فهرست مقالات Type-2 fuzzy logic

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  1 - Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation
  Zahra Barati Mahdi Jafari Shahbazzadeh Vahid Khatibi Bardsiri
  predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to pre چکیده کامل

  predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models. پرونده مقاله
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  2 - A Hybrid Type-2 Fuzzy-LSTM Model for Prediction of Environmental Temporal Patterns
  Aref Safari Rahil Hosseini
  Computational intelligence methods, such as fuzzy logic and deep neural networks, are robust models to solve real-world problems. In many dynamic and complex problems, statistical attributes frequently change over the time. Recurrent neural networks (RNN) are suitable t چکیده کامل

  Computational intelligence methods, such as fuzzy logic and deep neural networks, are robust models to solve real-world problems. In many dynamic and complex problems, statistical attributes frequently change over the time. Recurrent neural networks (RNN) are suitable to model dynamic high-dimensional and non-linear state-space systems. Nevertheless, the RNN is incapable of modelling long-term dependencies in temporal data, and its learning using gradient descent is a complex and difficult task. Long Short-Term Memory (LSTM) networks were introduced to overcome the RNN issues, but coping with uncertainty is still a major challenge for the LSTM models. This research presents a Hybrid Type-2 Fuzzy LSTM (HHT2FLSTM) deep approach to learn long-term dependencies in order to obtain a reliable prediction in uncertain time series circumstances. The proposed model was applied to the air quality prediction problem to evaluate the model’s robustness in handling uncertainties in a real-world application. The proposed model has been evaluated on a real dataset that contains the outdoor pollutants from July 2011 to October 2020 in Tehran and Beijing by a 10-fold cv with an average area under the ROC curve of 97 % with a 95% confidence interval [95-97] %. پرونده مقاله
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  3 - Evolutionary Interval Type-2 Fuzzy Rule Learning Approaches for Uncertain Time-Series Prediction
  Aref Safari Rahil Hosseini
  This study presents Interval Type-2 Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-2 fuzzy evolutionary models for rule extraction that were proposed: 1) Evolutionary Interval Type-2 Fuzzy چکیده کامل

  This study presents Interval Type-2 Fuzzy Evolutionary models to manage uncertainty in the process of uncertain time-series prediction. This study presents two type-2 fuzzy evolutionary models for rule extraction that were proposed: 1) Evolutionary Interval Type-2 Fuzzy Rule Learning (EIT2FRL), and 1) Evolutionary Interval Type-2 Fuzzy Rule-Set Learning (EIT2FRLS). A ROC curve analysis was applied for performance evaluation, and the results were validated using a 10-fold cross-validation technique. The results reveal that the proposed methods have an AUC of 0.96 for EIT2FRLS and 0.93 for EIT2FRL proposed methods. The results are promising for knowledge extraction in uncertain circumstances, predicting uncertain patterns prediction, and making suitable strategies and optimal decisions. پرونده مقاله
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  4 - An Interval Type-2 Fuzzy LSTM Algorithm for Modeling Environmental Time-Series Prediction
  Aref Safari Rahil Hosseini
  10.22034/ap.2022.1963124.1133
  The statistical attributes of the non-stationary problems such as air quality and other natural phenomena frequently changed. Type-2 fuzzy logic is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. T چکیده کامل

  The statistical attributes of the non-stationary problems such as air quality and other natural phenomena frequently changed. Type-2 fuzzy logic is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. This research's main objective is to present a novel Fuzzy Deep LSTM (IT2FLSTM) model to predict air quality for Tehran and Beijing in a short and long time series scale. The proposed model has been evaluated on a real dataset that contains the one-decade information about outdoor pollutants from April 2011 to November 2020 in Tehran and Beijing. The IT2FLSTM model was evaluated using a ROC curve analysis and validated using 10-fold cross-validation. The results confirm the IT2FLSTM model's superiority with an average area under the ROC curve (AUC) of 97 % and a 95% confidence interval of [95-98] %. The proposed IT2FLSTM model promises to predict complex problems to make strategic prevention decisions to save more lives. پرونده مقاله