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Author:

Li, Rong (Li, Rong.) | Qiao, Junfei (Qiao, Junfei.) (Scholars:乔俊飞) | Li, Wenjing (Li, Wenjing.)

Indexed by:

CPCI-S

Abstract:

In view of the problem that Hopfield neural network is easy to fall into local minimum, this paper proposes a modified Hopfield neural network to overcome this problem. By adjusting the connection weights which is governed by the objective function, the energy function decreases until to the global minimum. The modified Hopfield neural network is used for solving TSP problem, and the experimental results show that the network will escape the local minimum. The validity and feasibility of the modified Hopfield neural network is proved.

Keyword:

Author Community:

  • [ 1 ] [Li, Rong]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Qiao, Junfei]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Wenjing]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Rong]Beijing Vocat Coll Agr, Dept Informat Technol, Beijing, Peoples R China

Reprint Author's Address:

  • [Li, Rong]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China;;[Li, Rong]Beijing Vocat Coll Agr, Dept Informat Technol, Beijing, Peoples R China

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Source :

PROCEEDINGS OF THE 2016 12TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA)

Year: 2016

Page: 1775-1780

Language: English

Cited Count:

WoS CC Cited Count: 6

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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