scholarly journals The effect of lane blockage on signalised intersection performance -analysing and modelling

2020 ◽  
Vol 62 (3) ◽  
Author(s):  
E Dogan ◽  
E Korkmaz ◽  
A P Akgüngör
Keyword(s):  
Performance Analysis ◽  
Network Models ◽  
Performance Criteria ◽  
Neural Network Models ◽  
The Road ◽  
Ann Models ◽  
On The Road ◽  
And Performance ◽  
Artificial Neural Network Models ◽  
Signalised Intersection

Unexpected stops or entry/exit manoeuvres of vehicles on the road may cause the related lane to become blocked. When this blocking happens in a signalised intersection zone, it also affects intersection performance. Determining the extent of this effect will assist traffic engineers with intersection design and performance analysis. In this study, the effects of Lane Blockage (LB) on intersection performance under various traffic conditions were analysed according to two performance criteria. ANN (Artificial Neural Network) models were also developed to enable the prediction of intersection performance. As a result of the analysis, it was clearly determined that the effect of LB on intersection performance was limited at v/c <0.5. However, it was determined that the intersection performance may decrease between 10% and 110% under the condition of 0.5 < v/c, depending on the LB frequency and duration. Additionally, the developed ANN models have R > 0.95 and will therefore be useful in LB-related intersection performance analysis.

scholarly journals A Review of the Artificial Neural Network Models for Water Quality Prediction

2020 ◽  
Vol 10 (17) ◽  
pp. 5776 ◽  
Author(s):  
Yingyi Chen ◽  
Lihua Song ◽  
Yeqi Liu ◽  
Ling Yang ◽  
Daoliang Li
Keyword(s):  
Water Quality ◽  
Wastewater Treatment Plants ◽  
Rapid Development ◽  
Network Models ◽  
Quality Prediction ◽  
Neural Network Models ◽  
Water Quality Prediction ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

scholarly journals Machine Learning ANN Models for Predicting Sensory Quality of Roasted Coffee Flavoured Sterilized Drink

2013 ◽  
Vol 2 (3) ◽  
pp. 09-13
Author(s):  
Sumit Goyal ◽  
Gyanendra Kumar Goyal
Keyword(s):  
Sensory Quality ◽  
Single Layer ◽  
Network Models ◽  
Prediction Performance ◽  
Coefficient Of Determination ◽  
Roasted Coffee ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Models

This paper highlights the significance of feedforward artificial neural network models for predicting shelf life of roasted coffee falvoured sterilized drink. Coffee is one of the most important products for trade in international market. Single as well as multilayer models were explored and different backpropagation algorithms were investigated, Root mean square error and coefficient of determination R2 were used to compare the prediction performance of single and multilayer feedforward ANN models. Experimental results suggested that multilayer models take less time and give better results as compared to single layer ANN models for prediction of sensory quality of roasted coffee falvoured sterilized drink..

scholarly journals Investigation of weight initialization using Fibonacci Sequence on the performance of neural networks*

2021 ◽  
Author(s):  
Dipanwita Sinha Mukherjee ◽  
Naveen Yeri
Keyword(s):  
Neural Network ◽  
Heuristic Method ◽  
Fibonacci Sequence ◽  
Network Models ◽  
Test Accuracy ◽  
Neural Network Models ◽  
Weight Initialization ◽  
And Performance ◽  
Artificial Neural Network Models ◽  
Better Than

<div>Initializing weights are important for fast convergence and performance improvement of Artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques.</div>

scholarly journals Investigation of weight initialization using Fibonacci Sequence on the performance of neural networks*

2021 ◽  
Author(s):  
Dipanwita Sinha Mukherjee ◽  
Naveen Yeri
Keyword(s):  
Neural Network ◽  
Heuristic Method ◽  
Fibonacci Sequence ◽  
Network Models ◽  
Test Accuracy ◽  
Neural Network Models ◽  
Weight Initialization ◽  
And Performance ◽  
Artificial Neural Network Models ◽  
Better Than

<div>Initializing weights are important for fast convergence and performance improvement of Artificial Neural Network models. This study proposes a heuristic method to initialize weights for Neural Network with Fibonacci sequence. Experiments have been carried out with different network structures and datasets and results have been compared with other initialization techniques such as Zero, Random, Xavier and He. It has been observed that for small sized datasets, Fibonacci initialization technique reports 94% of test accuracy which is better than Random (85%) and close to Xavier (93%) and He (96%) initialization methods. Also, for medium sized dataset, we have noted that performance of Fibonacci weight initialization method is comparable with the same for Random, Xavier and He initialization techniques.</div>

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

Sign in / Sign up

Export Citation Format

Share Document