LAUSR

Abstract A complex-valued Hopfield neural network (CHNN) has been widely used for the storage of image data. The CHNN has been extended using hypercomplex numbers. A couple of hypercomplex-valued Hopfield neural networks employ a twin-multistate activation function to reduce the numbers of weight parameters. In this work, we propose a matrix-valued twin-multistate Hopfield neural network (MTMHNN), whose neuron states and weights are 2 × 2 matrices. Computer simulations show that the MTMHNN has better noise tolerance than the hypercomplex-valued twin-multistate Hopfield neural networks.