scholarly journals Improved non-linear transfer function and neural network methods of flow routing for real-time forecasting

2001 ◽  
Vol 3 (3) ◽  
pp. 153-164 ◽  
Author(s):  
D. F. Lekkas ◽  
C. E. Imrie ◽  
M. J. Lees
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Real Time ◽  
State Of The Art ◽  
Comparison Results ◽  
Network Methods ◽  
Forecasting Performance ◽  
Non Linear ◽  
Artificial Neural

Data-based methods of flow forecasting are becoming increasingly popular due to their rapid development times, minimum information requirements, and ease of real-time implementation, with transfer function and artificial neural network methods the most commonly applied methods in practice. There is much antagonism between advocates of these two approaches that is fuelled by comparison studies where a state-of-the-art example of one method is unfairly compared with an out-of-date variant of the other technique. This paper presents state-of-the-art variants of these competing methods, non-linear transfer functions and modified recurrent cascade-correlation artificial neural networks, and objectively compares their forecasting performance using a case study based on the UK River Trent. Two methods of real-time error-based updating applicable to both the transfer function and artificial neural network methods are also presented. Comparison results reveal that both methods perform equally well in this case, and that the use of an updating technique can improve forecasting performance considerably, particularly if the forecast model is poor.

scholarly journals Image Analysis of Diabetic Retinopathy Disease Based on Artificial Neural Network Algorithms

2020 ◽  
Vol 4 (2) ◽  
pp. 201-209
Author(s):  
Tri Astuti ◽  
Gesha Agus Setiawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Fundus Images ◽  
Feature Extraction Method ◽  
Comparison Results ◽  
Network Methods ◽  
Artificial Neural ◽  
F Measure

Diabetic retinopathy is a complication of diabetes in the form of damage to the retina of the eye. High levels of glucose in the blood are the cause of small capillary blood vessels to rupture and can cause blindness. The signs of this disease can only be seen using retinal fundus images. To identify diabetic retinopathy, a computerized process and analysis are needed, one of which uses artificial neural network methods to determine its performance so that it will help the doctor in analyzing the disease and diagnosing whether a patient suffering from diabetic retinopathy or not. Texture feature extraction method using Gabor filter can represent feature value information that is skewness, kurtosis, mean, entrophy, and variance to be processed at the identification stage using artificial neural network methods. The comparison results of the DIARETDB0 dataset testing with the total of 130 fundus images using the backpropagation ANN method before randomizing the data yielded an accuracy value of 82.30%, a precision value of 71.28%, a recall value of 82.30%, and an f-measure of 76.39%. Whereas after randomizing the data for 30 times, the results of accuracy value were higher than before randomizing the data, namely the accuracy value of 83.07%, the precision value of 71.39%, the recall value of 83.07% and f-measure value of 76.78%. The tests carried out included in good classification.  

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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