Multilayer feedforward networks are universal approximators
Neural Networks
Machine Learning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
In-vehicle network level fault diagnostics using fuzzy inference systems
Applied Soft Computing
Applying ensemble learning techniques to ANFIS for air pollution index prediction in macau
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Application of adaptive network based fuzzy inference system method in economic welfare
Knowledge-Based Systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
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In this study various ANN and ANFIS models were developed to forecast the lake level fluctuations in Lake Urmia in northwest of Iran. In addition to the time series of lake levels, the time series of three most effective variables in the water budget of the lake namely, the rainfall, evaporation and inflow were also used to find the best input variables to the models. Furthermore the uncertainty due to the error in measuring the hydrological variables and also the uncertainty in the outputs of ANN and ANFIS models which stems from their sensitivity to the training sets used to train these models and also the initial configuration before commencement of training were estimated. Comparing the outputs and confidence intervals of the two types of models it was found that the results of ANFIS model are superior to those of ANN' in that they are both more accurate and with less uncertainty.