Fuzzy regression is a generalized regression model that shows the relationship between independent and dependent variables in the fuzzy environment. Fuzzy linear regression analysis is the generalization of regression models that is appropriate using all data based on a More
Fuzzy regression is a generalized regression model that shows the relationship between independent and dependent variables in the fuzzy environment. Fuzzy linear regression analysis is the generalization of regression models that is appropriate using all data based on a specific criterion. This paper uses an adaptive neural fuzzy inference system to analyze and predict a non-parametric fuzzy regression function with non-fuzzy inputs and symmetrical trapezoidal fuzzy outputs. To this end, a new hybrid algorithm is proposed in which fuzzy minimum squares and linear programming are used to optimize secondary weights. Algorithms are applied by multi layer validation to validate models. More precisely, the accuracy of the algorithms with simulations is fully confirmed. Finally, two simulation examples were used to examine the efficiency of the model, in which the data were defined as trapezoidal numbers and by teaching them and specifying the number of rules used, the unknown parameters were estimated.
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Introduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standar More
Introduction: The limitation of fresh water resources in the world, especially in arid and semi-arid regions such as Iran, has inevitably led to the reuse of urban wastewater. One of the most important indicators of sewage pollution and comparison with different standards for reuse or discharge to the water resources is TSS. The present study was conducted in 2016 with the aim of estimation of effluent TSS of Ahvaz wastewater treatment plant using inelegant models.
Material and methods: Regard to costly and time-consuming measurement tests of TSS, the capability of multivariate linear regression model, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) was studied to estimate (TSS) in wastewater treatment plant output by MATLAB and SPSS 21 software. Accordingly, various compounds of sewage quality parameters were evaluated during the 8-year statistical period (2008-2015) as input of models in two daily and monthly modes.
Results: The results of the regression model indicated that the maximum R2 for training and verification were 0.75 and 0.67 in daily and 0.68 and 0.66 in monthly period, respectively. The root mean square error (RMSE) in this test was 0.033 and 0.025 in the daily period and 0.053 and 0.053 in the monthly period. The maximum R2 in ANN for training and verification were 0.87 and 0.79 in daily and 0.87 and 0.85 in monthly period, respectively. The RMSE in this test was 0.030 and 0.023 in the daily period and 0.034 and 0.031 in the monthly period. Meanwhile, the maximum R2 in ANFIS for training and verification were 0.91 and 0.83 in daily and 0.89 and 0.87 for monthly period, respectively. The RMSE in this test was 0.026 and 0.025 in the daily period and 0.031 and 0.028 in the monthly period.
Conclusion: The results confirmed the application of three models is appropriate, but the ANFIS was considered as a more appropriate model.
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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.
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