Adaptive learning based on bit - significance optimization of Hopfield model and its electro - optical implementation for correlated images
Soo-Young Lee, Chang-Sup Shim, Ju-Seog Jang, Sang-Yung Shin · 1989
Introducing and optimizing bit-significance to the Hopfield model, ten highly correlated binary images, i.e., numbers 0 to 9, are successfully stored and retrieved in a 6×8 node system. Unlike many other neural networks models, this model has stronger error correction capability for correlated images such as 6, 8, 3, and 9. The bit-significance optimization is regarded as an adaptive learning process based on least-mean-square error algorithm, and may be implemented with another neural nets optimizer. A design for electro-optic implementation including the adaptive optimization networks is also introduced.