TY - JOUR

T1 - Neighbor-Layer Updating in MBDS for the Recall of Pure Bipolar Patterns in Gray-Scale Noise

AU - Lee, Donq Liang

AU - Wang, Wen June

N1 - Funding Information:
Manuscript received-August 19, 1993, revised March 25, 1994. This work was supported in part by the National Science Council, Republic of China, under contract NSC81-0408-E008-507. The authors are with the Institute of Computer Science and Electronic Engineering, National Central University, Chung Li 320, Taiwan, R.O.C. IEEE Log Number 9409392.

PY - 1995/11

Y1 - 1995/11

N2 - To improve bidirectional associative memory (BAM), a modified bidirectional decoding strategy (MBDS) network has been proposed. The former is a two-layer structure in which stored associations are recalled by directionally updating the neuron state through the connecting weights M and MT. The latter is an extension of the former in which two hidden layers are augmented and the corresponding extra connection weights-M. My, Tx, and Ty-are encoded. We introduce a new updating rule for MBDS networks, called neighbor-layer updating (NLU), which gathers all weighted activations of all neighbor layers. The neighbor layers are defined as the layers in which there are direct synaptic weights connected to each other. Because of modification of the connection weights-Mx, My, Tx, and Ty-and the constant bias inputs of MBDS, all stored associations are guaranteed to be recalled using NLU. Furthermore, with the aid of the Cohen-Grossberg theorem, all discrete MBDS results can be extended to continuous MBDS (we name it CMBDS). We also give stability proofs of both discrete MBDS and CMBDS. Computer simulations demonstrate that the proposed CMBDS can be applied to recall pure bipolar patterns in the presence of gray-scale noise. We show that by removing BAM connections (matrix M) from the MBDS structure, a bidirectional holographic memory (BHM) is obtained. Both derivation and simulation indicate that we can remove the matrix M from the MBDS structure if the dimension of the training associations is larger than 16.

AB - To improve bidirectional associative memory (BAM), a modified bidirectional decoding strategy (MBDS) network has been proposed. The former is a two-layer structure in which stored associations are recalled by directionally updating the neuron state through the connecting weights M and MT. The latter is an extension of the former in which two hidden layers are augmented and the corresponding extra connection weights-M. My, Tx, and Ty-are encoded. We introduce a new updating rule for MBDS networks, called neighbor-layer updating (NLU), which gathers all weighted activations of all neighbor layers. The neighbor layers are defined as the layers in which there are direct synaptic weights connected to each other. Because of modification of the connection weights-Mx, My, Tx, and Ty-and the constant bias inputs of MBDS, all stored associations are guaranteed to be recalled using NLU. Furthermore, with the aid of the Cohen-Grossberg theorem, all discrete MBDS results can be extended to continuous MBDS (we name it CMBDS). We also give stability proofs of both discrete MBDS and CMBDS. Computer simulations demonstrate that the proposed CMBDS can be applied to recall pure bipolar patterns in the presence of gray-scale noise. We show that by removing BAM connections (matrix M) from the MBDS structure, a bidirectional holographic memory (BHM) is obtained. Both derivation and simulation indicate that we can remove the matrix M from the MBDS structure if the dimension of the training associations is larger than 16.

UR - http://www.scopus.com/inward/record.url?scp=0029406204&partnerID=8YFLogxK

U2 - 10.1109/72.471361

DO - 10.1109/72.471361

M3 - 期刊論文

AN - SCOPUS:0029406204

SN - 1045-9227

VL - 6

SP - 1478

EP - 1489

JO - IEEE Transactions on Neural Networks

JF - IEEE Transactions on Neural Networks

IS - 6

ER -