Discrete Optimization with Fuzzy Constraints

Jelušič, Primož; Žlender, Bojan

doi:10.3390/sym9060087

Cited by 9 publications

(2 citation statements)

References 15 publications

(11 reference statements)

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“…However, the genetic algorithm (GA) is utilized in order to increase the convergence rate and to avoid getting trapped in local minima. In the recent past, several methods [12], [13] and algorithms have been applied to the optimization of engineering problems [14]- [19]. However in this paper the genetic algorithm is used to obtain the best prediction model for NOx emissions.…”

Section: Optimization Methodsmentioning

confidence: 99%

Anfis Prediction and Modelling of Harmful Effects on the Environment Produced by Vehicles Crossing Road Bumps

Toplak

Sever

2018

WIT Transactions on the Built Environment

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Modelling and predictions of road traffic emissions and ground-borne vibrations caused by vehicles driving over different types of road bumps has become a rapidly developing research area. However, due to the lack of effective methods and indicators that would define traffic behavior, the accuracy of emissions and vibrations with such an approach is often not at the appropriate level. This paper presents the attempt to model and predict harmful road traffic emissions and vibrations using microsimulation traffic tools and emission models on one side and hybrid optimized models based on artificial neural networks and neuro-fuzzy techniques on the other. The optimized ANFIS-based model was developed to predict harmful road traffic emissions. Basic data were obtained with two programs: VISSIM for micro simulation of traffic flow and ENVIVER to calculate the corresponding emissions at vehicle level. The predicted NOx values indicate a significant increase of emissions by vehicles driving over road bumps, which is almost 100% higher than normal driving. The evaluation and prediction of ground-borne vibrations focused on heavy-duty vehicles, which usually cause perceptible levels of ground-borne vibrations. The prognosis of ground-borne vibration was made with local measurements and empirical vibration predictors. The predicted vibration levels caused by heavy-duty vehicles driving over road bumps are up to 24 times higher than normal vehicle driving (sinusoidal bump). Research shows that setting up speed bumps in different locations in urban areas has to be carefully examined because the level of harmful emissions and ground-borne vibrations increase.

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Section: Optimization Methodsmentioning

confidence: 99%

Anfis Prediction and Modelling of Harmful Effects on the Environment Produced by Vehicles Crossing Road Bumps

Toplak

Sever

2018

WIT Transactions on the Built Environment

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“…In the recent past, various Figure 1: ANFIS architecture for two-rule two-input sugeno model. methods [6], [7] and algorithms have been applied to the optimization of structures [8]- [13]. However in this paper the optimization algorithms are used to obtain the best prediction model for traffic induced vibration.…”

Section: Genetic Algorithm and Particle Swarm Optimizationmentioning

confidence: 99%

A New Approach for the Prediction of Traffic-Induced Ground Vibration Using a Hybrid Optimized Anfis-Based Model

Lubej

Ivanič

2018

WIT Transactions on the Built Environment

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An attempt has been made to evaluate and predict the traffic-induced ground vibration using a hybrid optimized ANFIS-based model. Towards this aim, ground vibrations caused by traffic were monitored on a building located near the road. To investigate the appropriateness of this approach, the prediction by ANFIS was also compared with the most widely used vibration predictors. In this research, a hybrid of adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithm (GA) was proposed to predict traffic-produced ground vibration. The performance criterion selected for the comparison between the actual and the predicted data were the sum of squares due to error (SSE), the root mean square error (RMSE), and goodness of fit (R-square, adjusted R-square). It turns out that the hybrid GA-ANFIS prediction model outperforms the commonly used predictors and conventional ANFIS.

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Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

2020

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Efforts were made to predict and evaluate blast-induced ground vibrations and frequencies using an adaptive network-based fuzzy inference system (ANFIS), which has a fast-learning capability and the ability to capture the non-linear response during the blasting process. For this purpose, the ground vibrations generated by the blast in a tunnel tube were monitored at a residential building located directly above the tunnel tube. To investigate the usefulness of this approach, the prediction by the ANFIS was also compared to those by three of the most commonly used vibration predictors. The efficiency criteria chosen for the comparison between the predicted and actual data were the sum of squares due to error (SSE), the root mean squared error (RMSE), and the goodness of fit (R-squared and adjusted R-squared). The results show that the ANFIS prediction model performs better than the commonly used predictors.

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Discrete Optimization with Fuzzy Constraints

Cited by 9 publications

References 15 publications

Anfis Prediction and Modelling of Harmful Effects on the Environment Produced by Vehicles Crossing Road Bumps

Anfis Prediction and Modelling of Harmful Effects on the Environment Produced by Vehicles Crossing Road Bumps

A New Approach for the Prediction of Traffic-Induced Ground Vibration Using a Hybrid Optimized Anfis-Based Model

Prediction of Blast-Induced Ground Vibration Using an Adaptive Network-Based Fuzzy Inference System

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