Cognitive Networks for ATR: The Roles of Bifurcation and Chaos.
Quarterly rept. no. 2, Oct 94-1 Jan 95,
PENNSYLVANIA UNIV PHILADELPHIA
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In previous work we established the importance of developing neural networks that compute with diverse attractors as means for introducing cognition in neural networks. Cognition is the ability of a network to distinguish, on its own, between novel and familiar objects, the latter being the set of objects on which the network is trained and the former being the set of all objects never seen by the network before. Bifurcation between different kinds of attractors in such cognitive networks is the mechanism by which cognition is achieved. Cognition is essential in networks intended to operate in complex uncontrolled environment like that encountered in most automated target recognition ATR scenarios. For these reasons we began exploring neural net models that could exhibit all three types of attractors point, periodic, and chaotic. There is mounting evidence from biology to indicate that the study of such networks can help understand higher-level brain functions such as feature-linking, cognition, separation of object from background, inferencing, and other higher-level functions and can lead to the development of higher-level neural networks with enhanced capabilities.
- Computer Systems
- Target Direction, Range and Position Finding