Modelling of Shre Drag Tilt Velocimeter (DTV) with Curvilinear, Gompertz and Artificial Neural Network Method
Keywords:Drag Tilt, Modelling, Velocimeter
AbstractDifferent method of modelling presented in this paper on Shre Drag Tilt Velocimeter non-linear data. The idea of different non-linear modelling method is to know which makes more possible to describe more accurate on interacting effects between velocities and tilt angle when compared among modellers. The models, which were used are static analytic approximation model, curvilinear bivariate regression model, Gompertz the classical growth model and Artificial Neural Network (ANN) model. Accuracy of the models was determined by mean square error (MSE), mean absolute deviation (MAD), bias and R Square. The datasets gathered from an experiment of Shre DTV at flume were divided into training data and testing data for the purpose of developing and validating all type of models. The difference between the model and the observed value become the forecasting error measurements. For the training data, the lowest MSE, RMSE and better R Square were noted for the Gompertz model. But ANN generalized better on testing data by obtaining lowest MSE, RMSE and higher R Square among others. ANN generalization result is 88.60%, Gompertz is 54.89%, curvilinear is 69.28% and static analytic is -1.29%. Lower bias was also for the neural network test data. As demonstrated by the bias values, only curvilinear model presenting overestimation model while other models produce little or no overestimation of the observed tilt response. Interpretations of the parameters estimation on Gompertz model have been attempted previously. However, focusing on the ability of Shre DTV to predict responses may be more practical than the relevance of parameter estimates.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.