Chaotic neural network for learnable associative memory recall

Charles C. Hsu; Harold H. Szu

doi:10.1117/12.502480

1 April 2003 Chaotic neural network for learnable associative memory recall

Charles C. Hsu, Harold H. Szu

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.502480
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

We show that the Fuzzy Membership Function (FMF) is learnable with underlying chaotic neural networks for the open set probability. A sigmoid N-shaped function is used to generate chaotic signals. We postulate that such a chaotic set of innumerable realization forms a FMF exemplified by fuzzy feature maps of eyes, nose, etc., for the invariant face classification. The CNN with FMF plays an important role for fast pattern recognition capability in examples of both habituation and novelty detections. In order to reduce the computation complexity, the nearest-neighborhood weight connection is proposed. In addition, a novel timing-sequence weight-learning algorithm is introduced to increase the capacity and recall of the associative memory. For simplicity, a piece-wise-linear (PWL) N-shaped function was designed and implemented and fabricated in a CMOS chip.

Citation Download Citation

Charles C. Hsu and Harold H. Szu "Chaotic neural network for learnable associative memory recall", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.502480

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available