List of Articles Adaptive Neuro Fuzzy Inference System

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  1 - Development and combination of soft computing and geostatistical models in estimation of spatial distribution of groundwater level
  سامان معروف پور احمد فاخری فرد جلال شیری
  One of the most important issues in groundwater resources quantitative management is estimating water table level using observation wells network data. The purpose of this study is to estimate the groundwater level using the combination of the geostatistics and soft com More

  One of the most important issues in groundwater resources quantitative management is estimating water table level using observation wells network data. The purpose of this study is to estimate the groundwater level using the combination of the geostatistics and soft computing methods. Bam Normashir and Rhmtabad plains (Kerman province) with an area of 1928 km2 was selected as a case study of this work. In this study, Kriging and IDW methods were used along with the data driven ANN, ANFIS and GEP models for predicting the spatial distribution of groundwater level, then, the best model was selected for further sampling in the studied region. Data from 65 wells during the period of 2002 to 2011 were used. RMSE, R2, AARE and MAE statistical indices were used for comparing the applied models. Results showed that for all of the models with two input parameters (including longitude and latitude), ANN and IDW models presented the most accurate results with the lowest RMSE (7.138 and 15.062m, respectively) and AARE (33 and 44%, respectively), and the highest R2 (0.606 and 0.596, respectively) for the point and regional estimation of groundwater table level. Finally, ANN-IDW hybrid model was selected for estimation and zoning the groundwater level for the future investigations. Manuscript profile
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  2 - Comparative Study and Robustness Analysis of Quadrotor Control in Presence of Wind Disturbances
  Reham Mohammed
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  3 - 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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  4 - استفاده از روش‌های شبکه عصبی موجکی و سیستم استنتاج فازی عصبی تطبیقی در پیش‌بینی بارش ماهانه
  اباذر سلگی حیدر زارعی بهداد فلامرزی
  پیش بینی بارش به دلیل ماهیت تصادفی آن در مکان و زمان همواره با مشکلات بسیاری مواجه بوده است و این عدم قطعیت از اعتبار بسیاری از مدل های پیش بینی می کاهد. امروزه شبکه های غیرخطی به عنوان یکی از سیستم های هوشمند در پیش بینی یک چنین پدیده های پیچیده ای بسیار مورد استفاده ق More

  پیش بینی بارش به دلیل ماهیت تصادفی آن در مکان و زمان همواره با مشکلات بسیاری مواجه بوده است و این عدم قطعیت از اعتبار بسیاری از مدل های پیش بینی می کاهد. امروزه شبکه های غیرخطی به عنوان یکی از سیستم های هوشمند در پیش بینی یک چنین پدیده های پیچیده ای بسیار مورد استفاده قرار می گیرند. یکی از روش هایی که در سال های اخیر در زمینه هیدرولوژی مورد توجه قرار گرفته است، استفاده از تبدیل موجک به عنوان روشی نوین و مؤثر در زمینه آنالیز سیگنال ها و سری های زمانی است. در پژوهش حاضر، تجزیه و تحلیل موجک به صورت ترکیب با شبکه عصبی مصنوعی و مقایسه با سیستم استنتاج فازی- عصبی تطبیقی برای پیش بینی بارش ایستگاه وراینه در شهرستان نهاوند انجام شد. برای این منظور، سری زمانی اصلی با استفاده از تئوری موجک به چندین زیرسیگنال زمانی تجزیه شد، پس از آن این زیرسیگنال ها به عنوان داده های ورودی به شبکه عصبی مصنوعی برای پیش بینی بارش ماهانه استفاده شد. نتایج به دست آمده نشان داد که مدل ترکیبی موجک- شبکه عصبی عملکرد بهتری نسبت به مدل سیستم استنتاج فازی- عصبی تطبیقی دارد و می تواند برای پیش بینی بارش کوتاه مدت و بلند مدت استفاده شود. همچنین نتایج نشان داد که مدل ترکیبی موجک- شبکه عصبی در برآورد نقاط حدی به خوبی عمل می کند. Manuscript profile
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  5 - Designing the Model of Relationship between Social Capital and Quality of Work Life
  Mir Mehrdad Peidaie
  This study aimed to clarify the relationship between quality of work life (Q.W.L.) and social capital (S.C.) in an educational organization. This study was practical in nature but it is descriptive based on research design. The study tried to simulate the relation betwe More

  This study aimed to clarify the relationship between quality of work life (Q.W.L.) and social capital (S.C.) in an educational organization. This study was practical in nature but it is descriptive based on research design. The study tried to simulate the relation between Q.W.L. and S.C. The population of this research is all the employees in an organization. 96 people were selected based on cluster sampling and Cochran formula.In the process of mathematical modeling, we used Adaptive Neuro Fuzzy Inference System (A.N.F.I.S.), the input of model is the variable of QWL and the output is SC variables. To validate the model, we used the checking and testing data in hybrid training process.The result of this research showed that there was a strong relationship between SC and job security and growth opportunities and there was a weak relationship between SC and healthy working environment. Manuscript profile
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  6 - Frequency Control in Multi-Carrier Microgrids with the Presence of Electric Vehicles Based on Adaptive Neuro Fuzzy Inference System Controller
  Seyed Ali Seyed Beheshti Fini Seyed Mohammad Shariatmadar Vahid Amir
  Nowadays, the use of renewable resources has increased because of fossil fuel price growth, resource shortage, and environmental pollution. This study investigates a microgrid composed of wind and solar systems with battery storage sources and flywheel, diesel generator More

  Nowadays, the use of renewable resources has increased because of fossil fuel price growth, resource shortage, and environmental pollution. This study investigates a microgrid composed of wind and solar systems with battery storage sources and flywheel, diesel generator, and multi-carrier energy systems (MCH) as combined electricity and heat (CHP). The microgrid frequency is controlled based on the gas network and its consumption peak. In a multi-carrier network, the load distribution in the gas network is simultaneously considered with the electric charge distribution. Besides, the frequency is controlled nonlinearly. On the other hand, the growing trend of producing and using electric vehicles has generated new loads on the electricity network. In this regard, if these loads are not properly managed to charge them, the network’s frequency deviations will increase and cause the collapse of the electricity network.Therefore, electric vehicles (V2G) are considered in microgrid frequency tuning operations through ANFIS adaptive fuzzy control method. In order to compare the proposed method in the simulations, a fuzzy controller is used. The results of the simulations are examined in five studies that express the optimal performance of the proposed method in reducing frequency deviations, strength against disturbances and resistance Uncertainties in the system. The proposed method also has a more stable output power in microgrid production resources. Manuscript profile
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  7 - 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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  8 - 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