Lattice associative memories that are robust in the presence of noise
Gerhard X. Ritter, Gonzalo Urcid, Mark S. Schmalz · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
This paper presents a novel two-layer feedforward neural network that acts as an associative memory for pattern recall. The neurons of this network have dendritic structures and the computations performed by the network are based on lattice algebra. Use of lattice computation avoids multiplicative processes and, thus, provides for fast computation. The synaptic weights of the axonal fibers are preset, making lengthy training unnecessary. The proposed model exhibits perfect recall for perfect input vectors and is extremely robust in the presence of noisy or corrupted input.