Efficient pattern recalling using a non iterative hopfield associative memory

José Juan Carbajal Hernández; Luis Pastor Sánchez Fernández

doi:10.1007/978-3-642-25330-0_46

Efficient pattern recalling using a non iterative hopfield associative memory

José Juan Carbajal Hernández, Luis Pastor Sánchez Fernández

Centro de Investigación en Computación (CIC)

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

Abstract

Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field.

Original language	English
Title of host publication	Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings
Pages	522-529
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-25330-0_46
State	Published - 2011
Event	10th Mexican International Conference on Artificial Intelligence, MICAI 2011 - Puebla, Mexico Duration: 26 Nov 2011 → 4 Dec 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7095 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th Mexican International Conference on Artificial Intelligence, MICAI 2011
Country/Territory	Mexico
City	Puebla
Period	26/11/11 → 4/12/11

Keywords

Associative memory
Hopfield
neural networks
non iterative

Access to Document

10.1007/978-3-642-25330-0_46

Cite this

Carbajal Hernández, J. J., & Sánchez Fernández, L. P. (2011). Efficient pattern recalling using a non iterative hopfield associative memory. In Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings (PART 2 ed., pp. 522-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7095 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-25330-0_46

Carbajal Hernández, José Juan ; Sánchez Fernández, Luis Pastor. / Efficient pattern recalling using a non iterative hopfield associative memory. Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2. ed. 2011. pp. 522-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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abstract = "Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field.",

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booktitle = "Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings",

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Carbajal Hernández, JJ & Sánchez Fernández, LP 2011, Efficient pattern recalling using a non iterative hopfield associative memory. in Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7095 LNAI, pp. 522-529, 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Puebla, Mexico, 26/11/11. https://doi.org/10.1007/978-3-642-25330-0_46

Efficient pattern recalling using a non iterative hopfield associative memory. / Carbajal Hernández, José Juan ; Sánchez Fernández, Luis Pastor.
Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2. ed. 2011. p. 522-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7095 LNAI, No. PART 2).

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

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N2 - Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field.

AB - Actually associative memories have demonstrated to be useful in pattern processing field. Hopfield model is an autoassociative memory that has problems in the recalling phase; one of them is the time of convergence or non convergence in certain cases with patterns bad recovered. In this paper, a new algorithm for the Hopfield associative memory eliminates iteration processes reducing time computing and uncertainty on pattern recalling. This algorithm is implemented using a corrective vector which is computed using the Hopfield memory. The corrective vector adjusts misclassifications in output recalled patterns. Results show a good performance of the proposed algorithm, providing an alternative tool for the pattern recognition field.

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Carbajal Hernández JJ , Sánchez Fernández LP. Efficient pattern recalling using a non iterative hopfield associative memory. In Advances in Soft Computing - 10th Mexican International Conference on Artificial Intelligence, MICAI 2011, Proceedings. PART 2 ed. 2011. p. 522-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-25330-0_46

Efficient pattern recalling using a non iterative hopfield associative memory

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