Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications, Agualva-Cacem, Portekiz, 11 - 12 Ağustos 2011, ss.3202-3207
MODELLING OF THE Effect of Concentration and Temperature on Reaction
Time BY MEANS OF ARTIFICIAL NEURAL NETWORKS
Hakan Saricayir(1) ,
Muhammet Unal(2), Serhad Sadi Barutcuoglu(1), Musa Uce(1)
(1) Marmara University Ataturk Faculty of Education Chemistry Education
Department
(2) Marmara University Faculty of Technical Education Electronics and Computer
Education Department
ABSTRACT
An artificial neural network model was developed
to predict reaction time. The concentrations of the KIO3 solution and
temperature were employed as input to the network; the output of the network
was reaction time. The data used this study were collected through our
experiments. The multilayer feed-forward networks were trained and tested by 77
and 11 sets of input-output patterns using a back-propagation algorithm. A two-layered
network with 20 neurons in the hidden layer gave optimal results. The model
gave good predictions of reaction time correlation coefficient (R= 0.94296).
With this modeling, reaction time can be accurately predicted in various
concentration and temperature values.
MODELLING OF THE Effect of Concentration and Temperature on Reaction
Time BY MEANS OF ARTIFICIAL NEURAL NETWORKS
Hakan Saricayir(1) ,
Muhammet Unal(2), Serhad Sadi Barutcuoglu(1), Musa Uce(1)
(1) Marmara University Ataturk Faculty of Education Chemistry Education
Department
(2) Marmara University Faculty of Technical Education Electronics and Computer
Education Department
ABSTRACT
An artificial neural network model was developed
to predict reaction time. The concentrations of the KIO3 solution and
temperature were employed as input to the network; the output of the network
was reaction time. The data used this study were collected through our
experiments. The multilayer feed-forward networks were trained and tested by 77
and 11 sets of input-output patterns using a back-propagation algorithm. A two-layered
network with 20 neurons in the hidden layer gave optimal results. The model
gave good predictions of reaction time correlation coefficient (R= 0.94296).
With this modeling, reaction time can be accurately predicted in various
concentration and temperature values.