Application of Neural Networks for Characterization of Nano-porous Materials
Ali Ahmadpour, Akbar Shahsavand · Chemeca 2006: Knowledge and Innovation · 2006
Characterization of nano-porous materials is an attractive topic in the applied research studies. Efficient techniques are required to predict the proper values for nano-material characterization parameters. A novel method is introduced in the present article based on a special class of neural network known as Regularization network. A reliable procedure is presented for efficient training of the optimal network using two experimental data sets on activated carbon and carbon molecular sieve (CMS) characterization. These case studies were employed to compare the performances of two properly trained Regularization networks with conventional methods. It is also demonstrated that such Regularization networks provide more appropriate generalization performance over the conventional techniques.