Liked-BAM neural network for image recognition

D. G. Shen; F. H. Qi

Liked-BAM neural network for image recognition

D. G. Shen^*
, F. H. Qi

^*Corresponding author for this work

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

Abstract

A neural network model and its application to image recognition are proposed in this paper. This model consists of Mapping Network (MN) and Liked Bidirectional Associative Memory (LBAM). Invariant mapping is used in MN in order to decrease the number of dimensions of image samples and not to change the distance between them. LBAM's structure is simple and its convergence speed is fast. Several computer simulations given to prove that the model is capable of recognizing noise - added targets and targets cut off little part.

Original language	English
Title of host publication	Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)
Editors	Anon
Publisher	Publ by IEEE
Pages	966-968
Number of pages	3
ISBN (Print)	0780312333
Publication status	Published - 1993
Event	Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5) - Beijing, China Duration: 1993 Oct 19 → 1993 Oct 21

Publication series

Name	Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)

Other

Other	Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5)
City	Beijing, China
Period	93/10/19 → 93/10/21

ASJC Scopus subject areas

General Engineering

Cite this

Shen, DG & Qi, FH 1993, Liked-BAM neural network for image recognition. in Anon (ed.), Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93). Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93), Publ by IEEE, pp. 966-968, Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5), Beijing, China, 93/10/19.

@inproceedings{2719234fc71d4def9602164e206fe64e,

title = "Liked-BAM neural network for image recognition",

abstract = "A neural network model and its application to image recognition are proposed in this paper. This model consists of Mapping Network (MN) and Liked Bidirectional Associative Memory (LBAM). Invariant mapping is used in MN in order to decrease the number of dimensions of image samples and not to change the distance between them. LBAM's structure is simple and its convergence speed is fast. Several computer simulations given to prove that the model is capable of recognizing noise - added targets and targets cut off little part.",

author = "Shen, \{D. G.\} and Qi, \{F. H.\}",

year = "1993",

language = "English",

isbn = "0780312333",

series = "Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)",

publisher = "Publ by IEEE",

pages = "966--968",

editor = "Anon",

booktitle = "Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)",

note = "Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5) ; Conference date: 19-10-1993 Through 21-10-1993",

}

TY - GEN

T1 - Liked-BAM neural network for image recognition

AU - Shen, D. G.

AU - Qi, F. H.

PY - 1993

Y1 - 1993

N2 - A neural network model and its application to image recognition are proposed in this paper. This model consists of Mapping Network (MN) and Liked Bidirectional Associative Memory (LBAM). Invariant mapping is used in MN in order to decrease the number of dimensions of image samples and not to change the distance between them. LBAM's structure is simple and its convergence speed is fast. Several computer simulations given to prove that the model is capable of recognizing noise - added targets and targets cut off little part.

AB - A neural network model and its application to image recognition are proposed in this paper. This model consists of Mapping Network (MN) and Liked Bidirectional Associative Memory (LBAM). Invariant mapping is used in MN in order to decrease the number of dimensions of image samples and not to change the distance between them. LBAM's structure is simple and its convergence speed is fast. Several computer simulations given to prove that the model is capable of recognizing noise - added targets and targets cut off little part.

UR - https://www.scopus.com/pages/publications/0027757556

M3 - Conference contribution

AN - SCOPUS:0027757556

SN - 0780312333

T3 - Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)

SP - 966

EP - 968

BT - Proc 1993 IEEE Reg 10 Conf Comput Commun Control Power Eng (TENCON '93)

A2 - Anon, null

PB - Publ by IEEE

T2 - Proceedings of the 1993 IEEE Region 10 Conference on Computer, Communication, Control aand Power Engineering. Part 3 (of 5)

Y2 - 19 October 1993 through 21 October 1993

ER -

Liked-BAM neural network for image recognition

Abstract

Publication series

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this