On the learning dynamics of spatiotemporal neural networks
Jung-Hua Wang, Genghis Lin · 2002
Previously, spatiotemporal neural networks (STNNs) have been tested for applications such as speech recognition, radar and sonar echoes. STNNs have shown their plausibility using Kohonen's competitive learning and the Kosko/Klopf rule. This paper presents a modified version of the dynamic equation (used in determining the next output of a neuron) that can help ease the tuning problem. For asymmetric or temporal sequence learning, the authors analyze the Kosko/Klopf rule, and prove that the necessary condition in achieving asymptotic stability is to keep 0>