Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Focused local learning with wavelet neural networks
IEEE Transactions on Neural Networks
Non uniform noisy data training using wavelet neural network based on sampling theory
WSEAS TRANSACTIONS on SYSTEMS
The relationship of sample size and accuracy in radial basis function networks
WSEAS Transactions on Computers
Empirical determination of sample sizes for multi-layer perceptrons by simple RBF networks
WSEAS Transactions on Computers
WSEAS Transactions on Mathematics
The effect of training set size for the performance of neural networks of classification
WSEAS Transactions on Computers
Fourier-assisted machine learning of hard disk drive access time models
PDSW '13 Proceedings of the 8th Parallel Data Storage Workshop
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Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial basis function network and the wavelet neural network are applied in estimating periodic, exponential and piecewise continuous functions. Different types of basis functions are used as the activation function in the hidden nodes of the radial basis function network and the wavelet neural network. The performance is compared by using the normalized square root mean square error function as the indicator of the accuracy of these neural network models.