Neural networks for pattern recognition
Neural networks for pattern recognition
Neural network design
Swarm intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
IEEE Transactions on Signal Processing
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
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This paper describes a robust modeling method to handle inverse problems with missing data. The modeling method is applied to aircraft fuel measurement considering sensor failure. Neural Networks that are tolerant to noisy data are adapted to approximate the nonlinear physical process. Unlike previous algorithms that use gradient information to search input space in inverse problems, the proposed method thoroughly explores the input space using particle swarm optimization. The comparison results show the effectiveness of our method in dealing with missing data.