title: Recognition of general patterns using neural networks.
abstract: The Hopfield model of neural network stores memory in its symmetric synaptic connections and can only learn to recognize sets of nearly "orthogonal" patterns. A new algorithm is put forth to permit the recognition of general ("non-orthogonal") patterns. The algorithm specifies the construction of the new network's memory matrix Tij, which is, in general, asymmetrical and contains the Hopfield neural network (Hopfield 1982) as a special case. We find further that in addition to this new algorithm for general pattern recognition, there exists in fact a large class of Tij memory matrices which permit the recognition of non-orthogonal patterns. The general form of this class of Tij memory matrix is presented, and the projection matrix neural network (Personnaz et al. 1985) is found as a special case of this general form. This general form of memory matrix extends the library of memory matrices which allow a neural network to recognize non-orthogonal patterns. A neural network which followed this general form of memory matrix was modeled on a computer and successfully recognized a set of non-orthogonal patterns. The new network also showed a tolerance for altered and incomplete data. Through this new method, general patterns may be taught to the neural network.
authors.affiliation: Oak Ridge High School, TN 37830.
authors.forename: A J
authors.initials: AJ
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology; D008433:Mathematics; D008568:Memory; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology