Neural network approach to sensor design

A radial basis function (RBF) neural network is examined 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 is trained using the data from a previously developed physical model of TiO₂ slurry. The TiO₂ neural model is used for a prediction of the TiO₂ particle size distribution. The resulting prediction accuracies of the RBF network is 99.8% of 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