Subject Integration and Applications of Neural Networks
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
Study on application server aging prediction based on wavelet network with hybrid genetic algorithm
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
Wavelet neural network algorithms with applications in approximation signals
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Wavelet neural networks: A practical guide
Neural Networks
| Hi-index | 0.00 |
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 approach deteriorates when outliers are present. In this paper, we propose a robust wavelet neural network based on the theory of robust regression for dealing with outliers in the framework of function approximation. By adaptively adjusting the number of training data involved during training, the efficiency loss in the presence of Gaussian noise is accommodated. Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed network.