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Mapping binary associative memories onto sigmoidal neural networks using a modified projection learning rule

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1 Author(s)
Perfetti, R. ; Istituto di Elettronica, Perugia Univ., Italy

This paper shows the applicability of the well-known projection learning rule to the design of associative memories based on continuous-time neural networks, with sigmoidal nonlinearities. The proposed design method exhibits several interesting features: learning capability, computational efficiency, exact storage of binary vectors as asymptotically stable equilibrium points, and global stability of the resulting network. An example is included to illustrate the method

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Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on  (Volume:41 ,  Issue: 7 )