### Abstract

This paper proposes a method to compute exact amount of noise in recalling every encoded pattern. This can be used to find vectors for construction of augmented matrices for both Wang et al., and Wang-Lee methods. The computation of noise also gives a method for obtaining minimum dimension of augmented vectors. This information is used to construct augmented matrix for guaranteed recall of every encoded pattern. A three layer Bidirectional Associative Memory (TLBAM) that requires smaller size correlation matrices is also proposed. The number of elements in the correlation matrices are proportional to the number of pattern-pairs encoded. The TLBAM when encoded using the novel augmentation method guarantees correct recall of all stored pairs. The encoded network will also require much fewer elements than any other method.

Original language | English (US) |
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State | Published - Jan 1 1996 |

Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: Jun 3 1996 → Jun 6 1996 |

### Other

Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
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City | Washington, DC, USA |

Period | 6/3/96 → 6/6/96 |

### Fingerprint

### ASJC Scopus subject areas

- Software

### Cite this

*Three-stage architecture for bidirectional associative memory*. Paper presented at Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4), Washington, DC, USA, .

**Three-stage architecture for bidirectional associative memory.** / Sarkar, Dilip.

Research output: Contribution to conference › Paper

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TY - CONF

T1 - Three-stage architecture for bidirectional associative memory

AU - Sarkar, Dilip

PY - 1996/1/1

Y1 - 1996/1/1

N2 - This paper proposes a method to compute exact amount of noise in recalling every encoded pattern. This can be used to find vectors for construction of augmented matrices for both Wang et al., and Wang-Lee methods. The computation of noise also gives a method for obtaining minimum dimension of augmented vectors. This information is used to construct augmented matrix for guaranteed recall of every encoded pattern. A three layer Bidirectional Associative Memory (TLBAM) that requires smaller size correlation matrices is also proposed. The number of elements in the correlation matrices are proportional to the number of pattern-pairs encoded. The TLBAM when encoded using the novel augmentation method guarantees correct recall of all stored pairs. The encoded network will also require much fewer elements than any other method.

AB - This paper proposes a method to compute exact amount of noise in recalling every encoded pattern. This can be used to find vectors for construction of augmented matrices for both Wang et al., and Wang-Lee methods. The computation of noise also gives a method for obtaining minimum dimension of augmented vectors. This information is used to construct augmented matrix for guaranteed recall of every encoded pattern. A three layer Bidirectional Associative Memory (TLBAM) that requires smaller size correlation matrices is also proposed. The number of elements in the correlation matrices are proportional to the number of pattern-pairs encoded. The TLBAM when encoded using the novel augmentation method guarantees correct recall of all stored pairs. The encoded network will also require much fewer elements than any other method.

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