首页|Studies in the Area of Pattern Recognition and Artificial Intelligence Reported from Taizhou University (Tool Wear Prediction Based on LSTM and Deep Residual Ne twork)
Studies in the Area of Pattern Recognition and Artificial Intelligence Reported from Taizhou University (Tool Wear Prediction Based on LSTM and Deep Residual Ne twork)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on pattern recognition a nd artificial intelligence have been presented. According to news reporting orig inating from Zhejiang, People's Republic of China, by NewsRx correspondents, res earch stated, "To improve the accuracy and efficiency of tool wear predictions, this study proposes a tool wear prediction model called LSTM_ResNet which is based on the long short-term memory (LSTM) network and the Residual Ne twork (ResNet)."The news editors obtained a quote from the research from Taizhou University: "Th e model utilizes LSTM layers for processing, where the first block and loop bloc ks serve as the core modules of the deep residual network. The model employs a s eries of methods including convolution, batch normalization (BN), and Rectified Linear Unit (ReLU) to enhance the model's expression and prediction capabilities . The performance of the LSTM_ResNet model was evaluated using expe rimental data from the PHM2010 datasets and two different depths (64 and 128 lay ers), training both LSTM_ResNet models for 200 epochs. The 64-layer model's root mean square error (RMSE) values are 3.36, 4.35, and 3.59, and the mean absolute error (MAE) values are 2.42, 2.85, and 2.21; using 128 layers, the RMSE values are 3.66, 3.99, and 3.77, and the MAE values are 2.49, 2.73, and 3. 01."
Taizhou UniversityZhejiangPeople's R epublic of ChinaAsiaMachine LearningPattern Recognition and Artificial Int elligence
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