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This paper investigated the rule table optimization of fuzzy power system stabilizer (FPSS) benefiting from rule basis of related previous studies. In the previous studies, fuzzy rules for Power System Stabilizer (PSS) were obtained by trial and error according to the experience of experts. There were a few rule tables occurred in that way in the literature. In this subject field, five rule tables with a few differences among them were taken. Genetic algorithm (GA) was employed as an optimization method, and single machine infinite bus (SMIB) model was used for simulation system. This work proposed to contribute optimization performance of FPSS adding these rule tables to the initial population of GA. Thus GAs reached an optimum solution more quickly. Simulation studies and the integral of absolute error (IAE) performance results for four loading conditions were shown. The effectiveness of the proposed approach was discussed by comparing it with the five rule tables. [ABSTRACT FROM AUTHOR]

Abstract: In this study, two types of solar air collectors are constructed and examined experimentally. The types are called as zigzagged absorber surface type and flat absorber surface type called Model I and Model II respectively. Experiments are carried out between 10.00 and 17.00h in August and September under the prevailing weather conditions of Karabuk (city of the Turkey) for 5days. Then, thermal performances belongs to experimental systems are calculated by using data obtained from experiments. To estimate thermal performances of solar air collectors an artificial neural network (ANN) model is designed. The measured data and calculated performance values are used at the design of Levenberg–Marquardt (LM) based multi-layer perceptron (MLP) in Matlab nftool module. Calculated values of thermal performances are compared to predicted values. Statistical error analysis is used to evaluate results. Comparing and statistical results demonstrate effectiveness of the proposed ANN. Also reliability of ANN and meaningfulness of input variables are tested via applying stepwise regression method to the data used in designing ANN. [ABSTRACT FROM AUTHOR]

In this study, effect of the marble cutting parameters on energy consumption was estimated by using Artificial Neural Network and ANFIS. In both of these estimation methods, marble's hardness, speed of circular saw rotation and cutting speed parameters were used as inputs, specific energy factor, cutting energy in per unit volume was taken as output. The data for training networks were taken from a PC based block cutter machine (S/T) with circular diamond saw blade at Afyon Kocatepe University Labs. These data are cutting parameters of "Denizli Travertine" and "Bilecik Beige" type marbles. Some of these data were used for training and then all data were used for testing stage of the networks. To show the effectiveness of the study, estimated data were compared with experimental data. As conclusion, it was reported that estimated data were quite close to experimental result. [ABSTRACT FROM AUTHOR]

In this study, effect of the marble cutting parameters on energy consumption was estimated by using Artificial Neural Network and ANFIS. In both of these estimation methods, marble's hardness, speed of circular saw rotation and cutting speed parameters were used as inputs, specific energy factor, cutting energy in per unit volume was taken as output. The data for training networks were taken from a PC based block cutter machine (S/T) with circular diamond saw blade at Afyon Kocatepe University Labs. These data are cutting parameters of "Denizli Travertine" and "Bilecik Beige" type marbles. Some of these data were used for training and then all data were used for testing stage of the networks. To show the effectiveness of the study, estimated data were compared with experimental data. As conclusion, it was reported that estimated data were quite close to experimental result. [ABSTRACT FROM AUTHOR]

Abstract: This paper represents an artificial neural network (ANN) backpropagation algorithm is used to choose best coefficients of hierarchical fuzzy power system stabilizer (HFPSS). PSS is used for stability enhancement of a single machine infinite bus (SMIB) power system. ANN algorithm is used to predict load condition of the power system. And according to the predicted load condition ANN determinates choosing optimal parameters of the hierarchical fuzzy controller (HFC) to achieve better performance. Simulation results are compared with conventional PSS (CPSS) to show the effectiveness of the proposed controller. Also quantitative criterias of measuring performance is computed for 16 loading conditions. [Copyright &y& Elsevier]