Computational Optimization and Applications
Fuzzy logic and neurofuzzy applications explained
Fuzzy logic and neurofuzzy applications explained
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A New Lagrangian Relaxation Based Algorithm for a Class ofMultidimensional Assignment Problems
Computational Optimization and Applications
Fuzzy logic approach to multisensor data association
Mathematics and Computers in Simulation
Using a genetic algorithm for multi-hypothesis tracking
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
International Journal of RF and Microwave Computer-Aided Engineering
Mathematical and Computer Modelling: An International Journal
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Hi-index | 12.05 |
In this paper, a genetic tracker with adaptive neuro-fuzzy inference system (GT-ANFIS) is presented for multiple target tracking (MTT). First, the data association problem, formulated as an N-dimensional assignment problem, is solved using the genetic algorithm (GA), and then the inaccuracies in the estimation are corrected by the adaptive neuro-fuzzy inference system (ANFIS). The performances of the GT-ANFIS, the joint probabilistic data association filter (JPDAF), the genetic tracker (GT), and the genetic tracker with neural network (GT-NN) are compared with each other for six different tracking scenarios. It was shown that the tracks estimated by using proposed GT-ANFIS agree better with the true tracks than the tracks predicted by the JPDAF, the GT, and the GT-NN.