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Designing asymmetric Hopfield-type associative memory with higher order hamming stability
Donq-Liang Lee1, Thomas C Chuang
1Department of Computer Science and Information Engineering, Vanung University, Chung-Li 32056, Taiwan, ROC. dllee@csie.vnu.edu.tw
IEEE Transactions on Neural Networks
|December 14, 2005
Summary
This study introduces a novel approach to designing Hopfield-type associative memory (HAM) by expanding attraction basins using Hamming distance. The method enhances recall capability and memory capacity in associative memory systems.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Computational Neuroscience
Background:
- Traditional Hopfield-type associative memory (HAM) design often relies on hyperplane optimization or constrained optimization for weight matrix calculation.
- Existing methods face limitations in effectively expanding the basins of attraction for prototype vectors in the bipolar state space.
Purpose of the Study:
- To propose a new method for designing asymmetric Hopfield-type associative memory (HAM) that improves recall capability and memory capacity.
- To introduce the concept of 'higher order Hamming stability' for systematic expansion of attraction basins.
- To investigate the impact of non-zero diagonal elements in the weight matrix on memory performance.
Main Methods:
- The study proposes systematically increasing the training set size to achieve desired basin of attraction sizes, termed 'higher order Hamming stability'.
- The conventional minimum-overlap algorithm is modified to incorporate this higher order Hamming stability concept.
- The research explores allowing non-zero diagonal elements (self-connections) in the weight matrix during learning.
Main Results:
- Experimental results demonstrate improved recall capability and a reduced number of spurious memories using the proposed method.
- Allowing non-zero diagonal elements in the weight matrix effectively enlarges the basin width around prototype vectors.
- Relaxing the constraint of zero self-connections can increase basin width with only a minor increase in spurious memories.
Conclusions:
- The proposed 'higher order Hamming stability' method offers a more effective approach to designing Hopfield-type associative memory.
- Enlarging attraction basins via Hamming distance and strategic self-connection learning enhances memory recall and capacity.
- This research provides a valuable contribution to the field of artificial neural networks and associative memory design.
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