This paper suggests a novel methodology for building robust information processing systems based on wavelets and artificial neural networks (ANN) to be applied either in decision‐making tasks based on image information or in signal prediction and modeling tasks. The efficiency of such systems is increased when they simultaneously use input information in its original and wavelet transformed form, invoking ANN technology to fuse the two different types of input. A quality control decision‐making system as well as a signal prediction system have been developed to illustrate the validity of our approach. The first one offers a solution to the problem of defect recognition for quality control systems. The second application improves the quality of time series prediction and signal modeling in the domain of NMR. The accuracy obtained shows that the proposed methodology deserves the attention of designers of effective information processing systems.
Karras, D., Karkanis, S. and Mertzios, B. (1998), "Information systems based on neural network and wavelet methods with application to decision making, modeling and prediction tasks", Kybernetes, Vol. 27 No. 3, pp. 224-236. https://doi.org/10.1108/03684929810209405Download as .RIS
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