Robust incremental growing multi-experts network
Applied Soft Computing
A hybrid approach to outlier detection in the offset lithographic printing process
Engineering Applications of Artificial Intelligence
The Chebyshev-polynomials-based unified model neural networks forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust radial basis function neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonlinear dynamic system identification using Chebyshev functionallink artificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Robust support vector regression networks for function approximation with outliers
IEEE Transactions on Neural Networks
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IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Outlier identify based on BP neural network in dam safety monitoring
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 2
Hybrid robust support vector machines for regression with outliers
Applied Soft Computing
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
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In this study, CPBUM neural networks with annealing robust learning algorithm (ARLA) are proposed to improve the problems of conventional neural networks for modeling with outliers and noise. In general, the obtained training data in the real applications maybe contain the outliers and noise. Although the CPBUM neural networks have fast convergent speed, these are difficult to deal with outliers and noise. Hence, the robust property must be enhanced for the CPBUM neural networks. Additionally, the ARLA can be overcome the problems of initialization and cut-off points in the traditional robust learning algorithm and deal with the model with outliers and noise. In this study, the ARLA is used as the learning algorithm to adjust the weights of the CPBUM neural networks. It tunes out that the CPBUM neural networks with the ARLA have fast convergent speed and robust against outliers and noise than the conventional neural networks with robust mechanism. Simulation results are provided to show the validity and applicability of the proposed neural networks.