Learning and Approximation of Chaotic Time Series Using Wavelet-Networks
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Function approximation using artificial neural networks
MATH'07 Proceedings of the 12th WSEAS International Conference on Applied Mathematics
Function approximation using artificial neural networks
WSEAS Transactions on Mathematics
An Adaptive Wavelet Networks Algorithm for Prediction of Gas Delay Outburst
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
A novel chaotic neural network with the ability to characterize local features and its application
IEEE Transactions on Neural Networks
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A novel objective function is presented that incorporates both local and global errors as well as model parsimony in the construction of wavelet neural networks. Two methods are presented to assist in the minimization of this objective function, especially the local error term. First, during network initialization, a locally adaptive grid is utilized to include candidate wavelet basis functions whose local support addresses the local error of the local feature set. This set can be either user-defined or determined using information derived from the wavelet transform modulus maxima representation. Next, during the network construction, a new selection procedure based on a subspace projection operator is presented to help focus the selection of wavelet basis functions to reduce the local error. Simulation results demonstrate the effectiveness of these methodologies in minimizing local and global error while maintaining model parsimony and incurring a minimal increase on computational complexity