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1
Content available Daily Suspended Sediment Prediction Using Seasonal Time Series and Artificial Intelligence Techniques
Üneş Fatih, Taşar Bestami, Demirci Mustafa, Zelenakova Martina, Kaya Yunus Ziya, Varçin Hakan
Rocznik Ochrona Środowiska
|
2021
|
Tom 23
117--137
EN
Estimating the amount of suspended sediment in rivers correctly is important due to the adverse impacts encountered during the design and maintenance of hydraulic structures such as dams, regulators, water channels and bridges. The sediment concentration and discharge currents have usually complex relationship, especially on long term scales, which can lead to high uncertainties in load estimates for certain components. In this paper, with several data-driven methods, including two types of perceptron support vector machines with radial basis function kernel (SVM-RBF), and poly kernel learning algorithms (SVM-PK), Library SVM (LibSVM), adaptive neuro-fuzzy (NF) and statistical approaches such as sediment rating curves (SRC), multi linear regression (MLR) are used for forecasting daily suspended sediment concentration from daily temperature of water and streamflow in the river. Daily data are measured at Augusta station by the US Geological Survey. 15 different input combinations (1 to 15) were used for SVM-PK, SVM-RBF, LibSVM, NF and MLR model studies. All approaches are compared to each other according to three statistical criteria; mean absolute errors (MAE), root mean square errors (RMSE) and correlation coefficient (R). Of the applied linear and nonlinear methods, LibSVM and NF have good results, but LibSVM generates a slightly better fit under whole daily sediment values.
2
Content available A hybrid model for modelling the salinity of the Tafna River in Algeria
Houari Khemissi, Hartani Tarik, Remini Boualem, Lefkir Abdelouhab, Abda Leila, Heddam Salim
Journal of Water and Land Development
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2019
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no. 40
127--135
EN
In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).
PL
W pracy badano zdolność systemu wnioskowania rozmytego opartego na adaptacyjnej sieci (ANFIS) do przewidywania zasolenia rzeki Tafna. Do trenowania, oceny i testowania modelu hybrydowego wykorzystano serie pomiarów dobowych przepływów płynu i stężeń soli ze stacji pomiarowej w Pierre du Chat (160801). Dokładność wyników testowano za pomocą: współczynnika determinacji (R2), współczynnika wydajności Nasha–Sutcliffe’a (E), pierwiastka średniego błędu kwadratowego (RSR) i technik graficznych. Model dał zadowalające wyniki i wykazywał dobrą zgodność między danymi obserwowanymi a przewidywanymi: R2 (88% w przypadku uczenia sieci, 78.01% walidacji i 80.00% testowania), E (85.84% w przypadku uczenia sieci, 82.51% walidacji i 78.17% testowania) i RSR (2% w przypadku uczenia sieci, 10% walidacji i 49% testowania).
3
Content available remote A performance evaluation of neuro‑fuzzy and regression methods in estimation of sediment load of selective rivers
Varvani J., Khaleghi M. R.
Acta Geophysica
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2019
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Vol. 67, no. 1
205--214
EN
Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.
4
Content available Application of competitive and transition petri layers in adaptive neuro-fuzzy controller
Derugo P.
Power Electronics and Drives
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2016
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Vol. 1 (36), No. 1
103--115
EN
The article is a summary of previous work on the possibility of using Petri layers in adaptive neuro-fuzzy controllers. In the first part of the paper the controller and two types of Petri layer have been presented, competitive layer which resets certain signals and transition layer which causes omission of signals. Layer properties were described and comparison has been made. In the second part of the paper, the results of a simulation showing the advantages and disadvantages of proposed solutions have been presented. Both quality of reference signal tracking and energetic cost of control process have been calculated. In the last part, analysis and comments on the results were made. Main conclusions are that transition Petri layer can significantly reduce growth of numerical cost of the algorithm despite the increase of fuzzy rules count. Also both competitive Petri layer and transition Petri layer by changing some inner signals can affect output value of the fuzzy system and thus the control quality indicators change. Most positive solutions have been pointed out
5
Content available Neuro-fuzzy modelling of blending process in cement plant
Araromi D O, Odewale S A, Hamed J O
Advances in Science and Technology. Research Journal
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2015
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Vol. 9, nr 28
27--33
EN
The profitability of a cement plant depends largely on the efficient operation of the blending stage, therefore, there is a need to control the process at the blending stage in order to maintain the chemical composition of the raw mix near or at the desired value with minimum variance despite variation in the raw material composition. In this work, neuro-fuzzy model is developed for a dynamic behaviour of the system to predict the total carbonate content in the raw mix at different clay feed rates. The data used for parameter estimation and model validation was obtained from one of the cement plants in Nigeria. The data was pre-processed to remove outliers and filtered using smoothening technique in order to reveal its dynamic nature. Autoregressive exogenous (ARX) model was developed for comparison purpose. ARX model gave high root mean square error (RMSE) of 5.408 and 4.0199 for training and validation respectively. Poor fit resulting from ARX model is an indication of nonlinear nature of the process. However, both visual and statistical analyses on neuro-fuzzy (ANFIS) model gave a far better result. RMSE of training and validation are 0.28167 and 0.7436 respectively, and the sum of square error (SSE) and R-square are 39.6692 and 0.9969 respectively. All these are an indication of good performance of ANFIS model. This model can be used for control design of the process.
6
Content available remote Prediction-based Active Queue Management in the Internet
Jamali S., Nosrati M., Hashemi S. N.
Przegląd Elektrotechniczny
|
2013
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R. 89, nr 1a
296--299
EN
Random early detection (RED) is the most popular active queue management algorithm that is used by the Internet routers. This paper proposes a neuro-fuzzy controller which enhances the network performance by dynamically tuning of RED's maxp parameter. The controller first learns the network behavior against maxp variations and then adjusts maxp. Simulation results in ns-2 environment show that, the proposed learning RED, called LRED, keeps queue length and queuing delay in a pre-determined level and outperforms RED in terms of queue length and stability.
PL
W artykule zaprezentowano sterownik neuro-fuzzy który poprawia dynamiczne strojenie system RED stosowanego do kolejkowania w Internecie. Proponowany uczący się algorytm nazwany LRED pozwala na utrzymanie długości kolejki i opóźnienia w założonych granicach.
7
Content available remote Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease
Obot O. U., Uzoka F. M., Akinyokun O. C., Andy J. J.
Bio-Algorithms and Med-Systems
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2013
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Vol. 9, no. 3
115--133
EN
In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.
8
Content available Neuro-rough-fuzzy approach for regression modelling from missing data
Simiński K.
International Journal of Applied Mathematics and Computer Science
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2012
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Vol. 22, no. 2
461-476
EN
Real life data sets often suffer from missing data. The neuro-rough-fuzzy systems proposed hitherto often cannot handle such situations. The paper presents a neuro-fuzzy system for data sets with missing values. The proposed solution is a complete neuro-fuzzy system. The system creates a rough fuzzy model from presented data (both full and with missing values) and is able to elaborate the answer for full and missing data examples. The paper also describes the dedicated clustering algorithm. The paper is accompanied by results of numerical experiments.
9
Content available Merging of fuzzy models for neuro-fuzzy systems
Simiński K.
Theoretical and Applied Informatics
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2011
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Vol. 23, No. 2
107-126
EN
The merging of fuzzy model is widely used for reduction of rule number in fuzzy model. The supernumerosity of rules is mainly caused by grid partition of input domain. In the paper different cause for model merging is described. It is the need for creation of fuzzy model for large data set. In our solution the models are build basing data subset and then the submodels are merged into one. This approach enables quicker elaboration of submodels with relatively good knowledge generalisation ability without waiting for the whole data set to be processed. With passing time, the subsequent submodels are created and merged to create the better model.
PL
Artykuł opisuje scalanie modeli rozmytych w systemach neuronowo-rozmytych wykorzystywane przy tworzeniu modeli dla dużych zbiorów danych. Nieraz zbiory danych są tak duże, że nie jest możliwe wypracowanie modelu od razu dla całego zbioru. Tworzy się zatem modele dla podzbiorów zbioru danych. Uzyskane w ten sposób modele są następnie scalane, by wypracować jeden model. Podejście to jest także korzystne, gdy wszystkie dane nie są dostępne, ale są dostarczane partiami. Wtedy wstępny model jest wypracowany zanim wszystkie dane zostaną dostarczone do systemu. Artykuł przedstawia sposób wyznaczania podobieństwa reguł w modelu rozmytym oraz opisuje system neuronowo-rozmyty budujący i scalający modele wypracowane dla podzbiorów.
10
Content available Pole placement approach to discrete and neuro-fuzzy crane control system prototyping
Smoczek J., Szpytko J.
Journal of KONES
|
2009
|
Vol. 16, No. 4
435-445
EN
Today are observed rising requirements regarding increase productivity, reduced labour and maintenance cost, as well as optimizing the effectiveness of the material handling. The overhead travelling cranes play important role in selected manufacture applications. The paper presents methods of crane dynamic modelling and anti-sway discrete crane control system determining with using pole placement method (PPM). The TSK neuro-fuzzy crane controller was shown in the paper, as well as method of adaptation its control parameters to various values of rope length and masses of the load variables. The results of experiments carried out on real object were presented as well. Presented in the paper methods of crane dynamic modelling and control algorithm determining allow to prototype the effective anti-sway crane control systems. The method of determining conventional anti-sway crane control system based on discrete controllers type of PD elaborated with using pole placement method (PPM) was described in the paper. The TSK neuro-fuzzy crane controller was shown in the paper as well as method of adaptation its control parameters to various values ofrope length l and masses of the load m variables. The results of experiments carried out with using adaptive neuro-fuzzy TSK controller shown robustness on changeability of these variables and effectiveness of proposed control system.
11
Content available remote Wpływ temperatury na krótkoterminową prognozę obciążenia elektroenergetycznego
Łosiowski W.
Elektrotechnika i Elektronika
|
2000
|
T. 19, z. 2
132--139
PL
W artykule omówiono wpływ temperatury na prognozę dobowego profilu obciążeń elektroenergetycznych z wyprzedzeniem od jednego dnia do tygodnia, dla każdej pory roku, dla spółki dystrybucyjnej o maksymalnej wartości godzinowego szczytu rocznego rzędu 1000 MW. Do określenia prognozy użyto modeli opartych na sieci neurorozmytej ANFIS i sieci neuronowej typu kaskadowego. Zwrócono uwagę na praktyczny brak zależności od temperatury w okresie letnim oraz problemy związane z dokładnością prognozy i porównywania wyników uzyskanych dla różnych wielkości systemów energetycznych.
EN
Temperature influence of short-term daily profile load forecasting one day to week ahead, for each season.for distri- bution company with max daily pik about 1000 MW, was shown. For realise models of load forecasting, neuro-fuzzy ANFIS and cascaded neural network were used. Practical temperature indepedence in summer season was observed. Accuracy and uncomparison results problems for different magnitude systems were discused.
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