지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.14 No.2
- 발행연도
- 2025.4
- 수록면
- 205 - 217 (13page)
- DOI
- 10.5573/IEIESPC.2025.14.2.205
이용수
초록· 키워드
오류제보하기
Belt conveyors are common transportation equipment, and optimizing their performance is an effective measure to achieve intelligence and efficient transportation. The application of new technologies has increased the controllable parameters and operational parameters to be optimized in belt transportation systems, making performance prediction and multi-objective optimization problems more challenging. Traditional response prediction and optimization methods have become increasingly inadequate to meet research requirements. The emergence and development of machine learning methods and modern intelligent optimization methods have provided new directions for the prediction and optimization research of belt transportation systems. Therefore, in order to improve the system prediction and performance optimization of belt transportation systems, this study proposes an interpretive machine learning approach based on improved neural networks and support vector machines, which combines the optimization of network initial weight threshold and training set test set, and establishes a high-performance predictive interpretive machine learning model. The results indicate that under different combinations of training and validation set partitioning, all evolutionary processes in the trajectory of the initial weight threshold optimization of the proposed comprehensive neural network optimization method reach their optimal state in the 17th generation. The optimization algorithm stably converged to the optimal value after only 14 generations of evolution, resulting in a mean square error of 0.011678 for the optimal network prediction, while the mean square error of all individuals in the initial population was 0.016845. If the average network prediction performance of all individuals in the initial population is taken as the basic standard, a comprehensive optimization algorithm is used to optimize the network’s prediction performance to 29.6%. After the 10th reinforcement training, the prediction error of the support vector machine decreases to 0.99%, and the gross of simulations is 65. The designed method helps to achieve intelligent transportation systems, unmanned operation and high performance, and can achieve sustainable development while ensuring transportation safety and efficiency.
목차
Abstract
1. Introduction
2. Related Works
3. Predictive High-performance BCS Applications Based on Interpretive Machine Learning Models
4. Verification of Predicting High Performance Transportation Systems Based on Interpretable Machine Learning Models
5. Conclusion
References
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