Hyperbolic-valued Hopfield neural networks in hybrid mode

摘要

A complex-valued Hopfield neural network (CHNN) has been applied as a multistate neural associative memory. Although synchronous mode accelerates the recall process, a CHNN with a projection rule may be trapped at a cycle of length two. A hyperbolic-valued Hopfield neural network (HHNN) with a conventional projection rule converges to a fixed point in synchronous mode. A noise robust projection rule improves the noise tolerance of HHNN, though it is not able to be applied to an HHNN in synchronous mode. In this work, we proposed hybrid mode, that is, asynchronous mode after synchronous mode. The conventional projection rule is employed in synchronous mode, and the noise robust projection rule is employed in asynchronous mode. Thus, the HHNN in hybrid mode converges in both modes. The HHNN in hybrid mode is expected to provide better noise tolerance than an HHNN in synchronous mode and faster recall than an HHNN in asynchronous mode. Computer simulations imply that our expectations are achieved.(c) 2021 Elsevier B.V. All rights reserved.