Minimal resource allocation on CAN bus using radial basis function networks

Yan Hao Wei; Mu Song Chen; Chuan Ku Lin; Chi Pan Hwang

doi:10.1007/978-3-540-72395-0_122

Minimal resource allocation on CAN bus using radial basis function networks

Yan Hao Wei, Mu Song Chen, Chuan Ku Lin, Chi Pan Hwang

Department of Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Optimal message scheduling is one of the key issues in the field of controller area network (CAN) bus system. There are numerous approaches related to this issue. Most of them are essentially based on priority-based strategies. In 1, we utilized Radial Basic Function (RBF) network 2 as a message scheduling controller to dynamically schedule messages. Furthermore, an online Backward-Through-Time (BTT) algorithm is presented for parameter optimization under a priori fixed network structure. Intuitively, an inappropriate RBF network structure leads to performance degradation. In the worst case, the CAN system diverges. In this paper, we extend our previous works by including Minimal Resource Allocation (MRA) algorithm for structure determination, In this way, both problems of parameter optimization and structure determination can be resolved at the same time. Simulation results demonstrated that the proposed BTT with MRA methods outperform our previous results in terms of convergence time, stability, and the number of required hidden neurons (or radial basis functions).

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
Publisher	Springer Verlag
Pages	998-1005
Number of pages	8
Edition	PART 3
ISBN (Print)	9783540723943
DOIs	https://doi.org/10.1007/978-3-540-72395-0_122
Publication status	Published - 2007
Event	4th International Symposium on Neural Networks, ISNN 2007 - Nanjing, China Duration: 2007 Jun 3 → 2007 Jun 7

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	4493 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	4th International Symposium on Neural Networks, ISNN 2007
Country	China
City	Nanjing
Period	07-06-03 → 07-06-07

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-72395-0_122

Fingerprint Dive into the research topics of 'Minimal resource allocation on CAN bus using radial basis function networks'. Together they form a unique fingerprint.

Cite this

Wei, Y. H., Chen, M. S., Lin, C. K., & Hwang, C. P. (2007). Minimal resource allocation on CAN bus using radial basis function networks. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings (PART 3 ed., pp. 998-1005). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4493 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_122

Wei, Yan Hao ; Chen, Mu Song ; Lin, Chuan Ku ; Hwang, Chi Pan. / Minimal resource allocation on CAN bus using radial basis function networks. Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 3. ed. Springer Verlag, 2007. pp. 998-1005 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

@inproceedings{66e667a638544bfba8767439980a7d63,

title = "Minimal resource allocation on CAN bus using radial basis function networks",

abstract = "Optimal message scheduling is one of the key issues in the field of controller area network (CAN) bus system. There are numerous approaches related to this issue. Most of them are essentially based on priority-based strategies. In 1, we utilized Radial Basic Function (RBF) network 2 as a message scheduling controller to dynamically schedule messages. Furthermore, an online Backward-Through-Time (BTT) algorithm is presented for parameter optimization under a priori fixed network structure. Intuitively, an inappropriate RBF network structure leads to performance degradation. In the worst case, the CAN system diverges. In this paper, we extend our previous works by including Minimal Resource Allocation (MRA) algorithm for structure determination, In this way, both problems of parameter optimization and structure determination can be resolved at the same time. Simulation results demonstrated that the proposed BTT with MRA methods outperform our previous results in terms of convergence time, stability, and the number of required hidden neurons (or radial basis functions).",

author = "Wei, {Yan Hao} and Chen, {Mu Song} and Lin, {Chuan Ku} and Hwang, {Chi Pan}",

year = "2007",

doi = "10.1007/978-3-540-72395-0_122",

language = "English",

isbn = "9783540723943",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

number = "PART 3",

pages = "998--1005",

booktitle = "Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings",

address = "Germany",

edition = "PART 3",

note = "4th International Symposium on Neural Networks, ISNN 2007 ; Conference date: 03-06-2007 Through 07-06-2007",

}

Wei, YH, Chen, MS, Lin, CK & Hwang, CP 2007, Minimal resource allocation on CAN bus using radial basis function networks. in Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4493 LNCS, Springer Verlag, pp. 998-1005, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, 07-06-03. https://doi.org/10.1007/978-3-540-72395-0_122

Minimal resource allocation on CAN bus using radial basis function networks. / Wei, Yan Hao; Chen, Mu Song; Lin, Chuan Ku; Hwang, Chi Pan.

Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 3. ed. Springer Verlag, 2007. p. 998-1005 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4493 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Minimal resource allocation on CAN bus using radial basis function networks

AU - Wei, Yan Hao

AU - Chen, Mu Song

AU - Lin, Chuan Ku

AU - Hwang, Chi Pan

PY - 2007

Y1 - 2007

N2 - Optimal message scheduling is one of the key issues in the field of controller area network (CAN) bus system. There are numerous approaches related to this issue. Most of them are essentially based on priority-based strategies. In 1, we utilized Radial Basic Function (RBF) network 2 as a message scheduling controller to dynamically schedule messages. Furthermore, an online Backward-Through-Time (BTT) algorithm is presented for parameter optimization under a priori fixed network structure. Intuitively, an inappropriate RBF network structure leads to performance degradation. In the worst case, the CAN system diverges. In this paper, we extend our previous works by including Minimal Resource Allocation (MRA) algorithm for structure determination, In this way, both problems of parameter optimization and structure determination can be resolved at the same time. Simulation results demonstrated that the proposed BTT with MRA methods outperform our previous results in terms of convergence time, stability, and the number of required hidden neurons (or radial basis functions).

AB - Optimal message scheduling is one of the key issues in the field of controller area network (CAN) bus system. There are numerous approaches related to this issue. Most of them are essentially based on priority-based strategies. In 1, we utilized Radial Basic Function (RBF) network 2 as a message scheduling controller to dynamically schedule messages. Furthermore, an online Backward-Through-Time (BTT) algorithm is presented for parameter optimization under a priori fixed network structure. Intuitively, an inappropriate RBF network structure leads to performance degradation. In the worst case, the CAN system diverges. In this paper, we extend our previous works by including Minimal Resource Allocation (MRA) algorithm for structure determination, In this way, both problems of parameter optimization and structure determination can be resolved at the same time. Simulation results demonstrated that the proposed BTT with MRA methods outperform our previous results in terms of convergence time, stability, and the number of required hidden neurons (or radial basis functions).

UR - http://www.scopus.com/inward/record.url?scp=38049174247&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=38049174247&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-72395-0_122

DO - 10.1007/978-3-540-72395-0_122

M3 - Conference contribution

AN - SCOPUS:38049174247

SN - 9783540723943

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 998

EP - 1005

BT - Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings

PB - Springer Verlag

T2 - 4th International Symposium on Neural Networks, ISNN 2007

Y2 - 3 June 2007 through 7 June 2007

ER -

Wei YH, Chen MS, Lin CK, Hwang CP. Minimal resource allocation on CAN bus using radial basis function networks. In Advances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings. PART 3 ed. Springer Verlag. 2007. p. 998-1005. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-540-72395-0_122