Models of Self-Correlation Type Complex-Valued Associative Memories and Their Performance Comparison

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. We have already proposed a model of self-correlation type associative memories using complex-valued neural networks with one of the most commonly used activation functions. In this paper, we propose two additional models using different nonlinear complex functions and investigate their behavior as associative memories theoretically. Comparisons are also made among these three models in terms of their performance: dynamics and storage capabilities.