A novel neural hetero-associative memory model for pattern recognition

A novel hetero-associative neural network model is proposed where the associative recall of pattern is achieved in a single pass through the system. Instead of forming the memory matrix by an outer product formulation, inner product cross-correlation of input data with each set of the library vector was performed. The limitation regarding the constraint imposed on the choice or selection of patterns that can be stored is avoided by such a formulation. The reliability of the proposed model is much improved in comparison to the heteroassociative memory models which uses outer product correlation formulation to construct the memory matrix.