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ZMLloveMLN's repositories

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

Apache-2.0000

GitHub520

:kissing_heart: 让你“爱”上 GitHub，解决访问时图裂、加载慢的问题。（无需安装）

000

netron

Visualizer for neural network, deep learning and machine learning models

MIT000

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.

000

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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