Optical implementation of neural networks

Francis T. S. Yu; Ruyan Guo

doi:10.1117/12.455865

6 December 2002 Optical implementation of neural networks

Francis T. S. Yu, Ruyan Guo

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.455865
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

An adaptive optical neuro-computing (ONC) using inexpensive pocket size liquid crystal televisions (LCTVs) had been developed by the graduate students in the Electro-Optics Laboratory at The Pennsylvania State University. Although this neuro-computing has only 8×8=64 neurons, it can be easily extended to 16×20=320 neurons. The major advantages of this LCTV architecture as compared with other reported ONCs, are low cost and the flexibility to operate. To test the performance, several neural net models are used. These models are Interpattern Association, Hetero-association and unsupervised learning algorithms. The system design considerations and experimental demonstrations are also included.

Citation Download Citation

Francis T. S. Yu and Ruyan Guo "Optical implementation of neural networks", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.455865

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