List of Articles Adaptive Neuro Fuzzy Inference System (ANFIS)

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  1 - Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances
  Reham Mohammed
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  2 - Modeling and zoning water quality parameters using Sentinel-2 satellite images and computational intelligence (Case study: Karun river)
  Kazem Rangzan Mostafa Kabolizade Mohsen Rahshidian Hossein Delfan
  Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 More

  Considering the progress made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can monitor all surface water zones. In this study, the Sentinel-2 satellite images were used to estimate the concentration of acidity, bicarbonate and sulfate parameters. Initially, Sentinel-2 satellite images were pre-processing and then bands and spectral indexes were determined to identify the significant relationship between the parameter values of water quality and images using the multivariate regression method. In the next stage, using Artificial neural network (ANN) and Adaptive Neuro fuzzy inference system (ANFIS) models, the relationship between Sentinel-2 satellite images and water quality parameters were modeled and then their accuracy was calculated for real values. The results showed that in the modeling of sulfate parameter using Sentinel-2 satellite, ANFIS model with relative error equal to 0.0773 and RMSe equal to 0.8014 has a higher accuracy compared to ANN models with relative error equal to 0.1581 and RMSe equal to 1.2477. While, the relative error of the results of the ANN model are obtained 0.0064 and 0.0556 for acidity and bicarbonate parameter, respectively, and RMSe is equal to 0.0702 and 0.2691, respectively.  The ANFIS model has a relative error of 0.0165 and 0.0722, and RMSe is 0.1975 and 0.3037 for acidity and bicarbonate parameter, respectively. Finally, using satellite images, the mentioned models were applied to prepare a qualitative map of each parameter along the part of the Karun river. Manuscript profile
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  3 - Long-term Streamflow Forecasting by Adaptive Neuro-Fuzzy Inference System Using K-fold Cross-validation: (Case Study: Taleghan Basin, Iran)
  Reza Esmaeelzadeh Alireza Borhani Dariane
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  4 - Hybrid PCA-ANFIS approach and Dove Swarm Optimization for predicting Financial Distress
  sina Kheradyar Mohammad Hasan Gholizadeh Forough Lotfi
  In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Principal Component Analysis (PCA) is proposed for predicting the financial distress of companies. This system not only has the ability to adapt and learn, but also reduces the error, because it av More

  In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Principal Component Analysis (PCA) is proposed for predicting the financial distress of companies. This system not only has the ability to adapt and learn, but also reduces the error, because it avoids additional parameters when input variables are too high. In order to confirm the effectiveness of this model, 181 listed companies in the Tehran Stock Exchange (905 companies-years) were selected by using systematic samples from 2011 to 2015, which 58 of those were distressed and 847 companies-years were healthy. These companies were randomly divided into two sets: a training set for designing model and a check set for validating the model. The results of the research show that the Adaptive Neuro Fuzzy Inference System based on Principal Component Analysis is capable for predicting the financial distress of companies accepted in Tehran Stock Exchange and when the proposed model is combined with Dove Swarm Optimization metaheuristic algorithm, Reducing the error value increases the accuracy of the model. Therefore, it can be seen that the use of a complementary algorithm can increase the predictability of the PCA-ANFIS model. Manuscript profile