A comparison of radial basis function networks and fuzzy neural logic networks for autonomous star recognition

Abstract

Autonomous star recognition requires that many similar patterns must be distinguished from one another with a small training set. Since these systems are implemented on-board a spacecraft, the network needs to have low memory requirements and minimal computational complexity. Fast training speeds are also important since star sensor capabilities change over time. This paper compares two networks that meet these needs: radial basis function networks and neural logic networks. Neural logic networks performed much better than radial basis function networks in terms of recognition accuracy, memory needed, and training speed.