2016
DOI: 10.1007/s00521-016-2362-0
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Neural network-based fuzzy inference system for speed control of heavy duty vehicles with electronic throttle control system

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Cited by 26 publications
(15 citation statements)
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“…The ANFIS model is also applicable in the case of hybrid drives where it minimizes engine fuel consumption with internal combustion and maximizes torque (Mohebbi et al, 2005). Eski & Yıldırım (2017) describe the use of ANFIS model for the electronic regulation of throttle of heavy vehicles. Car parking is a demanding action, sometimes for experienced drivers, and if it is a truck with a trailer, the problem becomes very complex.…”
Section: Vehicle Steering and Controlmentioning
confidence: 99%
“…The ANFIS model is also applicable in the case of hybrid drives where it minimizes engine fuel consumption with internal combustion and maximizes torque (Mohebbi et al, 2005). Eski & Yıldırım (2017) describe the use of ANFIS model for the electronic regulation of throttle of heavy vehicles. Car parking is a demanding action, sometimes for experienced drivers, and if it is a truck with a trailer, the problem becomes very complex.…”
Section: Vehicle Steering and Controlmentioning
confidence: 99%
“…[18] firstly introduced the fuzzy neural network to predict delayed time for process control. Then as presented in [19], an adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy inference system, was deployed to control the speed of a heavy duty vehicle. Not only for control system, but also for mobile learning system, [20,21] incorporated ANFIS as a reasoning engine to deliver learning content for mobile learning system.…”
Section: Fuzzy Logic and Evolving Fuzzy Neural Network (Efunn)mentioning
confidence: 99%
“…Eski and Yildirim (2017) made a performance comparison among four controllers such as PID controller, model-based neural network controller, adaptive neural network-based fuzzy inference controller and robust adaptive neural-based fuzzy inference controller in terms of transient characteristics of position-controlled electronic throttle valve system. The simulated results showed that robust adaptive neural-based fuzzy inference controller outperforms the other suggested controllers [20]. Wang et al (2016) proposed a robust adaptive scheme for position control of electronic throttle (ET) valve.…”
Section: Introductionmentioning
confidence: 99%