Design and Analysis of One-to-Many Associative Memory Driven by External Input

This article presents a cascading neural network model to implement one-to-many associative memories, where the output of the preceding network serves as the input of the next network, and the model is driven by external input patterns. The design procedure is provided by considering the existence and global asymptotical stability of equilibrium point. Compared to traditional multidirectional associative memory models, the model proposed in this article requires fewer neurons to achieve the same functionality and avoids the issue of pseudo-equilibrium points. The illustrative example is given to support the effectiveness of the theoretical results.