Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural\nnetwork (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates,\nand CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the\nlocal minima, and their noise tolerance is low. Lee improved the noise tolerance of the CHNNs by detecting and exiting the local\nminima. In the present work, we propose a new recall algorithm that eliminates the local minima.We show that our proposed recall\nalgorithm not only accelerated the recall but also improved the noise tolerance through computer simulations.
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