DocumentCode
2441703
Title
A neural decision estimator for maneuvering targets
Author
Tao, Tao
Author_Institution
Beijing Univ. of Aeronaut. & Astronaut., China
Volume
6
fYear
1994
fDate
27 Jun- 2 Jul 1994
Firstpage
3926
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/ICNN.1994.374839
Filename
374839
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