Abstract
Recurrent neural networks have interesting properties and can handle dynamic information processing unlike the ordinary feedforward neural networks. Bidirectional associative memory (BAM) is a typical recurrent network. Ordinarily, weights of the BAM are determined by the Hebb's learning. In this paper, a recursive learning scheme for BAM is proposed and its hardware implementation is described. The learning scheme is applicable to analogue BAM as well. A simulation result and details of the implementation are shown.
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