Identification of nonlinear discrete-time systems using raised-cosine radial basis function networks
International Journal of Systems Science
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Software note: Hepatitis C virus contact map prediction based on binary encoding strategy
Computational Biology and Chemistry
A procedure for face detection & recognition
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Journal of Medical Systems
Prediction of mean monthly total ozone time series-application of radial basis function network
International Journal of Remote Sensing
A Radial Basis Function Neural Network Model for Classification of Epilepsy Using EEG Signals
Journal of Medical Systems
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Robust Support Vector Regression Based on Fuzzy Clustering
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Robust incremental growing multi-experts network
Applied Soft Computing
A procedure for face detection & recognition
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Deformable radial basis functions
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Meta learning intrusion detection in real time network
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A reduced data set method for support vector regression
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
On maximum likelihood fuzzy neural networks
Fuzzy Sets and Systems
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Skew-Radial Basis Function Expansions for Empirical Modeling
SIAM Journal on Scientific Computing
Prediction of magnetic field near power lines by normalized radial basis function network
Advances in Engineering Software
Vision-Based Fingertip-Writing Character Recognition
Journal of Signal Processing Systems
A fast robust learning algorithm for RBF network against outliers
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Engineering Applications of Artificial Intelligence
Dynamics model abstraction scheme using radial basis functions
Journal of Control Science and Engineering - Special issue on Dynamic Neural Networks for Model-Free Control and Identification
A novel self-constructing Radial Basis Function Neural-Fuzzy System
Applied Soft Computing
Robust Neuroevolutionary Identification of Nonlinear Nonstationary Objects
Cybernetics and Systems Analysis
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Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function, However, it still suffers from two major problems. First, it is difficult to use Gaussian functions to approximate constant values. If a function has nearly constant values in some intervals, the RBF network will be found inefficient in approximating these values. Second, when the training patterns incur a large error, the network will interpolate these training patterns incorrectly. In order to cope with these problems, an RBF network is proposed in this paper which is based on sequences of sigmoidal functions and a robust objective function. The former replaces the Gaussian functions as the basis function of the network so that constant-valued functions can be approximated accurately by an RBF network, while the latter is used to restrain the influence of large errors. Compared with traditional RBF networks, the proposed network demonstrates the following advantages: (1) better capability of approximation to underlying functions; (2) faster learning speed; (3) better size of network; (4) high robustness to outliers