A modeling approach for iron concentration in sand filtration effluent using adaptive neuro-fuzzy model

Çakmakçı, Mehmet | Kınacı, Cumali | Bayramoğlu, Mahmut | Yıldırım, Yılmaz

Article | 2010 | Expert Systems with Applications37 ( 2 ) , pp.1369 - 1373

Effluent iron concentration is an important water quality criterion used for the assessment of the performance of rapid sand filters, in addition to other criteria. This study deals with the prediction of effluent iron concentrations by adaptive neuro-fuzzy (ANFIS) model with input parameters including filter hydraulic loading rate, influent iron concentration, bed porosity and operation time. With trying various types of membership functions, two rule base generation methods, namely subtractive clustering and grid partition were used for a first order Sugeno type inference system. Models were evaluated using root mean squared error . . . (RMSE), index of agreement (IA) and R2 as statistical performance parameters. The fit between experimental results and model outputs showed good agreement for tap water and deionized water; testing RMSE values were 36.33 and 7.66 µg/L, the IA values were 0.996 and 0.971, and R2 values were 0.99 and 0.89, respectively. It was concluded that neuro-fuzzy modeling may be successfully used to predict effluent iron concentration in sand filtration. © 2009 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Optimization of digital holographic setup by a fuzzy logic prediction system

Kaya, Gülhan Ustabaş | Erkaymaz, Okan | Saraç, Zehra

Article | 2016 | Expert Systems with Applications56 , pp.177 - 185

In this study, the optimization of the digital holography setup is achieved by a using fuzzy logic prediction system. In fact, when this optimization process is experimentally performed, some parameters are changed in the setup. These parameters affect directly the obtained image quality after a reconstruction process, which is determined by normalized root mean square. The aim of this study is to achieve the optimization of digital holographic setup by using both experimental and fuzzy logic prediction systems. Furthermore, the required time during the experimental optimization can be lowered by using a numerical method like the fu . . .zzy logic prediction system. Here, the experimental optimization results and the optimization results obtained by the fuzzy logic prediction system are compared. It is offered that the designed experimental system can be optimized by using an artificial intelligent tool. The applied fuzzy logic prediction model is used the first time for optimization of hologram recording setup. As a result, it is reached a conclusion that the optimization of digital holographic setup can be numerically performed by the fuzzy logic prediction system. Moreover, while digital holographic setup is experimentally designed, the required time for optimization is reduced, as well. © 2016 Elsevier Ltd. All rights reserved Daha fazlası Daha az

EEG signals classification using the K-means clustering and a multilayer perceptron neural network model

Orhan, Umut | Hekim, Mahmut | Özer, Mahmut

Article | 2011 | Expert Systems with Applications38 ( 10 ) , pp.13475 - 13481

We introduced a multilayer perceptron neural network (MLPNN) based classification model as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were decomposed into frequency sub-bands using discrete wavelet transform (DWT). The wavelet coefficients were clustered using the K-means algorithm for each frequency sub-band. The probability distributions were computed according to distribution of wavelet coefficients to the clusters, and then used as inputs to the MLPNN model. We conducted five different experiments to evaluate the performance of the proposed model in the classifications of different mixtures of . . . healthy segments, epileptic seizure free segments and epileptic seizure segments. We showed that the proposed model resulted in satisfactory classification accuracy rates. © 2010 Elsevier Ltd. All rights reserved Daha fazlası Daha az

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