Optimal learning algorithm in bidirectional associative memories
The authors present an optimal learning algorithm in a discrete bidirectional associative memory (BAM). According to the cost function that measures the goodness of BAM, the authors transform the problem of specifying the connection matrix into a global optimization, solved by a gradient descent method. This learning algorithm guarantees to store all trained patterns whenever it is allowable. Experimental results are reported to demonstrate the power of the algorithm.<<ETX>>