26 April 2011 The concept models and implementations of multiport neural net associative memory for 2D patterns
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Abstract
The paper considers neural net models and training and recognizing algorithms with base neurobiologic operations: p-step autoequivalence and non-equivalenc The Modified equivalently models (MEMs) of multiport neural net associative memory (MNNAM) are offered with double adaptive - equivalently weighing (DAEW) for recognition of 2D-patterns (images). It is shown, the computing process in MNNAM under using the proposed MEMs, is reduced to two-step and multi-step algorithms and step-by-step matrix-matrix (tensor-tensor) procedures. The given results of computer simulations confirmed the perspective of such models. Besides the result was received when MNNAM capacity on base of MEMs exceeded the amount of neurons.
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Vladimir G. Krasilenko, Aleksandr I. Nikolskyy, Rimma A. Yatskovskaya, Victor I. Yatskovsky, "The concept models and implementations of multiport neural net associative memory for 2D patterns", Proc. SPIE 8055, Optical Pattern Recognition XXII, 80550T (26 April 2011); doi: 10.1117/12.883669; https://doi.org/10.1117/12.883669
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