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- W1821981656 abstract "The authors present a generalized Hopfield algorithm which is based on the Gramm-Schmidt orthogonal process and the gradient descent approach. The method studies the correlation between input and stored vectors and reduces the cross-correlation noise by using the orthogonal technique. Simulation results have shown an increase in storage capacity with respect to the number of stored vectors. Although one can apply the K-L transform or the discrete cosine transform on the training patterns, the proposed model is much better for practical implementation using a neural network. A significant improvement of the signal-to-noise ratio is obtained. The model is implementable on any 'inner product' version of the Hopfield machine. >" @default.
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- W1821981656 date "2002-12-09" @default.
- W1821981656 modified "2023-09-22" @default.
- W1821981656 title "Self-orthogonalization associative memories" @default.
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- W1821981656 doi "https://doi.org/10.1109/ijcnn.1991.170545" @default.
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