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Max-plus and min-plus projection autoassociative morphological memories and their compositions for pattern
Alex Santana Dos Santos1, Marcos Eduardo Valle2
1Exact and Technological Science Center, Federal University of the Recôncavo of Bahia, Rua Rui Barbosa, 710, Centro - Cruz das Almas-BA CEP 44.380-000, Brazil.
Summary
New projection autoassociative morphological memories (PAMMs) offer unlimited storage and fast retrieval. These robust models demonstrate excellent noise tolerance and promising results in complex classification tasks.
Area of Science:
- Computer Science
- Artificial Intelligence
- Mathematical Morphology
Background:
- Autoassociative morphological memories (AMMs) are known for their robustness and computational efficiency.
- Existing AMMs offer unlimited storage capacity and single-step retrieval.
Purpose of the Study:
- Introduce novel max-plus and min-plus projection autoassociative morphological memories (PAMMs).
- Explore the properties and compositions of these new PAMMs.
- Evaluate their performance in classification problems.
Main Methods:
- Developed max-plus PAMMs to find the largest max-plus combination less than or equal to the input.
- Developed min-plus PAMMs to find the smallest min-plus combination greater than or equal to the input.
- Investigated compositions of these PAMMs and their behavior.
Main Results:
- PAMMs provide unlimited absolute storage capacity and one-step retrieval.
- The proposed PAMMs exhibit excellent noise tolerance.
- Demonstrated promising results in classification tasks with high dimensionality and numerous classes.
Conclusions:
- PAMMs represent a significant advancement in autoassociative memory models.
- Their properties make them suitable for complex data processing and classification.
- Further research into PAMM compositions could yield enhanced memory systems.
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