On activation functions for complex-valued neural networks: existence of energy functions
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Models of self-correlation type complex-valued associative memories and their dynamics
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
Complex-valued neural networks: the merits and their origins
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
| Hi-index | 0.00 |
Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. In complex-valued neural networks, there are several possibilities in choosing an activation function because of a wide variety of complex functions. This paper proposes three models of orthogonal type dynamic associative memories using complex-valued neural networks with three different activation functions. We investigate their behavior as associative memories theoretically. Comparisons are also made among these three models in terms of dynamics and storage capabilities.