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Article Citation - Scopus: 1Adaptive Neuro-Fuzzy Inference Technique for Estimation of Light Penetration in Reservoirs(Springer Japan Kk, 2007) Soyupak, Selcuk; Karaer, Feza; Senturk, Engin; Hekim, HuseyinAn adaptive neuro-fuzzy inference technique has been adopted to estimate light levels in a reservoir. The data were collected randomly from Doganci Dam Reservoir over a number of years. The input data set is a matrix with vectors of time, depth, sampling location, and incident solar radiation. The output data set is a vector representing light measured at various depths. Randomization and logarithmic transformations have been applied as preprocessing. One-half of the data have been utilized for training; testing and validation steps utilized one-fourth each. An adaptive neuro-fuzzy inference system (ANFIS) has been built as a prediction model for light penetration. Very high correlation values between predictions and real values on light measurements with relatively low root mean square error values have been obtained for training, test, and validation data sets. Elimination of the overtraining problem was ensured by satisfying close root mean square error values for all sets.Article Citation - WoS: 14Citation - Scopus: 15An Accurate Optical Gain Model Using Adaptive Neuro-Fuzzy Inference System(Natl inst Optoelectronics, 2009) Celebi, F. V.; Altindag, T.; Computer EngineeringThis paper presents a single, simple, new and an accurate optical gain model based on adaptive neuro-fuzzy inference system (ANFIS) which combines the benefits of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs). The dynamic optical gain model results are in very good agreement with the previously published experimental findings.
