title: Wavelet neural networks: a practical guide.
abstract: Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of applications. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thoroughly examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.
authors: University of Kent, School of Mathematics, Statistics and Actuarial Science, Cornwallis Building, P.O. CT2 7NF, Canterbury, UK. A.Alexandridis@kent.ac.uk
authors: Antonios K
authors: AK
authors: Alexandridis
authors: Achilleas D
authors: AD
authors: Zapranis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D001943:Breast Neoplasms / Q000473:pathology / Q000503:physiopathology; D003198:Computer Simulation; D016001:Confidence Intervals; D025461:Feedback, Physiological; D005260:Female; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D013696:Temperature; D058067:Wavelet Analysis; D014919:Wind