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Encoding method for bidirectional associative memory using projection on convex sets.

¹Dept. of Comput. Sci., Chinese Univ. of Hong Kong, Shatin.

IEEE Transactions on Neural Networks

|January 1, 1993

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The enhanced Householder encoding algorithm (EHCA) significantly improves bidirectional associative memory (BAM) capacity and convergence. This new method combines Householder encoding with projection on convex sets for better performance.

Area of Science:

Artificial Intelligence
Machine Learning
Neural Networks

Background:

Traditional bidirectional associative memory (BAM) encoding methods, like correlation, have limited capacity.
The Householder encoding algorithm (HCA) improves BAM capacity but results in two matrices, potentially causing convergence issues.

Purpose of the Study:

To present an enhanced Householder encoding algorithm (EHCA) for bidirectional associative memory (BAM).
To improve the capacity and maintain the convergence property of BAM.

Main Methods:

Developed the enhanced Householder encoding algorithm (EHCA) by integrating the Householder encoding algorithm (HCA) with projection on convex sets (POCS).
Reduced the two interconnection matrices from HCA into a single matrix using POCS to ensure BAM convergence.

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Main Results:

The enhanced Householder encoding algorithm (EHCA) greatly improves the capacity of bidirectional associative memory (BAM).
EHCA maintains the convergent property of BAM, unlike the original HCA.
Simulation results validate the significant capacity enhancement.

Conclusions:

The enhanced Householder encoding algorithm (EHCA) offers a superior approach for BAM implementation.
EHCA addresses the limitations of previous methods, providing both high capacity and reliable convergence.