Global Approximations to Cost and Production Functions using Artificial Neural Networks

Tsionas, Efthymios G.; Michaelides, Panayotis G.; Vouldis, Angelos T.

doi:10.2991/ijcis.2009.2.2.4

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…As a commonly used nonlinear function approximation tool, artificial neural network has shown great advantages in forecasting, pattern identification, optimization techniques, and signal processing for its nonlinear, flexible, and valid self-organization properties. A variety of problem areas are modeled using ANN [19][20][21] and in many instances, ANN has provided superior results compared to the conventional modeling techniques.…”

Section: Artificial Neural Network Artificial Neural Networkmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

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The neural network may learn and incorporate the uncertainties to predict the driver’s lane-changing behavior more accurately. In this paper, we will discuss in detail the effectiveness of Back-Propagation (BP) neural network for prediction of lane-changing trajectory based on the past vehicle data and compare the results between BP neural network model and Elman Network model in terms of the training time and accuracy. Driving simulator data and NGSIM data were processed by a smooth method and then used to validate the availability of the model. The test results indicate that BP neural network might be an accurate prediction of driver’s lane-changing behavior in urban traffic flow. The objective of this paper is to show the usefulness of BP neural network in prediction of lane-changing process and confirm that the vehicle trajectory is influenced previously by the collected data.

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Section: Artificial Neural Network Artificial Neural Networkmentioning

confidence: 99%

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Ding

Wang

et al. 2013

Mathematical Problems in Engineering

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“…ANNs have been often used to approximate nonlinear functions, exhibiting significant benefits for prediction, signal processing, optimization, and pattern identification purposes, in light of their valid and flexible nonlinear self-organization characteristics. ANNs are employed to model a wide range of problems [73][74][75] and have yielded more significant outcomes than conventional models in several cases. e present work adopted a typical feed-forward BP NN that had sigmoid hidden neurons along with linear output neurons, as shown in Figure 3.…”

Section: Nn Modelmentioning

confidence: 99%

Lane‐Changing Trajectory Prediction Modeling Using Neural Networks

Hamedi

Shad

2022

Advances in Civil Engineering

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Concerning autonomous driving, lane-changing (LC) is essential, particularly within complicated dynamic settings. It is a challenging task to model LC since driving behavior is complicated and uncertain. The present study adopts a dual-layer feed-forward backpropagation neural network involving sigmoid hidden neurons and linear output neurons for evaluating intrinsic LC complexity. Furthermore, the estimation and validation of the model were performed by large-scale trajectory data. Empirical LC data were obtained from the Next Generation Simulation (NGSIM) project for training and testing the neural network-based LC model. The findings revealed that the introduced model could make precise LC predictions of vehicles under small trajectory errors and satisfactory accuracy. The present work assessed LC beginning/endpoints and velocity estimates by analyzing the vehicles around. It was observed that the neural network model yielded almost the same predictions as the observational LC trajectories as well as following vehicle trajectories on the original and target lanes. Furthermore, for LC behavior characteristic validation, the neural network-produced LC gap distributions underwent comparisons to real-life data, demonstrating the characteristics of LC gap distributions not to differ from the real-life LC behavior substantially. Eventually, the introduced neural network-based LC model was compared to a support vector regression-based LC model. It was found that the trajectory predictions of both models were adequately consistent with the observational data and could capture both lateral and longitudinal vehicle movements. In turn, this demonstrates that the neural network and support vector regression models had satisfactory performance. Also, the proposed models were evaluated using new inputs such as speed, gap, and position of the subject vehicle. The analysis findings indicated that the performance of the proposed NN and SVR models was higher than the model with new inputs.

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“…A variety of problem areas are modeled using ANN [35][36][37] and, in many instances, ANN has provided superior results compared to the conventional modeling techniques. 38 It is published by several researchers that ANN performs excellently on pattern recognition tasks and its potential advantages have been addressed in the literature.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

Kaya

Aktaş

Topçu

et al. 2010

IJCIS

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The purpose of this study is to compare the performances of Artificial Neural Networks (ANN) and Multinomial Probit (MNP) approaches in modeling the choice decision within fast moving consumer goods sector. To do this, based on 2597 toothpaste purchases of a panel sample of 404 households, choice models are built and their performances are compared on the 861 purchases of a test sample of 135 households. Results show that ANN's predictions are better while MNP is useful in providing marketing insight.

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Global Approximations to Cost and Production Functions using Artificial Neural Networks

Cited by 3 publications

References 4 publications

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

A Neural Network Model for Driver’s Lane-Changing Trajectory Prediction in Urban Traffic Flow

Lane‐Changing Trajectory Prediction Modeling Using Neural Networks

Brand Choice Modeling Modeling Toothpaste Brand Choice: An Empirical Comparison of Artificial Neural Networks and Multinomial Probit Model

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