Robust Function Approximation Using Ellipsoidal Fuzzy Rules

Robust Function Approximation Using Fuzzy Rules with Ellipsoidal Regions

This paper discusses robust function approximation when the Takagi-Sugeno type model is used for the consequent part of fuzzy rules. With this model, the parameters of the liner equation that defines the output value of the fuzzy rule are determined by the least-squares method. Therefore, if the training data include outliers, the method fails to determine the parameter values correctly. To ove...

Path Loss Prediction Using Fuzzy Inference System and Ellipsoidal Rules

It is well known as the prediction of radio wave path loss in urban environment plays a key role in order to correctly plan wireless systems and mobile communication networks. To obtain more flexible prediction models able to give accurate results, in recent years Soft computing Techniques has been exploited. In this study, a novel approach based on ellipsoidal fuzzy inference system EFIS is in...

Fuzzy rules extraction directly from numerical data for function approximation

Robust TSK fuzzy modeling for function approximation with outliers

The Takagi–Sugeno–Kang (TSK) type of fuzzy models has attracted a great attention of the fuzzy modeling community due to their good performance in various applications. Various approaches for modeling TSK fuzzy rules have been proposed in the literature. Most of them define their fuzzy subspaces based on the idea of training data being close enough instead of having similar functions. Besides, ...

Function Approximation Using Robust Wavelet Neural Networks

Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators but achieve faster convergence and are capable of dealing with the so-called “curse of dimensionality.” In addition, WNN are generalized RBFN. However, the generalization performance of WNN trained by least-squares appro...