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.
A single homogeneous layer of neural network is reviewed. For optical computing, a vector outer product model of neural network is fully explored and is characterized to be quasi-linear (QL). The relationships among the hetero-associative memory [AM], the ill-posed inverse association (solved by annealing algorithm Boltzmann machine (BM)), and the symmetric interconnect [T] of Hopfield's model E(N) are found by applying Wiener's criterion to the output feature f and setting [EQUATION].
Harold Szu
"Three Layers Of Vector Outer Product Neural Networks For Optical Pattern Recognition", Proc. SPIE 0634, Optical and Hybrid Computing, (13 February 1986); https://doi.org/10.1117/12.964021
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.
The alert did not successfully save. Please try again later.
Harold Szu, "Three Layers Of Vector Outer Product Neural Networks For Optical Pattern Recognition," Proc. SPIE 0634, Optical and Hybrid Computing, (13 February 1986); https://doi.org/10.1117/12.964021