Application of a radial basis function neural network to sensor design

Ryszard M. Lec; Mohamad T. Musavi; H. P. Pendse; Wahid Ahmed

doi:10.1117/12.148003

12 July 1993 Application of a radial basis function neural network to sensor design

Ryszard M. Lec, Mohamad T. Musavi, H. P. Pendse, Wahid Ahmed

Proceedings Volume 1918, Smart Structures and Materials 1993: Smart Sensing, Processing, and Instrumentation; (1993) https://doi.org/10.1117/12.148003
Event: 1993 North American Conference on Smart Structures and Materials, 1993, Albuquerque, NM, United States

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.

Citation Download Citation

Ryszard M. Lec, Mohamad T. Musavi, H. P. Pendse, and Wahid Ahmed "Application of a radial basis function neural network to sensor design", Proc. SPIE 1918, Smart Structures and Materials 1993: Smart Sensing, Processing, and Instrumentation, (12 July 1993); https://doi.org/10.1117/12.148003

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