Abstract
A dynamic method which produces estimates of real eigenvectors and eigenvalues is presented. More generally, the technique can be applied to estimate eigenspectra of real n-dimensional k-forms. The proposed approach is based on a spectral splicing property of the line manifolds often found in solutions of polynomial differential equations. As such, it defines an artificial continuous time neural network with stored memories determined by the eigenspectra locations. This paradigm provides a good insight into an analog behavior of large scale neural structures which provide auto- or hetero-associative memories. Consequently, it has applications not only in computational sciences but also as an information processor.
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Samardzija, N., Waterland, R.L. A neural network for computing eigenvectors and eigenvalues. Biol. Cybern. 65, 211–214 (1991). https://doi.org/10.1007/BF00206218
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DOI: https://doi.org/10.1007/BF00206218