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Sensitivity to noise in bidirectional associative memory (BAM).

Shengzhi Du1, Zengqiang Chen, Zhuzhi Yuan

  • 1Department of Automation, Nankai University, Tianjin 300071, China.

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Summary

This study enhances bidirectional associative memory (BAM) by improving noise immunity. New methods, like SVBAM, EPRLAB, and EBL, boost error correction without sacrificing pattern capacity.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Original Hebbian encoding for Bidirectional Associative Memory (BAM) has limitations in pattern capacity and recall.
  • Existing perceptron-based methods improve pattern capacity but neglect error correction.
  • Noise sensitivity is a critical but often overlooked aspect of BAM performance.

Purpose of the Study:

  • To analyze the noise sensitivity of BAM and develop methods to enhance its error correction capability.
  • To investigate the relationship between noise sensitivity, minimum absolute value of net inputs (MAV), and weight variance.
  • To introduce and evaluate novel encoding schemes for improved BAM noise immunity.

Main Methods:

  • Analysis of BAM failure association to identify key factors influencing noise sensitivity.
  • Development of a principle for improving BAM error correction based on MAV and weight variance.
  • Implementation and testing of revised encoding schemes: Small Variance Learning for BAM (SVBAM), Evolutionary Pseudorelaxation Learning for BAM (EPRLAB), and Evolutionary Bidirectional Learning (EBL).

Main Results:

  • BAM noise sensitivity is a function of the quotient of MAV divided by weight variance.
  • SVBAM, EPRLAB, and EBL demonstrate superior noise immunity compared to original BAM.
  • These improved methods do not negatively impact BAM's pattern capacity.

Conclusions:

  • The proposed principle offers a viable strategy for enhancing BAM's error correction capabilities.
  • EPRLAB and EBL guarantee convergence to global optimal solutions for pattern capacity and noise immunity.
  • SVBAM's convergence may be influenced by preset functions, requiring further investigation.