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- W12871865 abstract "In a particular associative memory for learning control systems a function f( s ) of n variables {s i } is represented for each point s as the sum of a number of stored weights, the values of which are determined in a preliminary training or learning mode. The choice of weights for a particular point s k of the Euclidean space R n defined by the { s i } may be represented by a p-vector a k containing ρ “ones” and p- ρ “zeros” ( ρ <<p), with the position of the “ones” denoting the particular ρ weights chosen to represent s k . It is a desirable property that given two points s j and s k the Hamming distance between two corresponding weights vectors a j and a k is proportional to the Euclidean distance between tne points s j and s k This problem of finding vectors a is shown to be equivalent to geometrical problem of the placing of ρ -1 points have differing integer coordinates inside a hypercube of dimension n and size ρ x ρ x ρ x … x ρ ( ρ repeated n times). Computer generated lattices nave been devised using arithmetic progressions (modulo ρ ) to generate coordinates for placing of the ρ -1 points. Values of n from 2 to 10 and values of ρ from 2 to 50 are considered. A table of such lattices is presented which improves an earlier and less sophisticated scheme devised by J.S. Albus in 1975" @default.
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- W12871865 date "1991-09-01" @default.
- W12871865 modified "2023-09-25" @default.
- W12871865 title "Improved Allocation of Weights for Associative Memory Storage in Learning Control Systems" @default.
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- W12871865 doi "https://doi.org/10.1016/s1474-6670(17)54222-6" @default.
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