The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Wavelet neural networks: A practical guide
Open accessAbstract
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.
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Kanarachos, S., Christopoulos, S.-R. G., Chroneos, A., & Fitzpatrick, M. E. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.04.028 · Cited by 120
Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy
Putcha, A., Nguyen, T., Smith, R., Choffin, R., & Bai, W. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202200351 · Cited by 10
Metadata
- Authors
- Alexandridis, A. K., & Zapranis, A. D.
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 42
- Pages
- 1-27
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W2006447203
- Cited by
- 277
- References
- 49
- FWCI
- 19.88
- Type
- journal article
- Language
- en
- Open access
- green
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