High order Hopfield network with self-feedback to solve crossbar switch problem

Yuxin Ding; Li Dong; Bin Zhao; Zhanjun Lu

doi:10.1007/978-3-642-24965-5_35

High order Hopfield network with self-feedback to solve crossbar switch problem

Yuxin Ding^*
, Li Dong
, Bin Zhao
, Zhanjun Lu

^*Corresponding author for this work

Harbin Institute of Technology Shenzhen

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

1 Scopus citations

Abstract

High order network has a higher store capacity and a faster convergence speed compared with the first order network. To improve the convergence speed of the energy function, in this paper a new kind of high order discrete neural network with self-feedback is proposed to solve crossbar switch problem. The construction method of the high order energy function for this problem is presented and the neural computing method is given. We also discuss the strategies for the network to escape from local minima. Compared with the first order Hopfield network, experimental results show the high order network with self-feedback has a quick convergence speed, its performance is better than the first order Hopfield network.

Original language	English
Title of host publication	Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings
Pages	315-322
Number of pages	8
Edition	PART 3
DOIs	https://doi.org/10.1007/978-3-642-24965-5_35
State	Published - 2011
Externally published	Yes
Event	18th International Conference on Neural Information Processing, ICONIP 2011 - Shanghai, China Duration: 13 Nov 2011 → 17 Nov 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	7064 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Neural Information Processing, ICONIP 2011
Country/Territory	China
City	Shanghai
Period	13/11/11 → 17/11/11

Keywords

Hopfield network
crossbar switch problem
high order network

Access to Document

10.1007/978-3-642-24965-5_35

Cite this

Ding, Y., Dong, L., Zhao, B., & Lu, Z. (2011). High order Hopfield network with self-feedback to solve crossbar switch problem. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings (PART 3 ed., pp. 315-322). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-24965-5_35

Ding, Yuxin ; Dong, Li ; Zhao, Bin et al. / High order Hopfield network with self-feedback to solve crossbar switch problem. Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 3. ed. 2011. pp. 315-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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abstract = "High order network has a higher store capacity and a faster convergence speed compared with the first order network. To improve the convergence speed of the energy function, in this paper a new kind of high order discrete neural network with self-feedback is proposed to solve crossbar switch problem. The construction method of the high order energy function for this problem is presented and the neural computing method is given. We also discuss the strategies for the network to escape from local minima. Compared with the first order Hopfield network, experimental results show the high order network with self-feedback has a quick convergence speed, its performance is better than the first order Hopfield network.",

keywords = "Hopfield network, crossbar switch problem, high order network",

author = "Yuxin Ding and Li Dong and Bin Zhao and Zhanjun Lu",

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booktitle = "Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings",

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Ding, Y, Dong, L, Zhao, B & Lu, Z 2011, High order Hopfield network with self-feedback to solve crossbar switch problem. in Neural Information Processing - 18th International Conference, ICONIP 2011, 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. 7064 LNCS, pp. 315-322, 18th International Conference on Neural Information Processing, ICONIP 2011, Shanghai, China, 13/11/11. https://doi.org/10.1007/978-3-642-24965-5_35

High order Hopfield network with self-feedback to solve crossbar switch problem. / Ding, Yuxin; Dong, Li; Zhao, Bin et al.
Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 3. ed. 2011. p. 315-322 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7064 LNCS, No. PART 3).

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

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AU - Dong, Li

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N2 - High order network has a higher store capacity and a faster convergence speed compared with the first order network. To improve the convergence speed of the energy function, in this paper a new kind of high order discrete neural network with self-feedback is proposed to solve crossbar switch problem. The construction method of the high order energy function for this problem is presented and the neural computing method is given. We also discuss the strategies for the network to escape from local minima. Compared with the first order Hopfield network, experimental results show the high order network with self-feedback has a quick convergence speed, its performance is better than the first order Hopfield network.

AB - High order network has a higher store capacity and a faster convergence speed compared with the first order network. To improve the convergence speed of the energy function, in this paper a new kind of high order discrete neural network with self-feedback is proposed to solve crossbar switch problem. The construction method of the high order energy function for this problem is presented and the neural computing method is given. We also discuss the strategies for the network to escape from local minima. Compared with the first order Hopfield network, experimental results show the high order network with self-feedback has a quick convergence speed, its performance is better than the first order Hopfield network.

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Ding Y, Dong L, Zhao B, Lu Z. High order Hopfield network with self-feedback to solve crossbar switch problem. In Neural Information Processing - 18th International Conference, ICONIP 2011, Proceedings. PART 3 ed. 2011. p. 315-322. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-642-24965-5_35

High order Hopfield network with self-feedback to solve crossbar switch problem

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