Genetic approach to the design of bidirectional associative memory
Guoyong Shi · International Journal of Systems Science · 1997
A genetic design approach to learning the weight matrix of a bidirectional associative memory (BAM) is proposed in this paper. This approach removes inadequacies in conventional iterative learning algorithms. A restriction is made in the weight matrix representation in order to reduce the solution search. Design procedures are provided in detail. Simulations show that an approximate weight matrix can be effectively learnt by applying a genetic algorithm, which possesses extensive searching ability. By this approach, higher storage capacity and a satisfactory approximate recall can be realized despite the restriction imposed on the weight matrix. For the case that all training pattern pairs are storable, the genetic approach is capable of automatically making the attraction basin of each stored pattern pair as large as possible. This is realized by an appropriately defined discrete evaluation index. A larger attraction basin implies higher noise correction ability of the BAM. However, automatic adjustment of the attraction basin is difficult to realize by other methods.