Multilayer feedforward networks are universal approximators
Neural Networks
The Fuzzy C Quadratic Shell clustering algorithm and the detection of second-degree curves
Pattern Recognition Letters
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Fuzzy Systems as Universal Approximators
IEEE Transactions on Computers
Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Takagi-Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging
Expert Systems with Applications: An International Journal
Function approximation using fuzzy neural networks with robust learning algorithm
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
A hybrid clustering and gradient descent approach for fuzzymodeling
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
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
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
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Adaptive fuzzy c-shells clustering and detection of ellipses
IEEE Transactions on Neural Networks
The fuzzy c spherical shells algorithm: A new approach
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
Applied Computational Intelligence and Soft Computing
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The back propagation (BP) algorithm for function approximation is multilayer feed-forward perceptions to learn parameters from sampling data. The BP algorithm uses the least squares method to obtain a set of weights minimizing the object function. One of main issues on the BP algorithm is to deal with data sets having variety of data distributions and bound with noises and outliers. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a fuzzy-based data sifter is used to partition the nonlinear system's domain into several piecewise linear subspaces to be represented by neural networks. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm is proposed to greatly mitigate the influence of data noise and outliers. (2) A fuzzy-based data sifter is proposed to locate good turning-points to partition a given nonlinear data domain into piecewise clusters so that a neural network can be constructed with fewer rules. Two experiments are illustrated and these results have shown that the proposed approach has good performance in various kinds of data domains with data noise and outliers.