Cs. Leung, OPTIMUM LEARNING FOR BIDIRECTIONAL ASSOCIATIVE MEMORY IN THE SENSE OFCAPACITY, IEEE transactions on systems, man, and cybernetics, 24(5), 1994, pp. 791-796
Citations number
15
Categorie Soggetti
Controlo Theory & Cybernetics","Computer Science Cybernetics","Engineering, Eletrical & Electronic
Borrowing the idea of Perceptron, Bidirectional Learning (BL) is propo
sed here to enhance the recall performance of Bidirectional Associativ
e Memory (BAM). By modifying the proof of convergence of Perceptron, w
e have proved that BL yields one of the solution connection matrices w
ithin a finite number of iterations (if the solutions exist). Accordin
g to the above convergence of BL, the capacity of BAM with BL is large
r than or equal to that with any other learning rule. Hence, BL can be
considered as an optimum learning rule for BAM in the sense of capaci
ty. Simulations show that BL greatly improves the capacity and the err
or correction capability of BAM.