On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks
The major function of dynamic networks is to sense information from the environment and process the information to the downstream. Therefore how to measure the information transmission ability of a dynamic network is an important topic to evaluate network performance. However, the dynamic behavior o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2012-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/14/9/1652 |
| _version_ | 1798006778594590720 |
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| author | Ying-Po Lin Bor-Sen Chen |
| author_facet | Ying-Po Lin Bor-Sen Chen |
| author_sort | Ying-Po Lin |
| collection | DOAJ |
| description | The major function of dynamic networks is to sense information from the environment and process the information to the downstream. Therefore how to measure the information transmission ability of a dynamic network is an important topic to evaluate network performance. However, the dynamic behavior of a dynamic network is complex and, despite knowledge of network components, interactions and noises, it is a challenge to measure the information transmission ability of a dynamic network, especially a nonlinear stochastic dynamic network. Based on nonlinear stochastic dynamic system theory, the information transmission ability can be investigated by solving a Hamilton-Jacobi inequality (HJI)-constrained optimization problem. To avoid difficulties associated with solving a complex HJI-constrained optimization problem for information transmission ability, the Takagi-Sugeno (T-S) fuzzy model is introduced to approximate the nonlinear stochastic dynamic network by interpolating several local linear stochastic dynamic networks so that a HJI-constrained optimization problem can be replaced by the linear matrix inequalities (LMIs)-constrained optimization problem. The LMI problem can then be efficiently solved for measuring information transmission ability. We found that a more stable (robust) dynamic network has less information transmission ability, and vice versa. Finally, an example of a biochemical network in cellular communication is given to illustrate the measurement of information transmission ability and to confirm the results by using Monte Carlo simulations. |
| first_indexed | 2024-04-11T13:01:05Z |
| format | Article |
| id | doaj.art-ed33ce7f187b4daabf8e38dd1104897d |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:01:05Z |
| publishDate | 2012-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-ed33ce7f187b4daabf8e38dd1104897d2022-12-22T04:22:57ZengMDPI AGEntropy1099-43002012-09-011491652167010.3390/e14091652On the Information Transmission Ability of Nonlinear Stochastic Dynamic NetworksYing-Po LinBor-Sen ChenThe major function of dynamic networks is to sense information from the environment and process the information to the downstream. Therefore how to measure the information transmission ability of a dynamic network is an important topic to evaluate network performance. However, the dynamic behavior of a dynamic network is complex and, despite knowledge of network components, interactions and noises, it is a challenge to measure the information transmission ability of a dynamic network, especially a nonlinear stochastic dynamic network. Based on nonlinear stochastic dynamic system theory, the information transmission ability can be investigated by solving a Hamilton-Jacobi inequality (HJI)-constrained optimization problem. To avoid difficulties associated with solving a complex HJI-constrained optimization problem for information transmission ability, the Takagi-Sugeno (T-S) fuzzy model is introduced to approximate the nonlinear stochastic dynamic network by interpolating several local linear stochastic dynamic networks so that a HJI-constrained optimization problem can be replaced by the linear matrix inequalities (LMIs)-constrained optimization problem. The LMI problem can then be efficiently solved for measuring information transmission ability. We found that a more stable (robust) dynamic network has less information transmission ability, and vice versa. Finally, an example of a biochemical network in cellular communication is given to illustrate the measurement of information transmission ability and to confirm the results by using Monte Carlo simulations.http://www.mdpi.com/1099-4300/14/9/1652information transmission abilitynonlinear stochastic dynamic networkHJILMI, T-S fuzzy modelnetwork performance |
| spellingShingle | Ying-Po Lin Bor-Sen Chen On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks Entropy information transmission ability nonlinear stochastic dynamic network HJI LMI, T-S fuzzy model network performance |
| title | On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks |
| title_full | On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks |
| title_fullStr | On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks |
| title_full_unstemmed | On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks |
| title_short | On the Information Transmission Ability of Nonlinear Stochastic Dynamic Networks |
| title_sort | on the information transmission ability of nonlinear stochastic dynamic networks |
| topic | information transmission ability nonlinear stochastic dynamic network HJI LMI, T-S fuzzy model network performance |
| url | http://www.mdpi.com/1099-4300/14/9/1652 |
| work_keys_str_mv | AT yingpolin ontheinformationtransmissionabilityofnonlinearstochasticdynamicnetworks AT borsenchen ontheinformationtransmissionabilityofnonlinearstochasticdynamicnetworks |