Title :
A neural decision estimator for maneuvering targets
Author_Institution :
Beijing Univ. of Aeronaut. & Astronaut., China
fDate :
27 Jun- 2 Jul 1994
Abstract :
The idea of solving constrained optimization problems such as the TSP with Hopfield neural network is used and an algorithm of the neural decision (ND) of maneuvering levels is put forward. Because the ND algorithm is parallel, the ND adaptive estimator can compute as fast as an ordinary Kalman filter based on a 2nd-order model. Computer simulations indicate it has a satisfactory performance in tracking maneuvering targets
Keywords :
Hopfield neural nets; adaptive estimation; decision theory; optimisation; state estimation; target tracking; tracking; travelling salesman problems; Hopfield neural network; TSP; adaptive estimator; constrained optimization; maneuvering levels; maneuvering targets; neural decision estimator; tracking; Cities and towns; Computer simulation; Concurrent computing; Constraint optimization; Hopfield neural networks; Neodymium; Neural networks; Neurons; Target tracking; Voltage;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374839
