Balance optimization method of energy shipping based on Hopfield neural network
It is of great significance for the optimization of transportation strategy to study the methods of energy shipping scheduling. Based on the Hopfield neural network (HNN) theory, this paper proposes Hopfield neural network energy transportation path optimization algorithm with improved activation fu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-03-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016822008225 |
| _version_ | 1797884469427830784 |
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| author | Yuan Ji Linlin Wang Danlan Xie |
| author_facet | Yuan Ji Linlin Wang Danlan Xie |
| author_sort | Yuan Ji |
| collection | DOAJ |
| description | It is of great significance for the optimization of transportation strategy to study the methods of energy shipping scheduling. Based on the Hopfield neural network (HNN) theory, this paper proposes Hopfield neural network energy transportation path optimization algorithm with improved activation function, which solves the problems of poor mapping ability, low flexibility and high sensitivity of neurons near zero to input of traditional activation function. The improved activation function can reduce the derivative value of the activation function and the sensitivity of neurons near zero to the input value by flexibly adjusting the steepness, positioning and mapping range of energy sea transportation at the same time. The experimental results show that: under the same initial conditions, the model proposed in this paper shows better error code performance, and the data length of the required transmission sequence is shorter. When sending 8PSK (Phase Shift Keying) signal with a data length of N = 40, the error rate of the 2-path synthesized random channel is less than 0.112. The error code performance and convergence speed of the energy shipping path planning algorithm are improved effectively. |
| first_indexed | 2024-04-10T04:08:15Z |
| format | Article |
| id | doaj.art-52d86c3583b3417f9f03d2ef9469ef05 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-10T04:08:15Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-52d86c3583b3417f9f03d2ef9469ef052023-03-13T04:15:15ZengElsevierAlexandria Engineering Journal1110-01682023-03-0167171181Balance optimization method of energy shipping based on Hopfield neural networkYuan Ji0Linlin Wang1Danlan Xie2School of Information and Business Management, Dalian Neusoft University of Information, Dalian 116023, ChinaSchool of Digital Arts & Design, Dalian Neusoft University of Information, Dalian 116023, ChinaCollege of Artificial Intelligence and E-Commerce, Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou 311599, China; Corresponding author.It is of great significance for the optimization of transportation strategy to study the methods of energy shipping scheduling. Based on the Hopfield neural network (HNN) theory, this paper proposes Hopfield neural network energy transportation path optimization algorithm with improved activation function, which solves the problems of poor mapping ability, low flexibility and high sensitivity of neurons near zero to input of traditional activation function. The improved activation function can reduce the derivative value of the activation function and the sensitivity of neurons near zero to the input value by flexibly adjusting the steepness, positioning and mapping range of energy sea transportation at the same time. The experimental results show that: under the same initial conditions, the model proposed in this paper shows better error code performance, and the data length of the required transmission sequence is shorter. When sending 8PSK (Phase Shift Keying) signal with a data length of N = 40, the error rate of the 2-path synthesized random channel is less than 0.112. The error code performance and convergence speed of the energy shipping path planning algorithm are improved effectively.http://www.sciencedirect.com/science/article/pii/S1110016822008225Hopfield neural networkMaritime transportation of energyBalance optimizationObject detection |
| spellingShingle | Yuan Ji Linlin Wang Danlan Xie Balance optimization method of energy shipping based on Hopfield neural network Alexandria Engineering Journal Hopfield neural network Maritime transportation of energy Balance optimization Object detection |
| title | Balance optimization method of energy shipping based on Hopfield neural network |
| title_full | Balance optimization method of energy shipping based on Hopfield neural network |
| title_fullStr | Balance optimization method of energy shipping based on Hopfield neural network |
| title_full_unstemmed | Balance optimization method of energy shipping based on Hopfield neural network |
| title_short | Balance optimization method of energy shipping based on Hopfield neural network |
| title_sort | balance optimization method of energy shipping based on hopfield neural network |
| topic | Hopfield neural network Maritime transportation of energy Balance optimization Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S1110016822008225 |
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