A New Bi-directional Associative Memory

Abstract. Hebbian hetero-associative learning is inherently asymmetric. Storing a forward association from pattern A to pattern B enables the recalling of pattern B given pattern A. This, in general, does not allow the recalling of pattern A given pattern B. The forward association between A and B will tend to be stronger than the backward association between B and A. In this paper it is described how the dynamical associative model proposed in [10] can be extended to create a bi-directional associative memory where forward association between A and B is equal to backward association between B and A. This implies that storing a forward association, from pattern A to pattern B, would enable the recalling of pattern B given pattern A and the recalling of pattern A given pattern B. We give some formal results that support the functioning of the proposal, and provide some examples were the proposal finds application.

An artificial associative neural net for underground mine landmark recognition.

A new bi-directional associative memory (BAM), a type of artificial neural net, is proposed, defined, demonstrated and tested as the core classifier in a landmark recognition system for underground mine navigation. The simulation of this new BAM shows improved ability in recognizing images of objects as compared with the BAM proposed by Kosko. This dissertation documents: (1) A new bi-directional associative memory model which shows an obvious improvement over the Kosko BAM in applications of pattern recognition; (2) A new distinctive system structure using the proposed BAMs for multiple feature space pattern recognition applications; (3) The analysis of simulation results for underground mine landmark recognition. (4) Image sets of underground mine objects for system testing in future applications