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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

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Strip thickness prediction method based on improved border collie optimizing LSTM.

Lijie Sun¹, Lin Zeng², Hongjuan Zhou³

¹School of Electronics and Information Engineering, Taizhou University, Taizhou, Zhejiang, China.

Peerj. Computer Science

|November 25, 2022

Summary

This summary is machine-generated.

This study introduces an improved Border Collie Optimization (IBCO) algorithm to optimize Long Short-Term Memory (LSTM) networks for accurate strip thickness prediction. The novel IBCO-LSTM method significantly enhances prediction performance in the rolling industry.

Keywords:

Border Collie Feature selection LSTM Mutual information Strip thickness

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Area of Science:

Materials Science
Industrial Engineering
Data Science

Background:

Strip thickness accuracy is a critical quality indicator in the rolling industry.
Precise control of strip thickness is essential for high-quality strip products.

Purpose of the Study:

To develop an advanced method for predicting strip thickness.
To improve the accuracy and reliability of strip thickness control in manufacturing processes.

Main Methods:

A novel Improved Border Collie Optimization (IBCO) algorithm was developed, incorporating chaotic mapping and dynamic weighting.
Long Short-Term Memory (LSTM) networks were employed for their effectiveness in handling time-series data.
The IBCO algorithm was used to optimize LSTM hyperparameters, including hidden neurons and learning rate, creating the IBCO-LSTM model.

Main Results:

The proposed IBCO-LSTM model demonstrated excellent prediction performance on actual strip data.
Experimental results validated the superior capability of IBCO-LSTM in strip thickness prediction.

Conclusions:

The IBCO-LSTM method offers a significant advancement in strip thickness prediction accuracy.
This approach provides a robust solution for enhancing quality control in the rolling industry.