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A pseudo-inverse neural net with storage capacity exceeding N

By limiting the range of interaction between the prototype vectors in the autoassociative neural network and by calculating the pseudoinverse matrix from local clusters of prototypes, it is possible to store more than N correlated prototype vectors, increase the size of the basins of attraction, and include more close neighbors of prototypes in their basins. This conclusion is supported by figures for the sizes and shapes of the basins of attraction obtained from computer simulations. To demonstrate the technique, experiments were conducted with sets of 16 and 20 random prototype vectors and a network of 16 neurons at the input layer. Exhaustive scans of the state space of the input layer were used to get detailed information on the shapes and sizes of the basins. The results are presented and discussed