<p> The renewable wind power sources are difficult to be predicted in view of the fluctuating factors such as wind bearing, pressure, wind speed, and humidity of the surrounding atmosphere. An attempt is made in this paper to propose a difference method to build a neural network and a long short term memory (LSTM) model for wind power prediction. First, the correlation of each data is analyzed and then per-forming difference processing on the original data to solve the problem that the original data cannot be analyzed by probability distribution. The prediction is made by building the neural network and LSTM and feeding the original data and the difference-processed data into the neural network model respective-ly. Finally, the data are added for validation, and the raw data used include wind power data in Belgium from November 1, 2019 to November 30, 2019.The experimental results show that the LSTM prediction accuracy is improved by 178.67%, and is effective in predicting long-term wind power data with 216.06% accuracy improvement, the neural network prediction accuracy is improved by 154.07%, and the short-term wind power prediction accuracy is improved by 228%.</p> <p> </p>
<p>In recent decades, the neural network approach to predicting yarn quality indicators has been recognized for its high accuracy. Although using neural networks to predict yarn quality indicators has a high accuracy advantage, its relationship understanding between each input parameter and yarn quality indicators may need to be corrected, i.e., increasing the raw cotton strength, the final yarn strength remains the same or decreases. Although this is normal for prediction algorithms, actual production need is more of a trend for individual parameter changes to predict a correct yarn, i.e., raw cotton strength increase should correspond to yarn strength increase. This study proposes a yarn quality prediction method based on actual production by combining nearest neighbor, particle swarm optimization, and expert experience to address the problem. We Use expert experience to determine the upper and lower limits of parameter weights, the particle swarm optimization finds the optimal weights, and then the nearest neighbor algorithm is used to calculate the predicted values of yarn indexes. Finally, the current problems and the rationality of the method proposed in this paper are verified by experiments.</p> <p> </p>
<p>This study aimed to propose a method to predict yarn unevenness grounded on the generalized regression neural network and traditional neural network model to further improve the prediction accuracy. The yarn unevenness model was constructed. Under this model, a three-layer neural network, a four-layer neural network, a five-layer neural network, and a generalized regression neural network were designed. Finally, Python was used for training and simulation. The training parameters and the three network models data were made consistent to ensure the comparability of the results. The results showed that using the yarn unevenness model, the average relative error of the four-layer neural network to cut down 0.87% compared with that of the three-layer neural network. Compared with the five-layer neural network, the four-layer neural network performance was not much different, but the running speed was increased by 46.05%. Compared with the four-layer neural network, the average relative error of the generalized regression neural network was reduced by 0.57%, the mean square error was reduced by 0.98%, he root mean square error was reduced by 4.76%, and the running speed was increased by 74.70%.</p> <p> </p>
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