Neural Models for Predicting Hole Diameters in Drilling Processes

Spiralling in BTA Deep-hole Drilling: Models of Varying Frequencies

application of affine gray-box neural models for nonlinear control of chemical processes

Adaptive Neural Fuzzy Inference System Models for Predicting the Shear Strength of Reinforced Concrete Deep Beams

A reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. In this study, a new approach based on the Adaptive Neural Fuzzy Inference System (ANFIS) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical a...

Artificial neural network models for production of nano-grained structure in AISI 304L stainless steel by predicting thermo-mechanical parameters

An artificial neural network (ANN) model is developed for the analysis, simulation, and prediction of the austenite reversion in the thermo-mechanical treatment of 304L austenitic stainless steel. The results of the ANN model are in good agreement with the experimental data. The model is used to predict an appropriate annealing condition for austenite reversion through the martensite to austeni...

Diameters in preferential attachment models

In this paper, we investigate the diameter in preferential attachment (PA-) models, thus quantifying the statement that these models are small worlds. The models studied here are such that edges are attached to older vertices proportional to the degree plus a constant, i.e., we consider affine PA-models. There is a substantial amount of literature proving that, quite generally, PA-graphs posses...