Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge

Çakmakçı, Mehmet

doi:10.1007/s00449-007-0131-2

Cited by 82 publications

(45 citation statements)

References 29 publications

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“…The input of the data included the input data and output data in the form of a data array (Chen et al 2010(Chen et al : 1187. The final action at this stage involved defining and partitioning the universe of discourse for the input variables using the subtractive clustering method (Cakmakci 2007;Wei et al 2011). The next step involved is generating the fuzzy inference system (FIS) (Chen et al 2010;Efendigil et al 2009).…”

Section: Anfis Processmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

2015

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Abstract. This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia's domestic low cost carriers' demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model's training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R 2 -value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.

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Section: Anfis Processmentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

2015

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“…It was found that the prediction performances of ANFIS model is better than traditional multiple linear regression model. ANFIS has also been used in the field of science and technology by many researchers (Guler & Ubeyli, 2004; Zaheeruddin & Garima, 2006; Naadimutha et al, 2007;Cakmakci, 2007;Bakhtyar et al, 2008;Wang & Elhag, 2008;Khajeh et al, 2009;Radulovic & Rankovic, 2010;Yan et al, 2010;Ata & Kocyigit, 2010;Sargolzaei & Kianifar, 2010;Yilmaz & Kaynar, 2011;Mohammadi et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Bazzazi¹,

Esmaeili²

2012

Archives of Mining Sciences

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Adaptive neuro-fuzzy inference system (ANFIS) is powerful model in solving complex problems. Since ANFIS has the potential of solving nonlinear problem and can easily achieve the input-output mapping, it is perfect to be used for solving the predicting problem. Backbreak is one of the undesirable effects of blasting operations causing instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling. In this paper, ANFIS was applied to predict backbreak in Sangan iron mine of Iran. The performance of the model was assessed through the root mean squared error (RMSE), the variance account for (VAF) and the correlation coefficient (R 2 ) computed from the measured of backbreak and model-predicted values of the dependent variables. The RMSE, VAF, R 2 indices were calculated 0.6, 0.94 and 0.95 for ANFIS model. As results, these indices revealed that the ANFIS model has very good prediction performance.Keywords: blasting, backbreak, adaptive neuro-fuzzy inference system, Sangan iron mine Adaptywny system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej (AN-FIS) stanowi potężny narzędzie do rozwiązywania złożonych problemów. Ponieważ model ANFIS może być wykorzystywany do rozwiązywania problemów nieliniowych i umożliwia wygodne przedstawienie problemu w formie: wejście -wyjście, jest idealnym narzędziem do rozwiązywania problemów związanych z prognozowaniem. Pękanie skał w odkrywce jest jednym z niekorzystnych skutków prowadzenia prac strzałowych, powoduje niestabilność ścian, uszkodzenia maszyn i urządzeń, nieodpowiednią fragmentację skał oraz prowadzi do obniżenia efektywności wierceń. W pracy przedstawiono zastosowanie systemu ANFIS do prognozowania pękań skał w kopalni rud żelaza w Sangan (Iran). Działanie modelu zbadano na podstawie wartości błędu średniokwadratowego (RMSE), wariancji (VAF) i współczynnika korelacji Słowa kluczowe: prace strzałowe, pękanie skał, system wnioskowania wykorzystujący elementy sieci neuronowych i logiki rozmytej, kopalnia rud żelaza Sangan IntroductionAlthough the main purpose of blasting in open pit mines is rock breakage and finally facilitating in loading operations, the other effects of blasting such as ground vibration, fly rock and backbreak should be considered. Backbreak can be defined as breakage behind the last row of holes (Konya & Walter, 1991). This phenomenon may cause instability in mine walls, falling down the machinery, improper fragmentation and reduction in efficiency of drilling (Bazzazi, 2005). Several factors leading to backbreak have been described by various researchers. Konya and Walter (1991) described some of the causes for backbreak such as excessive burden and stiff benches, long stemming depth on stiff benches and improper timing delay. Gate et al. (2005) believed that a combination of factors in the blasting such as overstemming of the shot holes and short timing delays in the firing sequence may have led to the severe backbreak. Moreover, the adverse geological structure app...

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“…The normalized data are divided into 126 samples for training and 14 samples for testing of the model. A large percentage of the data set should be used during the training stage since ANFIS is quite adapted nonlinear functional dependency between the influent and effluent variables [10]. This could also result in avoiding the problem of over fitting, which led to large testing error.…”

Section: Anfis Model Implementationmentioning

confidence: 99%

Estimation of pH and MLSS using Neural Network

Yasmin¹,

Gaya²,

Wahab³

et al. 2017

TELKOMNIKA

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Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge

Cited by 82 publications

References 29 publications

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

An Adaptive Neuro-Fuzzy Inference System for Forecasting Australia’s Domestic Low Cost Carrier Passenger Demand

Prediction of Backbreak in Open Pit Blasting by Adaptive Neuro-Fuzzy Inference System / Prognozowanie Spękań Skał Przy Pracach Strzałowych W Kopalniach Odkrywkowych Przy Użyciu Metod Neuronowych I Wnioskowania Rozmytego (Anfis) Zastosowanych W Modelu Adaptywnym

Estimation of pH and MLSS using Neural Network

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