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Article Citation - WoS: 1Application of Artificial Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Kapulukaya Dam Reservoir(Centre Environment Social & Economic Research Publ-ceser, 2007) Tuzun, Ilhami; Soyupak, Selcuk; Ince, Ozlem; Basaran, GokbenAn Artificial Neural Network (ANN) modelling approach has been shown to be successful in calculating time and space dependent dissolved oxygen (DO) concentration profiles in Kapulukaya Dam Reservoir using limited number of input variables. The variation of inflow to the reservoir with respect to time was significantly high. The reservoir operational levels were relatively stable. The Levenberg-Marquardt algorithm was adopted during training. Preprocessing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Different configurations of Multilayer perceptron neural networks were designed by selecting different combinations of number of hidden layers (single and double) and number of neurons within each of the hidden layers. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The conventional model criteria of correlation coefficient (R) and mean square errors (MSE) were adopted to compare model performances. The correlation coefficients between neural network estimates and field measurements were as high as 0.96 for daily and monthly data respectively with experiments that involve double layer neural network structure with 31 neurons within each hidden layer. The study results revealed that the data sizes effect model performances up to a certain level.Article Citation - WoS: 44Citation - Scopus: 49A Neural Network-Based Approach for Calculating Dissolved Oxygen Profiles in Reservoirs(Springer London Ltd, 2003) Soyupak, S; Karaer, F; Gürbüz, H; Kivrak, E; Sentürk, E; Yazici, AA Neural Network (NN) modelling approach has been shown to be successful in calculating pseudo steady state time and space dependent Dissolved Oxygen (DO) concentrations in three separate reservoirs with different characteristics using limited number of input variables. The Levenberg-Marquardt algorithm was adopted during training. Pre-processing before training and post processing after simulation steps were the treatments applied to raw data and predictions respectively. Generalisation was improved and over-fitting problems were eliminated: Early stopping method was applied for improving generalisation. The correlation coefficients between neural network estimates and field measurements were as high as 0.98 for two of the reservoirs with experiments that involve double layer neural network structure with 30 neurons within each hidden layer. A simple one layer neural network structure with 11 neurons has yielded comparable and satisfactorily high correlation coefficients for complete data set, and training, validation and test sets of the third reservoir.Article Citation - WoS: 1Citation - Scopus: 3A robust on-line learning algorithm for intelligent control systems(John Wiley & Sons Ltd, 2003) Efe, MÖ; Kaynak, O; Wilamowski, BM; Yu, XHThis paper describes a novel error extraction approach for exploiting the strength of Levenberg-Marquardt (LM) optimization technique in intelligent control systems: Since the target value of the control signal is unknown, tuning of the controller parameters becomes a tedious task if the knowledge about the system and the environment is limited. The suggested methodology utilizes the sliding model control (SMC) technique. The error extraction scheme postulates the form of error on the applied control signal using the discrepancy from the prescribed reaching dynamics. The devised approach has been tested on the non-linear Duffing oscillator, which has been forced to follow a periodic orbit radically different from the natural one. The results obtained through a series of simulations have confirmed the high precision and robustness advantages without knowing the analytical details of the system under investigation. The issues of observation noise and the stability in the parametric space have approximately been addressed from the point of SMC perspective. Copyright (C) 2003 John Wiley Sons, Ltd.
