Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Journal of Global Optimization
Fault Diagnosis Using Wavelet Neural Networks
Neural Processing Letters
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Nonlinear Adaptive Wavelet Control Using Constructive Wavelet Networks
IEEE Transactions on Neural Networks
Wavelet Basis Function Neural Networks for Sequential Learning
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Efficient Object Recognition Using Boundary Representation and Wavelet Neural Network
IEEE Transactions on Neural Networks
Voting based extreme learning machine
Information Sciences: an International Journal
Short-term wind power prediction based on wavelet decomposition and extreme learning machine
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis
Neural Processing Letters
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In this paper, we introduce a new learning method for composite function wavelet neural networks (CFWNN) by combining the differential evolution (DE) algorithm with extreme learning machine (ELM), in short, as CWN-E-ELM. The recently proposed CFWNN trained with ELM (CFWNN-ELM) has several promising features. But the CFWNN-ELM may have some redundant nodes due to the number of hidden nodes assigned a priori and the input weight matrix and the hidden node parameter vector randomly generated once and never changed during the learning phase. The introduction of DE into CFWNN-ELM is to search for the optimal network parameters and to reduce the number of hidden nodes used in the network. Simulations on several artificial function approximations, real-world data regressions and a chaotic signal prediction problem show some advantages of the proposed CWN-E-ELM. Compared with CFWNN-ELM, CWN-E-ELM has a much more compact network size and Compared with several relevant methods, CWN-E-ELM is able to achieve a better generalization performance.