Wavelet neural networks: A practical guide

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 t...

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Bibliographic Details
Published in:Neural networks Vol. 42; pp. 1 - 27
Main Authors: Alexandridis, Antonios K., Zapranis, Achilleas D.
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.06.2013
Elsevier
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Brain - physiology
Breast Neoplasms - pathology
Breast Neoplasms - physiopathology
Computer science; control theory; systems
Computer Simulation
Confidence Intervals
Connectionism. Neural networks
Exact sciences and technology
Feedback, Physiological
Female
Humans
Inference from stochastic processes; time series analysis
Mathematics
Model identification
Model selection
Models, Neurological
Nerve Net - physiology
Neural Networks (Computer)
Nonlinear Dynamics
Prediction intervals
Predictive Value of Tests
Probability and statistics
Sciences and techniques of general use
Statistics
Temperature
Variable selection
Wavelet Analysis
Wavelet networks
Wind
Confidence intervals
Prediction intervals
Model selection
Wavelet networks
Model identification
Variable selection
Chaos
Initialization
Differential equation
Time series
Neural network
Statistical forecasting
Delay equation
Complexity
Confidence interval
Model matching
Wavelet transformation
Statistical model
Classification
System identification
ISSN:0893-6080, 1879-2782, 1879-2782
Online Access:Get full text
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Description
Summary: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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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2013.01.008