Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
A course in fuzzy systems and control
A course in fuzzy systems and control
Robust interval regression analysis using neural networks
Fuzzy Sets and Systems
Introduction to Linear Regression Analysis, Solutions Manual (Wiley Series in Probability and Statistics)
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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
On the optimal design of fuzzy neural networks with robust learningfor function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
Fuzzy identification using fuzzy neural networks with stable learning algorithms
IEEE Transactions on Fuzzy Systems
TSK-fuzzy modeling based on ϵ-insensitive learning
IEEE Transactions on Fuzzy Systems
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Preliminary Study on Wilcoxon Learning Machines
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer
International Journal of Fuzzy System Applications
D-FNN based soft-sensor modeling and migration reconfiguration of polymerizing process
Applied Soft Computing
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In this paper, M-estimators, where M stands for maximum likelihood, used in robust regression theory for linear parametric regression problems will be generalized to nonparametric maximum likelihood fuzzy neural networks (MFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed MFNNs. Simulation results show that the MFNNs proposed in this paper have good robustness against outliers.