model for short-term power forecast, and using Whale optimization algorithmto （woa）optimize the iteration,learning rate, hidden-layer nodes. 利用tensorflow1.x，使用lstm进行短期电力负荷预测建模，并采用鲸鱼算法对lstm的迭代次数，学习率 隐含层节点数进行寻优，详情看我博客： https://blog.csdn.net/qq_41043389（这个是布谷鸟的，我改了优化算法，所以就没写博客） 需要的代码的可以加我qq2919218574,出售的，不是免费，不是免费，不是免费
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39

GA-DA-for-feature-select

In the Python environment, the genetic algorithm and the dragonfly algorithm are first combined to form a modified dragonfly genetic algorithm, and the effectiveness of the improved method is verified by using the test function. Secondly, the improved algorithm and decision tree are applied to feature selection (classification task) to remove redundant features from the original features
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40

VMD-SSA-LSSVM-for-power-forecast

In this paper, LSSVM is used for short-term power load forecasting, and a short-term power load forecasting model based on LSSVM is proposed. At the same time, a Sparrow Algorithm (SSA) model is established to optimize the parameters of LLSVM to improve the forecasting accuracy. However, studies have shown that if a time series forecast model is built directly on the original series, the forecast data will lag the actual data. Such a model is meaningless. This is mainly due to the autocorrelation in the time series data, so I use VMD decomposition The method decomposes the original sequence, then models each sequence separately, and finally adds the results of each sequence test set as the final result. The comparative analysis results show that the prediction accuracy of this model is better than that of many other prediction models, and this model shows better performance in short-term load forecasting.
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41

sparrow-search-algorithm-for-cnn-s-hyper-parameters

在matlab2018a中，采用麻雀算法SSA对CNN的超参数进行寻优，包括卷积核大小，数量，全连接层神经元数，迭代次数，学习率等，有回归有分类，需要的加我qq2919218574，代码收费的哟
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42

SSA-DBN-classification

Combining the advantages of deep belief network (DBN) in extracting features and processing high-dimensional and non-linear data, a classification method based on deep belief network is proposed. This method uses the Fourier spectrum (FFT) of the original time domain signal to train a deep confidence network through deep learning. Its advantage is that the method does not need to set parameters when performing FFT on the signal, and directly uses all spectral components for modeling, so there is no need for complexity The feature selection method has strong versatility and adaptability. Finally, in order to further enhance the classification accuracy of DBN, the Sparrow Search Algorithm (SSA) is used to optimize the weight parameters of DBN. The experimental results show that the method proposed in this paper can effectively improve the classification and recognition accuracy.
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43

EMD-GA-DBN-Regression

（MATLAB CODE） Establishing a time series forecasting model for wind speed prediction based on DBN，Due to the autocorrelation of the wind speed sequence, the predicted value and the actual value lag, so EMD is used to decompose the wind speed sequence, and then the decomposed components are modeled in turn. To further mention accuracy, the simulated annealing algorithm is used to optimize the DBN。 (MATLAB代码)采用深度置信网络DBN建立风速预测的时间序列预测模型，由于数据本身的自相关性，导致得到的预测值与实际值存在滞后。针对这个问题，首先对风速序列进行EMD得到imf分量，然后对各分量进行DBN建模。最后为进一步提高精度，采用模拟退火算法的DBN各隐含层节点进行优化，需要的可以加我qq2919218574 ，效果可以看csdn博客https://blog.csdn.net/qq_41043389/article/details/104517495 程序是matlab代码
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44

SWT-ISSA-LSTM-time-series-forecast

The long and short-term memory network LSTM has attracted attention in recent years for the short-term time series prediction problem. However, because this method is a deep learning method, it usually faces the influence of many hyperparameters. As we all know, the setting of deep learning hyperparameters is not There is no clear guideline, and most of them use empirical methods, such as learning rate 1e-3, 1e-4, etc. The number of iterations is set according to the change of loss curve, etc. This method is simple to try and find the effect is better A group of people, time-consuming and labor-intensive. To this end, this paper will use the improved sparrow search algorithm ISSA to optimize the parameters of LSTM, while using synchronous squeeze wavelet SWT to filter the original data to reduce noise, and use the data of the noise reduction meeting to model, and finally use the example verification to show that, The prediction effect of the SWT-ISSA-LSTM model is better. 长短时记忆网络LSTM在针对短时时间序列预测问题上近来年受到大家的关注，但由于该方法为深度学习方法，通常面临着众多超参数的影响，而众所周知，关于深度学习超参数的设置并没有一直明确的指导方针，大多采用经验方法，比如学习率1e-3,1e-4啥的，迭代次数根据loss曲线的变化等进行设置，这种方法说简单的就是无限尝试，找到效果比较好的一组，耗时耗力。为此，本文将采用改进的麻雀搜索算法ISSA对 LSTM进行参数优化 , 同时采用同步挤压小波SWT对原始数据进行滤波降噪，并采用降噪会的数据进行建模，最后用实例验证表明 ,SWT-ISSA-LSTM 模型的预测效果更佳。
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