Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms

Zhang, J.; Chen, R.H.; Chen, Yongzheng; Tian, Wenxi; Su, G.H.; Qiu, Suizheng

doi:10.1016/j.nucengdes.2017.09.027

Cited by 42 publications

(10 citation statements)

References 31 publications

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“…Now values of the optimization algorithm such as selection, crossover, and mutation rates can be set to design the algorithm. The fitness function is the reciprocal of the quadratic sum of the difference between predicted and real values [135]. Roulette wheel selection is used to select a new individual, then two chromosomes are exchanged via crossover operation to generate a new individual.…”

Section: Genetic Neural Networkmentioning

confidence: 99%

“…Equipping an ANN with a GA could save training time and improve the precision of the forecasting model [135].…”

Section: Genetic Neural Networkmentioning

confidence: 99%

“…Equipping an ANN with a GA could save training time and improve the precision of the forecasting model [135]. The next section is dedicated to present the architecture of ANN, which is the basic framework of the developed forecasting model.…”

Section: Genetic Neural Networkmentioning

confidence: 99%

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Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

show abstract

Section: Genetic Neural Networkmentioning

confidence: 99%

“…Equipping an ANN with a GA could save training time and improve the precision of the forecasting model [135].…”

Section: Genetic Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Now values of the optimization algorithm such as selection, crossover, and mutation rates can be set to design the algorithm. The fitness function is the reciprocal of the quadratic sum of the difference between predicted and real values [109]. Roulette wheel selection is used to select a new individual, then two chromosomes are exchanged via crossover operation to generate a new individual.…”

Section: Genetic Neural Networkmentioning

confidence: 99%

“…Equipping an ANN with a GA could save training time and improve the precision of the forecasting model [109]. Next section is dedicated to present the architecture of ANN, which is the basic framework of the developed forecasting model.…”

Section: Genetic Neural Networkmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Hafezi¹,

Akhavan²,

Zamani³

et al. 2019

Preprint

View full text Add to dashboard Cite

Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

show abstract

Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches

Sutcubasi

Metin

Ergüzel

et al. 2019

Neural Comput & Applic

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Prediction of LBB leakage for various conditions by genetic neural network and genetic algorithms

Cited by 42 publications

References 31 publications

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Anatomical connectivity changes in bipolar disorder and schizophrenia investigated using whole-brain tract-based spatial statistics and machine learning approaches

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