Abstract
In this paper, first we derive a novel relaxation method for the system of linear inequalities and apply it to the learning for associative memories. Since the proposed intersection learning can guarantee the recall of all training data, it can greatly enlarge the storage capacity of associative memories. In addition, it requires much less weights renewal times than the conventional methods. We also propose a multimodule associative memory which can be learned by the intersection learning algorithm. The proposed associative memory can deal with many-to-many associations and it is applied to a knowledge processing task. Computer simulation results show the effectiveness of the proposed learning algorithm and associative memory.
| Original language | English |
|---|---|
| Title of host publication | International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES |
| Publisher | IEEE |
| Pages | 377-386 |
| Number of pages | 10 |
| Volume | 2 |
| Publication status | Published - 1998 |
| Externally published | Yes |
| Event | Proceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98) - Adelaide, Aust Duration: 1998 Apr 21 → 1998 Apr 23 |
Other
| Other | Proceedings of the 1998 2nd International Conference on knowledge-Based Intelligent Electronic Systems (KES '98) |
|---|---|
| City | Adelaide, Aust |
| Period | 98/4/21 → 98/4/23 |
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ASJC Scopus subject areas
- Computer Science(all)
Cite this
Neural associative memory for intelligent information processing. / Hattori, Motonobu; Hagiwara, Masafumi.
International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES. Vol. 2 IEEE, 1998. p. 377-386.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Neural associative memory for intelligent information processing
AU - Hattori, Motonobu
AU - Hagiwara, Masafumi
PY - 1998
Y1 - 1998
N2 - In this paper, first we derive a novel relaxation method for the system of linear inequalities and apply it to the learning for associative memories. Since the proposed intersection learning can guarantee the recall of all training data, it can greatly enlarge the storage capacity of associative memories. In addition, it requires much less weights renewal times than the conventional methods. We also propose a multimodule associative memory which can be learned by the intersection learning algorithm. The proposed associative memory can deal with many-to-many associations and it is applied to a knowledge processing task. Computer simulation results show the effectiveness of the proposed learning algorithm and associative memory.
AB - In this paper, first we derive a novel relaxation method for the system of linear inequalities and apply it to the learning for associative memories. Since the proposed intersection learning can guarantee the recall of all training data, it can greatly enlarge the storage capacity of associative memories. In addition, it requires much less weights renewal times than the conventional methods. We also propose a multimodule associative memory which can be learned by the intersection learning algorithm. The proposed associative memory can deal with many-to-many associations and it is applied to a knowledge processing task. Computer simulation results show the effectiveness of the proposed learning algorithm and associative memory.
UR - http://www.scopus.com/inward/record.url?scp=0032218785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032218785&partnerID=8YFLogxK
M3 - Chapter
AN - SCOPUS:0032218785
VL - 2
SP - 377
EP - 386
BT - International Conference on Knowledge-Based Intelligent Electronic Systems, Proceedings, KES
PB - IEEE
ER -