Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm
With the increasing demands for vast amounts of data and high-speed signal transmission, the use of multi-conductor transmission lines is becoming more common. The impact of transmission lines on signal transmission is thus a key issue affecting the performance of high-speed digital systems. To solv...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2018-06-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/1.5025504 |
| _version_ | 1818194746100678656 |
|---|---|
| author | Yunlei Yang Muzhou Hou Jianshu Luo Taohua Liu |
| author_facet | Yunlei Yang Muzhou Hou Jianshu Luo Taohua Liu |
| author_sort | Yunlei Yang |
| collection | DOAJ |
| description | With the increasing demands for vast amounts of data and high-speed signal transmission, the use of multi-conductor transmission lines is becoming more common. The impact of transmission lines on signal transmission is thus a key issue affecting the performance of high-speed digital systems. To solve the problem of lossless two-conductor transmission line equations (LTTLEs), a neural network model and algorithm are explored in this paper. By selecting the product of two triangular basis functions as the activation function of hidden layer neurons, we can guarantee the separation of time, space, and phase orthogonality. By adding the initial condition to the neural network, an improved extreme learning machine (IELM) algorithm for solving the network weight is obtained. This is different to the traditional method for converting the initial condition into the iterative constraint condition. Calculation software for solving the LTTLEs based on the IELM algorithm is developed. Numerical experiments show that the results are consistent with those of the traditional method. The proposed neural network algorithm can find the terminal voltage of the transmission line and also the voltage of any observation point. It is possible to calculate the value at any given point by using the neural network model to solve the transmission line equation. |
| first_indexed | 2024-12-12T01:07:11Z |
| format | Article |
| id | doaj.art-ec0c10e4c2d147a9a724d2eeb9f2327b |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-12-12T01:07:11Z |
| publishDate | 2018-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-ec0c10e4c2d147a9a724d2eeb9f2327b2022-12-22T00:43:34ZengAIP Publishing LLCAIP Advances2158-32262018-06-0186065010065010-1410.1063/1.5025504030806ADVNeural network method for lossless two-conductor transmission line equations based on the IELM algorithmYunlei Yang0Muzhou Hou1Jianshu Luo2Taohua Liu3School of Mathematics and Statistics, Central South University, Changsha 410083, ChinaSchool of Mathematics and Statistics, Central South University, Changsha 410083, ChinaCollege of Science, National University of Defense Technology, Changsha 410073, ChinaSchool of Mathematics and Statistics, Central South University, Changsha 410083, ChinaWith the increasing demands for vast amounts of data and high-speed signal transmission, the use of multi-conductor transmission lines is becoming more common. The impact of transmission lines on signal transmission is thus a key issue affecting the performance of high-speed digital systems. To solve the problem of lossless two-conductor transmission line equations (LTTLEs), a neural network model and algorithm are explored in this paper. By selecting the product of two triangular basis functions as the activation function of hidden layer neurons, we can guarantee the separation of time, space, and phase orthogonality. By adding the initial condition to the neural network, an improved extreme learning machine (IELM) algorithm for solving the network weight is obtained. This is different to the traditional method for converting the initial condition into the iterative constraint condition. Calculation software for solving the LTTLEs based on the IELM algorithm is developed. Numerical experiments show that the results are consistent with those of the traditional method. The proposed neural network algorithm can find the terminal voltage of the transmission line and also the voltage of any observation point. It is possible to calculate the value at any given point by using the neural network model to solve the transmission line equation.http://dx.doi.org/10.1063/1.5025504 |
| spellingShingle | Yunlei Yang Muzhou Hou Jianshu Luo Taohua Liu Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm AIP Advances |
| title | Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm |
| title_full | Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm |
| title_fullStr | Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm |
| title_full_unstemmed | Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm |
| title_short | Neural network method for lossless two-conductor transmission line equations based on the IELM algorithm |
| title_sort | neural network method for lossless two conductor transmission line equations based on the ielm algorithm |
| url | http://dx.doi.org/10.1063/1.5025504 |
| work_keys_str_mv | AT yunleiyang neuralnetworkmethodforlosslesstwoconductortransmissionlineequationsbasedontheielmalgorithm AT muzhouhou neuralnetworkmethodforlosslesstwoconductortransmissionlineequationsbasedontheielmalgorithm AT jianshuluo neuralnetworkmethodforlosslesstwoconductortransmissionlineequationsbasedontheielmalgorithm AT taohualiu neuralnetworkmethodforlosslesstwoconductortransmissionlineequationsbasedontheielmalgorithm |