Another insight into artificial neural networks through behavioural analysis of access mode choice

Rao, Preeti; Sikdar, P K; Rao, K. V. Krishna; Dhingra, S. L.

doi:10.1016/s0198-9715(98)00036-2

Cited by 40 publications

(22 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we compute the elasticity following different methods that suit the non parametric framework. To our knowledge, prior publications contain only a few of such techniques, and we focus on two of these, both based on the definition of elasticity itself: SR (Subba Rao et al, 1998) and GK (Gruca and Klemz, 1998).…”

Section: Elasticitymentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Wine Demand with Artificial Neural Networks

Gerolimetto¹,

Mauracher²,

Procidano³

2008

J Wine Econ

View full text Add to dashboard Cite

In this paper we analyse wine demand in Italy with microdata. Instead of estimating a parametric model, we study the demand following a non parametric approach by means of Artificial Neural Networks. The input set includes the usual economic variables (price and income) and some sociodemographic factors that are also shown to be relevant for demand analysis. We compute price elasticities using two different nonparametric procedures. (JEL Classification: C14, C21, Q11, Q13)

show abstract

Section: Elasticitymentioning

confidence: 99%

“…To our knowledge, prior published studies contain only a few proposals to compute elasticities in a non parametric manner. In this paper, we focus on two methods developed by Subba Rao et al (1998), hereafter SR, and by Gruca and Klemz (1998), hereafter GK.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Wine Demand with Artificial Neural Networks

Gerolimetto¹,

Mauracher²,

Procidano³

2008

J Wine Econ

View full text Add to dashboard Cite

show abstract

“…Artificial neural networks have scarcely been used as a modelling approach in the analysis of crash-related injury severity. More recent applications in the transportation field using the ANN have included traffic prediction (Yin et al 2002;Zhong et al 2004), the estimation of traffic parameters (Tong, Hung 2002), traffic signal control (Zhang et al 2001), incident detection (Jin et al 2002;Yuan, Cheu 2003), travel behaviour analysis (Subba Rao et al 1998;Hensher, Ton 2000;Vythoulkas, Koutsopoulos 2003) and traffic accident analysis (Mussone et al 1996(Mussone et al , 1999Sohn, Lee 2003;Abdel-Aty, Pande 2005). For example, Abdelwahab and Abdel-Aty (2001) used artificial neural networks for modelling the relationship between driver injury severity and crash factors related to the driver, vehicle, roadway, and environmental characteristics.…”

Section: Introductionmentioning

confidence: 99%

Prediction for Traffic Accident Severity: Comparing the Artificial Neural Network, Genetic Algorithm, Combined Genetic Algorithm and Pattern Search Methods

2012

View full text Add to dashboard Cite

This paper focuses on predicting the severity of freeway traffic accidents by employing twelve accident-related parameters in a genetic algorithm (GA), pattern search and artificial neural network (ANN) modelling methods. The models were developed using the input parameters of driver's age and gender, the use of a seat belt, the type and safety of a vehicle, weather conditions, road surface, speed ratio, crash time, crash type, collision type and traffic flow. The models were constructed based on 1000 of crashes in total that occurred during 2007 on the Tehran–Ghom Freeway due to the fact that the remaining records were not suitable for this study. The GA evaluated eleven equations to obtain the best one. Then, GA and PS methods were combined using the best GA equation. The neural network used multi-layer perceptron (MLP) architecture that consisted of a multi-layer feed-forward network with hidden sigmoid and linear output neurons that could also fit multi-dimensional mapping problems arbitrarily well. The ANN was applied during training, testing and validation and had 12 inputs, 25 neurons in the hidden layers and 3 neurons in the output layer. The best-fit model was selected according to the R-value, root mean square errors (RMSE), mean absolute errors (MAE) and the sum of square error (SSE). The highest R-value was obtained for the ANN around 0.87, demonstrating that the ANN provided the best prediction. The combination of GA and PS methods allowed for various prediction rankings ranging from linear relationships to complex equations. The advantage of these models is improving themselves adding new data.

show abstract

“…By these models, process stability is analyzed and system performance is improved. But there are still two problems unsolved: accuracy of model was not satisfied for industrial application; response time of model was not fast enough for online application [2] .…”

Section: Introductionmentioning

confidence: 99%

Spatter rate estimation of GMAW-S based on partial least square regression

Cai

Wang

Yang

et al. 2008

J. Shanghai Jiaotong Univ. (Sci.)

View full text Add to dashboard Cite

This paper analyzes the drop transfer process in gas metal arc welding in short-circuit transfer mode (GMAW-S) in order to develop an optimized spatter rate model that can be used on line. According to thermodynamic characters and practical behavior, a complete arcing process is divided into three sub-processes: arc re-ignition, energy output and shorting preparation. Shorting process is then divided as drop spread, bridge sustention and bridge destabilization. Nine process variables and their distribution are analyzed based on welding experiments with high-speed photos and synchronous current and voltage signals. Method of variation coefficient is used to reflect process consistency and to design characteristic parameters. Partial least square regression (PLSR) is utilized to set up spatter rate model because of severe correlativity among the above characteristic parameters. PLSR is a new multivariate statistical analysis method, in which regression modeling, data simplification and relativity analysis are included in a single algorithm. Experiment results show that the regression equation based on PLSR is effective for on-line predicting spatter rate of its corresponding welding condition.

show abstract

Another insight into artificial neural networks through behavioural analysis of access mode choice

Cited by 40 publications

References 3 publications

Analyzing Wine Demand with Artificial Neural Networks

Analyzing Wine Demand with Artificial Neural Networks

Prediction for Traffic Accident Severity: Comparing the Artificial Neural Network, Genetic Algorithm, Combined Genetic Algorithm and Pattern Search Methods

Spatter rate estimation of GMAW-S based on partial least square regression

Contact Info

Product

Resources

About