A novel brain-inspired approach based on spiking neural network for cooperative control and protection of multiple trains
The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collab...
Saved in:
| Published in: | Engineering applications of artificial intelligence Vol. 127; p. 107252 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.01.2024
|
| Subjects: | |
| ISSN: | 0952-1976, 1873-6769 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collaborative functionalities of various brain regions, harnessing the power of spiking neural networks. The controller’s primary tasks include reference velocity tracking, cooperative control, and active protection, with a special focus on cooperative protection within distinct operational modes. Furthermore, the fundamental principles of incorporating spiking neural networks into train control, such as coding and decoding mechanisms, are expounded. The overarching controller is partitioned into two principal functional segments. The first segment involves emulating the prefrontal cortex (PFC) for reference velocity tracking and active protection against overspeed and collisions through motor control and movement planning. The second segment employs a cerebellum-inspired network for cooperative control. Additionally, the brain-inspired network introduced in this study undergoes training utilizing biologically-inspired mechanisms, incorporating dopamine and pertinent teaching signals to facilitate realistic synaptic modifications. Simulation results in several scenarios validate the proposed approach. |
|---|---|
| AbstractList | The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a comprehensive and efficient integrated solution. To address this challenge, this paper introduces a brain-inspired controller that emulates the collaborative functionalities of various brain regions, harnessing the power of spiking neural networks. The controller’s primary tasks include reference velocity tracking, cooperative control, and active protection, with a special focus on cooperative protection within distinct operational modes. Furthermore, the fundamental principles of incorporating spiking neural networks into train control, such as coding and decoding mechanisms, are expounded. The overarching controller is partitioned into two principal functional segments. The first segment involves emulating the prefrontal cortex (PFC) for reference velocity tracking and active protection against overspeed and collisions through motor control and movement planning. The second segment employs a cerebellum-inspired network for cooperative control. Additionally, the brain-inspired network introduced in this study undergoes training utilizing biologically-inspired mechanisms, incorporating dopamine and pertinent teaching signals to facilitate realistic synaptic modifications. Simulation results in several scenarios validate the proposed approach. |
| ArticleNumber | 107252 |
| Author | Zhang, Zixuan Tan, Ligang Dong, Hairong Song, Haifeng Wang, Hongwei |
| Author_xml | – sequence: 1 givenname: Zixuan surname: Zhang fullname: Zhang, Zixuan organization: Beijing Jiaotong University, Beijing 100044, China – sequence: 2 givenname: Haifeng surname: Song fullname: Song, Haifeng organization: Beihang University, Beijing 100191, China – sequence: 3 givenname: Hongwei surname: Wang fullname: Wang, Hongwei organization: Beijing Jiaotong University, Beijing 100044, China – sequence: 4 givenname: Ligang surname: Tan fullname: Tan, Ligang organization: China State Railway Group Co., Ltd., Beijing 100844, China – sequence: 5 givenname: Hairong surname: Dong fullname: Dong, Hairong email: hrdong@bjtu.edu.cn organization: Beijing Jiaotong University, Beijing 100044, China |
| BookMark | eNqFkMtKAzEUhoMo2FZfQfICU3OZTGbAhaV4g4IbXYdMJqlpp8mQpJW-vRmqGzddnVz4_sP_TcGl804DcIfRHCNc3W_m2q3lMEg7J4jQ_MgJIxdggmtOi4pXzSWYoIaRAje8ugbTGDcIIVqX1QQcF9D5g-5hG6R1hXVxsEF3MOcFL9UXbGXMV-9g_that4ZO74Ps80jfPmyh8QEq7wcdZLIHnc8uBd9D6TqYI5JWyWbaG7jb98kOvYZpXBVvwJWRfdS3v3MGPp-fPpavxer95W25WBWKYpKKkpqKsabExFAuTVdzjSWtZcMMIw3tSm5K1dZ1SxCvMOs4ZS0tGZdlW1NaEzoD1SlXBR9j0EYMwe5kOAqMxChQbMSfQDEKFCeBGXz4Byqb5NhmLNCfxx9PuM7lDlYHEZXVTukuC1ZJdN6ei_gBfx2VLA |
| CitedBy_id | crossref_primary_10_1007_s41403_024_00512_4 crossref_primary_10_3390_math13010050 crossref_primary_10_1007_s11227_024_05948_7 crossref_primary_10_1016_j_tre_2025_104277 crossref_primary_10_1007_s13534_024_00436_6 crossref_primary_10_1016_j_engappai_2025_111435 |
| Cites_doi | 10.1109/72.991428 10.1049/itr2.12201 10.3389/fnins.2018.00291 10.3390/s20020500 10.1109/TITS.2019.2914910 10.1016/j.engappai.2020.103986 10.1016/j.biosystems.2008.05.008 10.1016/j.neucom.2021.08.005 10.1016/j.engappai.2021.104362 10.1016/j.neunet.2018.04.002 10.1093/cercor/bhl152 10.1088/1748-3190/ac290c 10.1016/j.neucom.2021.06.027 10.1109/TCNS.2021.3084064 10.1016/j.neunet.2019.05.019 10.3390/brainsci12070863 10.3389/fninf.2017.00034 10.1016/j.aap.2022.106703 10.1016/j.automatica.2022.110470 10.1109/TVT.2020.3019699 10.1109/TCYB.2018.2852772 10.1109/TNN.2004.832719 10.1155/2019/7261726 10.1109/TII.2020.3024946 10.1016/j.neunet.2014.01.006 10.1016/j.cie.2016.11.030 10.1109/TVT.2021.3098343 10.1109/TNNLS.2021.3111051 10.1155/2018/3061034 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.engappai.2023.107252 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Computer Science |
| EISSN | 1873-6769 |
| ExternalDocumentID | 10_1016_j_engappai_2023_107252 S0952197623014367 |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABMAC ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SBC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TN5 UHS WUQ ZMT ~G- 9DU AATTM AAXKI AAYWO AAYXX ABJNI ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD |
| ID | FETCH-LOGICAL-c312t-43f6559412f37afd87e1a38a95f5293d47f4cb88b207615d735b3457a4b833823 |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001094787800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0952-1976 |
| IngestDate | Sat Nov 29 07:04:08 EST 2025 Tue Nov 18 22:33:45 EST 2025 Fri Feb 23 02:35:43 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Brain-inspired control Cooperative operation Railway train control Spiking neural networks |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c312t-43f6559412f37afd87e1a38a95f5293d47f4cb88b207615d735b3457a4b833823 |
| ParticipantIDs | crossref_primary_10_1016_j_engappai_2023_107252 crossref_citationtrail_10_1016_j_engappai_2023_107252 elsevier_sciencedirect_doi_10_1016_j_engappai_2023_107252 |
| PublicationCentury | 2000 |
| PublicationDate | January 2024 2024-01-00 |
| PublicationDateYYYYMMDD | 2024-01-01 |
| PublicationDate_xml | – month: 01 year: 2024 text: January 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Engineering applications of artificial intelligence |
| PublicationYear | 2024 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Fu, Dessouky (b9) 2017; 103 Gao, Wei, Song, Zhang, Dong, Hu (b10) 2020; 96 Li, Wang, Shan, Lanzon, Petersen (b18) 2021; 8 Zhang, Song, Wang, Wang, Dong (b36) 2021; 70 Wang, Zhao, Lin, Cui, Luo, Zhu, Wang, Tang (b30) 2020; 17 Bohte, La Poutré, Kok (b2) 2002; 13 Xing, Yang, Zhang, Xu (b32) 2022 Felez, Kim, Borrelli (b7) 2019; 20 Duan, Schmid (b6) 2018 Xu, Peng, Zhang, Chen, Zhou, Yang, Gao, Huang (b33) 2019; 2019 Lin, Tian, Gui, Yang (b19) 2022; 144 Pu, Zhu, Zhang, Liu, Cai, Fu (b26) 2020; 69 Bing, Meschede, Chen, Knoll, Huang (b1) 2020; 121 Carrillo, Ros, Boucheny, Olivier (b3) 2008; 94 Izhikevich (b14) 2007; 17 Pan, Luo, Zhao, Zhang, Chen (b24) 2018; 2018 Zhang, Zhao, Zhang, Zhang (b37) 2022; 236 Clawson, Ferrari, Fuller, Wood (b4) 2016 Kasabov (b17) 2019 Moberget, Ivry (b23) 2019; 4 Kasabov (b16) 2014; 52 Xu, Tu, Xu, Wu (b34) 2022 Shalumov, Halaly, Tsur (b27) 2021; 16 Yamazaki, Vo-Ho, Bulsara, Le (b35) 2022; 12 Pérez, Cabrera, Castillo, Velasco (b25) 2018; 104 Van Albada, Rowley, Senk, Hopkins, Schmidt, Stokes, Lester, Diesmann, Furber (b29) 2018; 12 Xing, Li, Zhang, Xu (b31) 2021; 33 Fernández, Vargas, García, Carrillo, Aguilar (b8) 2021; 463 Liu, Lu, Luo, Yang (b20) 2021; 104 Dong, Zhu, Li, Lv, Gao, Zhang, Ning (b5) 2018; 48 Kaiser, Tieck, Hubschneider, Wolf, Weber, Hoff, Friedrich, Wojtasik, Roennau, Kohlhaas (b15) 2016 Hahne, Dahmen, Schuecker, Frommer, Bolten, Helias, Diesmann (b12) 2017; 11 Su, Liu, Zhu, Li, Tang, Lv (b28) 2022; 173 Lobov, Chernyshov, Krilova, Shamshin, Kazantsev (b21) 2020; 20 Izhikevich (b13) 2004; 15 Gerstner, Kistler (b11) 2002 Lu, Liu, Luo, Hua, Qiu, Huang (b22) 2021; 458 Kasabov (10.1016/j.engappai.2023.107252_b17) 2019 Van Albada (10.1016/j.engappai.2023.107252_b29) 2018; 12 Clawson (10.1016/j.engappai.2023.107252_b4) 2016 Izhikevich (10.1016/j.engappai.2023.107252_b14) 2007; 17 Pan (10.1016/j.engappai.2023.107252_b24) 2018; 2018 Su (10.1016/j.engappai.2023.107252_b28) 2022; 173 Carrillo (10.1016/j.engappai.2023.107252_b3) 2008; 94 Wang (10.1016/j.engappai.2023.107252_b30) 2020; 17 Gerstner (10.1016/j.engappai.2023.107252_b11) 2002 Xu (10.1016/j.engappai.2023.107252_b34) 2022 Izhikevich (10.1016/j.engappai.2023.107252_b13) 2004; 15 Zhang (10.1016/j.engappai.2023.107252_b36) 2021; 70 Yamazaki (10.1016/j.engappai.2023.107252_b35) 2022; 12 Li (10.1016/j.engappai.2023.107252_b18) 2021; 8 Moberget (10.1016/j.engappai.2023.107252_b23) 2019; 4 Pérez (10.1016/j.engappai.2023.107252_b25) 2018; 104 Felez (10.1016/j.engappai.2023.107252_b7) 2019; 20 Kasabov (10.1016/j.engappai.2023.107252_b16) 2014; 52 Lobov (10.1016/j.engappai.2023.107252_b21) 2020; 20 Xing (10.1016/j.engappai.2023.107252_b31) 2021; 33 Bohte (10.1016/j.engappai.2023.107252_b2) 2002; 13 Fu (10.1016/j.engappai.2023.107252_b9) 2017; 103 Bing (10.1016/j.engappai.2023.107252_b1) 2020; 121 Hahne (10.1016/j.engappai.2023.107252_b12) 2017; 11 Gao (10.1016/j.engappai.2023.107252_b10) 2020; 96 Lin (10.1016/j.engappai.2023.107252_b19) 2022; 144 Lu (10.1016/j.engappai.2023.107252_b22) 2021; 458 Shalumov (10.1016/j.engappai.2023.107252_b27) 2021; 16 Kaiser (10.1016/j.engappai.2023.107252_b15) 2016 Liu (10.1016/j.engappai.2023.107252_b20) 2021; 104 Zhang (10.1016/j.engappai.2023.107252_b37) 2022; 236 Xu (10.1016/j.engappai.2023.107252_b33) 2019; 2019 Dong (10.1016/j.engappai.2023.107252_b5) 2018; 48 Fernández (10.1016/j.engappai.2023.107252_b8) 2021; 463 Duan (10.1016/j.engappai.2023.107252_b6) 2018 Pu (10.1016/j.engappai.2023.107252_b26) 2020; 69 Xing (10.1016/j.engappai.2023.107252_b32) 2022 |
| References_xml | – volume: 33 start-page: 2094 year: 2021 end-page: 2105 ident: b31 article-title: A brain-inspired approach for collision-free movement planning in the small operational space publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 2019 ident: b17 article-title: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence – volume: 15 start-page: 1063 year: 2004 end-page: 1070 ident: b13 article-title: Which model to use for cortical spiking neurons? publication-title: IEEE Trans. Neural Netw. – start-page: 3381 year: 2016 end-page: 3388 ident: b4 article-title: Spiking Neural Network (SNN) control of a flapping insect-scale robot publication-title: 2016 IEEE 55th Conference on Decision and Control – volume: 12 start-page: 291 year: 2018 ident: b29 article-title: Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model publication-title: Front. Neurosci. – volume: 2019 year: 2019 ident: b33 article-title: Adaptive model predictive control for cruise control of high-speed trains with time-varying parameters publication-title: J. Adv. Transp. – start-page: 127 year: 2016 end-page: 134 ident: b15 article-title: Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks publication-title: 2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots – volume: 13 start-page: 426 year: 2002 end-page: 435 ident: b2 article-title: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks publication-title: IEEE Trans. Neural Netw. – volume: 8 start-page: 1743 year: 2021 end-page: 1753 ident: b18 article-title: Robust cooperative control of networked train platoons: A negative-imaginary systems’ perspective publication-title: IEEE Trans. Control Netw. Syst. – volume: 458 start-page: 308 year: 2021 end-page: 318 ident: b22 article-title: An autonomous learning mobile robot using biological reward modulate STDP publication-title: Neurocomputing – volume: 144 year: 2022 ident: b19 article-title: Cooperative control for multiple train systems: Self-adjusting zones, collision avoidance and constraints publication-title: Automatica – volume: 12 start-page: 863 year: 2022 ident: b35 article-title: Spiking neural networks and their applications: A review publication-title: Brain Sci. – volume: 20 start-page: 500 year: 2020 ident: b21 article-title: Competitive learning in a spiking neural network: Towards an intelligent pattern classifier publication-title: Sensors – volume: 121 start-page: 21 year: 2020 end-page: 36 ident: b1 article-title: Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle publication-title: Neural Netw. – year: 2022 ident: b34 article-title: Intelligent train operation based on deep learning from excellent driver manipulation patterns publication-title: IET Intell. Transp. Syst. – volume: 2018 year: 2018 ident: b24 article-title: A new calibration method for the real-time calculation of dynamic safety following distance under railway moving block system publication-title: Math. Probl. Eng. – volume: 70 start-page: 8545 year: 2021 end-page: 8555 ident: b36 article-title: Cooperative multi-scenario departure control for virtual coupling trains: A fixed-time approach publication-title: IEEE Trans. Veh. Technol. – start-page: 1 year: 2018 end-page: 5 ident: b6 article-title: Optimised headway distance moving block with capacity analysis publication-title: 2018 International Conference on Intelligent Rail Transportation – volume: 103 start-page: 271 year: 2017 end-page: 281 ident: b9 article-title: Models and algorithms for dynamic headway control publication-title: Comput. Ind. Eng. – volume: 173 year: 2022 ident: b28 article-title: A cooperative collision-avoidance control methodology for virtual coupling trains publication-title: Accid. Anal. Prev. – year: 2002 ident: b11 article-title: Spiking Neuron Models: Single Neurons, Populations, Plasticity – volume: 104 year: 2021 ident: b20 article-title: Spiking neural network-based multi-task autonomous learning for mobile robots publication-title: Eng. Appl. Artif. Intell. – volume: 96 year: 2020 ident: b10 article-title: Fuzzy adaptive automatic train operation control with protection constraints: A residual nonlinearity approximation-based approach publication-title: Eng. Appl. Artif. Intell. – volume: 17 start-page: 4935 year: 2020 end-page: 4945 ident: b30 article-title: A reinforcement learning empowered cooperative control approach for iiot-based virtually coupled train sets publication-title: IEEE Trans. Ind. Inform. – year: 2022 ident: b32 article-title: A brain-inspired approach for probabilistic estimation and efficient planning in precision physical interaction publication-title: IEEE Trans. Cybern. – volume: 236 start-page: 975 year: 2022 end-page: 988 ident: b37 article-title: Intelligent train control for cooperative train formation: A deep reinforcement learning approach publication-title: Proc. Inst. Mech. Eng. I – volume: 16 year: 2021 ident: b27 article-title: Lidar-driven spiking neural network for collision avoidance in autonomous driving publication-title: Bioinspiration Biomim. – volume: 463 start-page: 237 year: 2021 end-page: 250 ident: b8 article-title: A biological-like controller using improved spiking neural networks publication-title: Neurocomputing – volume: 17 start-page: 2443 year: 2007 end-page: 2452 ident: b14 article-title: Solving the distal reward problem through linkage of STDP and dopamine signaling publication-title: Cerebral Cortex – volume: 4 start-page: 820 year: 2019 end-page: 831 ident: b23 article-title: Prediction, psychosis, and the cerebellum publication-title: Biol. Psychiatry: Cogn. Neurosci. Neuroimag. – volume: 104 start-page: 15 year: 2018 end-page: 25 ident: b25 article-title: Bio-inspired spiking neural network for nonlinear systems control publication-title: Neural Netw. – volume: 48 start-page: 3381 year: 2018 end-page: 3389 ident: b5 article-title: Parallel intelligent systems for integrated high-speed railway operation control and dynamic scheduling publication-title: IEEE Trans. Cybern. – volume: 94 start-page: 18 year: 2008 end-page: 27 ident: b3 article-title: A real-time spiking cerebellum model for learning robot control publication-title: Biosystems – volume: 69 start-page: 10656 year: 2020 end-page: 10667 ident: b26 article-title: Speed profile tracking by an adaptive controller for subway train based on neural network and pid algorithm publication-title: IEEE Trans. Veh. Technol. – volume: 52 start-page: 62 year: 2014 end-page: 76 ident: b16 article-title: NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data publication-title: Neural Netw. – volume: 20 start-page: 2728 year: 2019 end-page: 2739 ident: b7 article-title: A model predictive control approach for virtual coupling in railways publication-title: IEEE Trans. Intell. Transp. Syst. – volume: 11 start-page: 34 year: 2017 ident: b12 article-title: Integration of continuous-time dynamics in a spiking neural network simulator publication-title: Front. Neuroinf. – volume: 13 start-page: 426 issue: 2 year: 2002 ident: 10.1016/j.engappai.2023.107252_b2 article-title: Unsupervised clustering with spiking neurons by sparse temporal coding and multilayer RBF networks publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.991428 – year: 2022 ident: 10.1016/j.engappai.2023.107252_b34 article-title: Intelligent train operation based on deep learning from excellent driver manipulation patterns publication-title: IET Intell. Transp. Syst. doi: 10.1049/itr2.12201 – volume: 12 start-page: 291 year: 2018 ident: 10.1016/j.engappai.2023.107252_b29 article-title: Performance comparison of the digital neuromorphic hardware SpiNNaker and the neural network simulation software NEST for a full-scale cortical microcircuit model publication-title: Front. Neurosci. doi: 10.3389/fnins.2018.00291 – volume: 20 start-page: 500 issue: 2 year: 2020 ident: 10.1016/j.engappai.2023.107252_b21 article-title: Competitive learning in a spiking neural network: Towards an intelligent pattern classifier publication-title: Sensors doi: 10.3390/s20020500 – year: 2022 ident: 10.1016/j.engappai.2023.107252_b32 article-title: A brain-inspired approach for probabilistic estimation and efficient planning in precision physical interaction publication-title: IEEE Trans. Cybern. – year: 2019 ident: 10.1016/j.engappai.2023.107252_b17 – volume: 4 start-page: 820 issue: 9 year: 2019 ident: 10.1016/j.engappai.2023.107252_b23 article-title: Prediction, psychosis, and the cerebellum publication-title: Biol. Psychiatry: Cogn. Neurosci. Neuroimag. – volume: 20 start-page: 2728 issue: 7 year: 2019 ident: 10.1016/j.engappai.2023.107252_b7 article-title: A model predictive control approach for virtual coupling in railways publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2019.2914910 – volume: 96 year: 2020 ident: 10.1016/j.engappai.2023.107252_b10 article-title: Fuzzy adaptive automatic train operation control with protection constraints: A residual nonlinearity approximation-based approach publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2020.103986 – volume: 94 start-page: 18 issue: 1–2 year: 2008 ident: 10.1016/j.engappai.2023.107252_b3 article-title: A real-time spiking cerebellum model for learning robot control publication-title: Biosystems doi: 10.1016/j.biosystems.2008.05.008 – volume: 463 start-page: 237 year: 2021 ident: 10.1016/j.engappai.2023.107252_b8 article-title: A biological-like controller using improved spiking neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.08.005 – start-page: 3381 year: 2016 ident: 10.1016/j.engappai.2023.107252_b4 article-title: Spiking Neural Network (SNN) control of a flapping insect-scale robot – start-page: 127 year: 2016 ident: 10.1016/j.engappai.2023.107252_b15 article-title: Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks – volume: 104 year: 2021 ident: 10.1016/j.engappai.2023.107252_b20 article-title: Spiking neural network-based multi-task autonomous learning for mobile robots publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2021.104362 – volume: 104 start-page: 15 year: 2018 ident: 10.1016/j.engappai.2023.107252_b25 article-title: Bio-inspired spiking neural network for nonlinear systems control publication-title: Neural Netw. doi: 10.1016/j.neunet.2018.04.002 – volume: 17 start-page: 2443 issue: 10 year: 2007 ident: 10.1016/j.engappai.2023.107252_b14 article-title: Solving the distal reward problem through linkage of STDP and dopamine signaling publication-title: Cerebral Cortex doi: 10.1093/cercor/bhl152 – volume: 16 issue: 6 year: 2021 ident: 10.1016/j.engappai.2023.107252_b27 article-title: Lidar-driven spiking neural network for collision avoidance in autonomous driving publication-title: Bioinspiration Biomim. doi: 10.1088/1748-3190/ac290c – volume: 458 start-page: 308 year: 2021 ident: 10.1016/j.engappai.2023.107252_b22 article-title: An autonomous learning mobile robot using biological reward modulate STDP publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.06.027 – volume: 8 start-page: 1743 issue: 4 year: 2021 ident: 10.1016/j.engappai.2023.107252_b18 article-title: Robust cooperative control of networked train platoons: A negative-imaginary systems’ perspective publication-title: IEEE Trans. Control Netw. Syst. doi: 10.1109/TCNS.2021.3084064 – volume: 121 start-page: 21 year: 2020 ident: 10.1016/j.engappai.2023.107252_b1 article-title: Indirect and direct training of spiking neural networks for end-to-end control of a lane-keeping vehicle publication-title: Neural Netw. doi: 10.1016/j.neunet.2019.05.019 – volume: 236 start-page: 975 issue: 5 year: 2022 ident: 10.1016/j.engappai.2023.107252_b37 article-title: Intelligent train control for cooperative train formation: A deep reinforcement learning approach publication-title: Proc. Inst. Mech. Eng. I – volume: 12 start-page: 863 issue: 7 year: 2022 ident: 10.1016/j.engappai.2023.107252_b35 article-title: Spiking neural networks and their applications: A review publication-title: Brain Sci. doi: 10.3390/brainsci12070863 – volume: 11 start-page: 34 year: 2017 ident: 10.1016/j.engappai.2023.107252_b12 article-title: Integration of continuous-time dynamics in a spiking neural network simulator publication-title: Front. Neuroinf. doi: 10.3389/fninf.2017.00034 – volume: 173 year: 2022 ident: 10.1016/j.engappai.2023.107252_b28 article-title: A cooperative collision-avoidance control methodology for virtual coupling trains publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2022.106703 – volume: 144 year: 2022 ident: 10.1016/j.engappai.2023.107252_b19 article-title: Cooperative control for multiple train systems: Self-adjusting zones, collision avoidance and constraints publication-title: Automatica doi: 10.1016/j.automatica.2022.110470 – year: 2002 ident: 10.1016/j.engappai.2023.107252_b11 – volume: 69 start-page: 10656 issue: 10 year: 2020 ident: 10.1016/j.engappai.2023.107252_b26 article-title: Speed profile tracking by an adaptive controller for subway train based on neural network and pid algorithm publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2020.3019699 – volume: 48 start-page: 3381 issue: 12 year: 2018 ident: 10.1016/j.engappai.2023.107252_b5 article-title: Parallel intelligent systems for integrated high-speed railway operation control and dynamic scheduling publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2018.2852772 – volume: 15 start-page: 1063 issue: 5 year: 2004 ident: 10.1016/j.engappai.2023.107252_b13 article-title: Which model to use for cortical spiking neurons? publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2004.832719 – volume: 2019 year: 2019 ident: 10.1016/j.engappai.2023.107252_b33 article-title: Adaptive model predictive control for cruise control of high-speed trains with time-varying parameters publication-title: J. Adv. Transp. doi: 10.1155/2019/7261726 – volume: 17 start-page: 4935 issue: 7 year: 2020 ident: 10.1016/j.engappai.2023.107252_b30 article-title: A reinforcement learning empowered cooperative control approach for iiot-based virtually coupled train sets publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2020.3024946 – volume: 52 start-page: 62 year: 2014 ident: 10.1016/j.engappai.2023.107252_b16 article-title: NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data publication-title: Neural Netw. doi: 10.1016/j.neunet.2014.01.006 – start-page: 1 year: 2018 ident: 10.1016/j.engappai.2023.107252_b6 article-title: Optimised headway distance moving block with capacity analysis – volume: 103 start-page: 271 year: 2017 ident: 10.1016/j.engappai.2023.107252_b9 article-title: Models and algorithms for dynamic headway control publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2016.11.030 – volume: 70 start-page: 8545 issue: 9 year: 2021 ident: 10.1016/j.engappai.2023.107252_b36 article-title: Cooperative multi-scenario departure control for virtual coupling trains: A fixed-time approach publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2021.3098343 – volume: 33 start-page: 2094 issue: 5 year: 2021 ident: 10.1016/j.engappai.2023.107252_b31 article-title: A brain-inspired approach for collision-free movement planning in the small operational space publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3111051 – volume: 2018 year: 2018 ident: 10.1016/j.engappai.2023.107252_b24 article-title: A new calibration method for the real-time calculation of dynamic safety following distance under railway moving block system publication-title: Math. Probl. Eng. doi: 10.1155/2018/3061034 |
| SSID | ssj0003846 |
| Score | 2.413543 |
| Snippet | The ongoing challenge of addressing critical issues related to intelligent cooperative control and active protection persists due to the absence of a... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 107252 |
| SubjectTerms | Brain-inspired control Cooperative operation Railway train control Spiking neural networks |
| Title | A novel brain-inspired approach based on spiking neural network for cooperative control and protection of multiple trains |
| URI | https://dx.doi.org/10.1016/j.engappai.2023.107252 |
| Volume | 127 |
| WOSCitedRecordID | wos001094787800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6769 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003846 issn: 0952-1976 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELbKLgcuvBG7POQDtyilSezaPlZo0cJhhcQiVVwiJ3FQVpVTtd1S_tj-PsavJGVXLHtAlaJoUjtO52tmPB5_g9A7JqQop5VJK09lTEpZxoKyIia8plSSpICPLTbBzs74fC6-jEZXYS_MdsG05rudWP5XVYMMlG22zt5B3V2nIIBzUDocQe1w_CfFzyLdbtUiKkzxh7jRZikdvMpAHh4Zu1WZNQK4YOLkkaG0BEVplxBu8w7Ltl0qzwkektkdo4BldfBOZpeMaOtMrPeC_D3NYTRcI7dpByubn2SrhQwIQa-FsL83u8seu1997vCpbGrlza1dCPBiuPxTNX0gwm2paH5I_10f2UjJILIRQpRpnAhXH6Z7WzsqAf--hclr6hhwr5kCF5W4GMOQ4DllMzaF4sd9g33u7T9sYpepGJLgLvLQT276yV0_99BhyqgAg3A4-3Qy_9z5ABl3W8TCEwz2pt88opvdooGrc_4YPfRzFDxz2HqCRko_RY_8fAV7a7AGUSgJEmTP0K8ZtujD--jDAX3Yog-3Gnv0YYc-7NGHAX14gD7s0YcBfbhHH25rHNCHHfqeo28fT84_nMa-ukdcZkm6iUlWT2E6S5K0zpisK85UIjMuBa0p-KAVYTUpC86L1ITaaMUyWmSEMkkKnpnV6xfoQLdavUR4ypJEEMYV5ZKQYlIYv2zCKzhRKpWTI0TDj5uXnvrejG2R_129R-h9127pyF9ubSGC7nLvwjrXNAdY3tL2-M53e4Ue9P-b1-hgs7pUb9D9crtp1qu3HpO_Ae-cxqU |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+brain-inspired+approach+based+on+spiking+neural+network+for+cooperative+control+and+protection+of+multiple+trains&rft.jtitle=Engineering+applications+of+artificial+intelligence&rft.au=Zhang%2C+Zixuan&rft.au=Song%2C+Haifeng&rft.au=Wang%2C+Hongwei&rft.au=Tan%2C+Ligang&rft.date=2024-01-01&rft.issn=0952-1976&rft.volume=127&rft.spage=107252&rft_id=info:doi/10.1016%2Fj.engappai.2023.107252&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_engappai_2023_107252 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0952-1976&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0952-1976&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0952-1976&client=summon |