Computing element for neural networks

Details Computing element for neural networks Computing element for neural networks

1989-08-07

1994-06-07

Shaw, Dale M.

Boots, shoes, and leggings

395 27, 307201, G06F 738

Patent

active

053195876

ABSTRACT:
A computing element for use in an array in a neural network. Each computing element has K (K>1) input signal terminals, K input backpropagated signal terminals, K output backpropagated signal terminals and at least one output terminal. The input terminals of the computing element located in row i, column j of the array of computing elements receive a sequence of concurrent input signals on K parallel input lines representing a parallel input signal S.sub.ij having vector elements (s.sub.ij1, s.sub.ij2, s.sub.ij3, . . . , s.sub.ijk).sup.T. The K input backpropagated signal terminals are coupled to receive an m-dimensional (m<K) backpropagated signal vector characterized to provide a measure of the performance error of the computing element. The computing element comprises a weighting function means responsive to the concurrent input signal S.sub.ij for computing a K-dimensional weighting coefficient and a scalar activation signal u.sub.ij by computing a K-dimensional weighting-coefficient vector, W.sub.ij =(w.sub.ij1, w.sub.ij2, . . . , w.sub.ijk).sup.T, and by forming the vector inner product of the input signal S.sub.ij vector elements and the weighting-coefficient vector, W.sub.ij. Feedback signals x.sub.ijk =w.sub.ijk *p.sub.ij are provided from the output backpropagated signal terminals where p.sub.ij provides a performance error of all columns of computing elements subsequent to this computing element weighted by the gain of the computing element. A nonlinear processor maps successive values of u.sub.ij through a nonlinear mapping function, M.sub.ij to provide a single-valued output signal y.sub.ij. The nonlinear processor responds to an m-dimensional backpropagated signal vector, X.sub.ij+1 =(x.sub.1,j+1,p ; x.sub.2,j+1,p ; x.sub.3,j+1,p ; . . . x.sub.m,j+1,p).sup.T characterized to provide a measure of the performance error of all computing elements subsequent to the computing element. Vector X.sub.i,j+1 comprises the pth member of all backpropagated error vectors from the computing elements of column j+1.

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Rippmann, "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, pp. 4-22 Apr. 1987.

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