Using GMDH Neural Networks to Model the Power and Torque of a Stirling Engine

Ahmadi, Mohammad Hossein; Ahmadi, Mohammad Ali; Mehrpooya, Mehdi; Rosen, Marc A.

doi:10.3390/su7022243

Cited by 81 publications

(36 citation statements)

References 41 publications

(59 reference statements)

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“…In recent years, ANNs have been successfully utilized in a variety of applications as one of the most current artificial intelligence techniques [47,48]. In numerous neural network architectures, the most prevalent training method is the feed-forward neural network with back propagation (BP) training algorithm [27].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

et al. 2016

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Abstract:Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid rk, B t s-ABBP (back propagation based on adaptive strategy with parameters k and B t ) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the rk, B t s-ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

et al. 2016

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“…The GMDH algorithm has been widely used in various fields, e.g. nuclear power plants [25], Stirling engine design [39], education [40]. This study investigated the relationship between seven physiological indices and work performance on different levels of task complexity.…”

Section: Group Methods Of Data Handlingmentioning

confidence: 99%

Predicting Driver’s Work Performance in Driving Simulator Based on Physiological Indices

Tran

Habiyaremye

et al. 2017

Intelligent Human Computer Interaction

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Abstract.Developing an early warning model based on mental workload (MWL) to predict the driver's performance is critical and helpful, especially for new or less experienced drivers. This study aims to investigate the correlation between human's MWL and work performance and develop the predictive model in the driving task using driving simulator. The performance measure (number of errors), subjective rating (NASA Task Load Index) as well as six physiological indices were assessed and measured. Additionally, the group method of data handling (GMDH) was used to establish the work performance model. The results indicate that different complexity levels of driving task have a significant effect on the driver's performance, and the predictive performance model integrates different physiological measures shows the validity of the proposed model is well with R 2 = 0.781. The proposed model is expected to provide a reference value of their work performance by giving physiological indices. Based on this model, the driving lesson plans will be proposed to sustain the appropriate MWL as well as improve work performance.

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Section: Group Methods Of Data Handlingmentioning

confidence: 99%