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Updating optical pseudoinverse associative memories.

B Telfer, D Casasent

|June 18, 2010

Summary

This study compares algorithms for updating optical pseudoinverse associative memories, focusing on methods suitable for optical implementation. It evaluates techniques based on memory size, speed, and ease of updating for enhanced performance.

Area of Science:

* Computer Science
* Optical Engineering
* Machine Learning

Background:

* Optical pseudoinverse associative memories offer a unique approach to information storage and retrieval.
* Efficient updating algorithms are crucial for the practical application of these memory systems.

Purpose of the Study:

* To present and compare selected algorithms for adding and deleting information in optical pseudoinverse associative memories.
* To describe new realizations of pseudoinverse updating methods for associative memory applications.
* To evaluate different updating methods based on key performance criteria.

Main Methods:

* Exploration of vector inner product matrix bordering for pseudoinverse updating.
* Application of reduced-dimensionality Karhunen-Loeve approximations for updating optical filters in associative memories.

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* Review and comparison of Greville's theorem with the Widrow-Hoff algorithm.

* Reformulation of Kohonen's gradient projection method for optical implementation.

* Discussion of the data matrix memory for comparative analysis.

Main Results:

* New methods for updating optical pseudoinverse associative memories are detailed.
* Greville's theorem and the Widrow-Hoff algorithm are compared.
* Kohonen's gradient projection method is adapted for optical systems.
* Performance of various updating algorithms is assessed against criteria like memory size, speed, and update ease.

Conclusions:

* The study provides a comparative analysis of algorithms for optical pseudoinverse associative memories.
* Novel updating techniques are presented, suitable for optical implementation.
* The findings guide the selection of efficient algorithms based on specific application requirements.