Pattern stability on complex-valued associative memory by local iterative learning scheme

Stability of embedded patterns on associative memory is investigated in this paper. The associative memory is composed of complex-valued Hopfield neural network, in which the state of neurons are encoded by the phase values on a unit circle of complex plane. Local iterative learning scheme and Projection rule are used for embedding the patterns onto the network. The retaining performance for embedded patterns are evaluated through storing randomly generated patterns and gray-scaled images with changing the resolution of neuron state.