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Proceedings Paper

Application of a radial basis function neural network to sensor design
Author(s): Ryszard M. Lec; Mohamad T. Musavi; H. P. Pendse; Wahid Ahmed
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Paper Abstract

One of the important tasks in sensor design is the development of a model for a sensing phenomena. Artificial neural networks are ideal for such a task because of their capability for representation of the mapping functions describing the processes and phenomena which are mathematically difficult or even intractable. We examined a radial basis function (RBF) neural network for modeling of acoustical properties of colloidal TiO2 slurry. The colloidal slurry is a very complex multiphase medium. The RBF network with a set of local Gaussian functions was trained using the data from the earlier developed physical model of TiO2 slurry. Next the TiO2 neural model was used for a prediction of the TiO2 particle size distribution. The resulting prediction accuracies of the RBF network were 99.8% for the data used in the training process and 88% for the data not used in the training. Compared to other available techniques neural networks can offer an effective and time efficient approach for the modeling of complex materials.

Paper Details

Date Published: 12 July 1993
PDF: 9 pages
Proc. SPIE 1918, Smart Structures and Materials 1993: Smart Sensing, Processing, and Instrumentation, (12 July 1993); doi: 10.1117/12.148003
Show Author Affiliations
Ryszard M. Lec, Univ. of Maine (United States)
Mohamad T. Musavi, Univ. of Maine (United States)
H. P. Pendse, Univ. of Maine (United States)
Wahid Ahmed, Univ. of Maine (United States)


Published in SPIE Proceedings Vol. 1918:
Smart Structures and Materials 1993: Smart Sensing, Processing, and Instrumentation
Richard O. Claus, Editor(s)

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