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Hebbian learning rule is well known as a memory storing scheme for associative memory models. This scheme is simple and fast, however, its performance gets decreased when memory patterns are not orthogonal each other. Pseudo-orthogonalization is a decorrelating method for memory patterns which uses XNOR masking between the memory patterns and randomly generated patterns. By a combination of this method...
This paper explores two types of multistate Hopfield neural networks, based on commutative quaternions that are similar to Hamilton's quaternions but with commutative multiplication. In one type of the networks, the state of a neuron is represented by two kinds of phases and one real number. The other type of the networks adopts the decomposed form of commutative quaternion, i.e., the state of a neuron...
We propose a learning scheme for multistate complex-valued and quaternionic neural networks in order to store correlated patterns with respect to each other. This is an extension of the so-called local iterative scheme for real-valued Hopfield neural networks. We first show the stability of desired memory patterns for a multistate complex-valued network and also for the multistate quaternionic network.
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