We found that due to high correlation of input data, a single hidden layer could not satisfactorily distinguish (with at least 55-85% accuracy) simple one-on-one maneuvers, such as the Turn-In, from more complex two-on-one maneuvers for this reason, two hidden layers were incorporated.

These sequences serve as the symbolic input to the artificial neural network we have provided. Additional inforraation can be used to establish which of the several alternative behaviors will actually take place.

This method has been used to describe the forms of relationships between accelerations and velocities (not the values themselves.) All possible modes of a system can be identified while offering a complete parametrization of all possible tactical maneuvers. We find tiust the resulting sequences of vectors uniquely express the time evolution of interacting dynamic objects. We have broken our central dynamical problem down into several smaller subproblems ('eigencm-ves'), which describe the states of a continuous-trajectory dynamic system. This problem is solved using a qualitative representation of the maneuvers and their implementation as a neural network.

The problem involves prediction and identification of continuous-trajectory air combat maneuvers where only partial/incomplete information is given. A.bstract-The goal of this paper is to consider, formulate, and solve prediction problems encountered in tactical air combat.

Air combat maneuvers rolling