Chaotic neurochips for fuzzy computing

Harold H. Szu; Lotfi A. Zadeh; Charles C. Hsu; Joseph T. DeWitte Jr.; Gyu Moon; Desa Gobovic; Mona E. Zaghloul

doi:10.1117/12.167517

1 March 1994 Chaotic neurochips for fuzzy computing

Harold H. Szu, Lotfi A. Zadeh, Charles C. Hsu, Joseph T. DeWitte Jr., Gyu Moon, Desa Gobovic, Mona E. Zaghloul

Proceedings Volume 2037, Chaos/Nonlinear Dynamics: Methods and Commercialization; (1994) https://doi.org/10.1117/12.167517
Event: SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, 1993, San Diego, CA, United States

Abstract

A massive chaotic neural network (CNN) is demonstrated with a fixed-point Hebbian synaptic weight dynamic: an instantaneous input, and a piecewise negative logic output. The variable slope of the output versus the input becomes a software control of the collective chaos hardware. Two applications are given. The mean synaptic weight field plays an important role for fast pattern recognition capability in examples of both the habituation and the novelty detections. Another novel usage of CNN is to be a bridge between neural learning and learnable fuzzy logic.

Citation Download Citation

Harold H. Szu, Lotfi A. Zadeh, Charles C. Hsu, Joseph T. DeWitte Jr., Gyu Moon, Desa Gobovic, and Mona E. Zaghloul "Chaotic neurochips for fuzzy computing", Proc. SPIE 2037, Chaos/Nonlinear Dynamics: Methods and Commercialization, (1 March 1994); https://doi.org/10.1117/12.167517

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