Abstract
This paper presents a novel, three-stage, auto-associative memory based on lattice algebra. The first two stages of this memory consist of correlation matrix memories within the lattice domain. The third and final stage is a two-layer feed-forward network based on dendritic computing. The output nodes of this feed-forward network yield the desired pattern vector association. The computations performed by each stage are all lattice based and, thus, provide for fast computation and avoidance of convergence problems. Additionally, the proposed model is extremely robust in the presence of noise. Bounds of allowable noise that guarantees perfect output are also discussed.
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Ritter, G.X., Iancu, L., Schmalz, M.S. (2004). A New Auto-associative Memory Based on Lattice Algebra. In: Sanfeliu, A., MartÃnez Trinidad, J.F., Carrasco Ochoa, J.A. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2004. Lecture Notes in Computer Science, vol 3287. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30463-0_18
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DOI: https://doi.org/10.1007/978-3-540-30463-0_18
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