adaptive neural fuzzy inference system

Submerged combustion vaporizers are one of the industrial equipments that produce a large amount of nitrogen oxides (NOx). These equipments are actually heat exchangers that are used in liquefied natural gas (LNG) terminals to evaporate liquefied natural gas and convert it into gas. Since previous studies have shown that the operating conditions of this equipment are effective on the amount of NOx production in it, artificial intelligence tools were used in this research to model and then optimize NOx emission in this equipment. For this purpose, 63 laboratory data were extracted from the researchers' previous researches, and then a combination of adaptive neural fuzzy inference system and genetic algorithm was used to model the data. In the developed system, oxygen concentration, temperature, water-oxygen concentration and solution pH were considered as input parameters to the model and NOx reduction percentage as output. The statistical analysis of the built model showed that this model with correlation coefficient of 0.9714, mean square error of 1.0938, average absolute error percentage of 4.9713 and maximum absolute error percentage of 13.2144 has a good accuracy in estimating the amount of NOx reduction. In the next step after the development of the model, the genetic algorithm and the built model were used to optimize the operating conditions with the lowest NOx emission rate. The results of this part of the research also showed that if the operating conditions are optimized, it is possible to reduce the amount of NOx released up to 37.24%

A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians.

Density flows are one of the most important factors in reducing the useful life of water structures, especially reservoir dams. Therefore, researchers have always been investigating solutions to eliminate these flows and increase the useful life of dams. One of the most practical methods known is to build an obstacle in the path of these currents. In this laboratory-numerical study, the effect of trapezoidal permeable obstacle (filled with 0.5 cm diameter grains) on the head of density flow is investigated and the effects of variables such as discharge, concentration, slope and height of the obstacle are studied. Based on the percentage reduction of the density flow head obtained from the experiments, the concentrated salt flow head is modeled using one of the soft computing methods known as the adaptive neural-fuzzy inference system (Anfis). Then, by comparing the results with the classical multivariable regression method, the performance of these two methods is compared. The error of training, validation and test data for the Anfis method are shown to be 0.07, 0.033 and 0.03, respectively, while for the multivariable regression method the mentioned errors are shown to be 0.12, 0.199 and 0.1084, respectively. Also, regression values for the training and test data for the Anfis method, are found to be 0.9954 and 0.9652 respectively whilst for the multivariable regression method, the mentioned parameters are shown to be 0.93108 and 0.90396 respectively which demonstrates the superiority and the efficiency of the adaptive neural-fuzzy inference system in modeling the head data.

A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians.

One of the most important effective parameters in earthquake, is behavior factor, which is depends on various factors such as structural geometric properties, structure ductility (performance level), fundamental period of the structure, damping, soil properties, earthquake characteristics (near- or far- field) and effect of higher modes. The most important feature of the behavior factor is that it allows the structural designer, to be able to evaluate the structural seismic demand, using an elastic analysis, based on force-based principles quickly. In seismic codes such as the Standard 2800, this coefficient is merely dependent on the type of lateral resistance system and is introduced with a fixed number. However, there is a relationship between the behavior factor, ductility (performance level), structural geometric properties, and type of earthquake (near fault and far fault). The purpose of this paper is to establish an accurate intelligent model related to the geometrical characteristics of the structure, performance level and the behavior factor in eccentrically steel frames, under earthquakes near-fault. For this purpose, genetic algorithm is used. Initially a wide database consisting of 12960 data with 3-, 6-, 9-, 12-, 15- and 20- stories, 3 column stiffness types, and 3 brace slenderness types were designed, and analyzed under 20 pulse-type near-fault earthquakes for 4 different performance levels. To generate the proposed model, 6769 training data were used in the form of adaptive-neural fuzzy inference system (ANFIS). Subtractive clustering and Fuzzy C-Mean clustering (FCM) methods have been used to generate the purposed model. The results showed that Fuzzy C-Mean clustering provides more accurate results than the other fuzzy inference system (FIS). To validate the proposed model, 2257 test data were used to calculate the mean squared error of the model. The results of correlation analysis of the proposed model show that the proposed intelligent model has high accuracy.

The need to solve the complex, nonlinear, and variable problems grows with time. Conventional mathematical models perform linear and constant analysis effectively. Although techniques that work on a particular model, capable of analyzing complex nonlinear and time-varying problems, however, they also face some limitations. Combining these with other issues such as decision making, etc., has inspired the development of intelligent techniques such as fuzzy logic, neural networks, genetic algorithms, and expert systems. Intelligent systems mainly employ a combination of these techniques to solve very complex problems. Although both fuzzy logic and artificial neural networks have been very successful in solving time-varying nonlinear problems, each has its own limitations which reduces their use in solve of many of these problems. The roof global ductility, is a comprehensive reflection of various engineering demand parameters (EDP), such as story-drift, plastic rotation at member ends, roof displacement, etc. Careful estimation of this parameter will certainly lead to greater accuracy in the design of structural members. One of the methods which establish a good estimate of the nonlinear seismic response is the using of EDP parameters and measuring the seismic intensity index. The main purpose of this paper is to establish an accurate intelligent model related to the geometrical characteristics of the structure, performance level, the behavior factor and global ductility in eccentrically steel frames, under earthquakes near-fault. For this purpose, genetic algorithm is used. Initially a wide database consisting of 12960 data with 3-, 6-, 9-, 12-, 15- and 20- stories, 3 column stiffness types, and 3 brace slenderness types were designed, and analyzed under 20 pulse-type near-fault earthquakes for 4 different performance levels. To generate the proposed model, 6769 training data were used in the form of adaptive-neural fuzzy inference system(ANFIS). Subtractive clustering and FCM methods have been used to generate the purposed model. The results showed that Subtractive clustering provides more accurate results than the other FIS. To validate the proposed model, 2257 test data were used to calculate the mean squared error of the model. The proposed model is an intelligent model in the range of data used, and can be used to estimate the global roof ductility of EBFs. To evaluate the efficiency and performance of the model, correlation coefficient and common error calculation criteria including RMSE and MARE were used. The correlation coefficient for the Subtractive clustering method was 0.888, based on intelligent model in the test data. In the other hand, the developed intelligent model can be used as a precise alternative to prediction of (μR) for EBFs under near-earthquakes. To evaluate the model’s efficiently and accuracy, various error criteria including Error, Mean Error, RMSE, MARE% and R were used between model values and real values, in the test data. From the results of this study, it can be pointed out that, the developed intelligent model can be used as an accurate substitute method to predict the (μR) for EBF structures, under near-fault earthquakes. The results of correlation analysis of the proposed model show that the proposed intelligent model has high accuracy.

A significant proportion of population in each community suffer from autism disorder. One of the challenges in diagnosing autism is the uncertainty in determining the severity of the disease. To this end, fuzzy systems based methods have been adopted in this study. The presented methods are based on 112 data driven from children and adolescents between the ages of 3 to 14 years. These data were collected from various rehabilitation centers in Tehran. The average performance accuracy of the proposed methods Using Genetic Algorithm with area under curve ROC compared to other methods (adaptive fuzzy neural inference system algorithm) proved to be 97/4% more reliable and efficient. The system designed in this article can be used as a medical diagnostics help tool for physicians.

Optimization of reservoirs operation is one of the most important tasks in the field of water resources management. In fact, vital requirement for beneficial use of water and energy resources clear the necessity of doing integrated planning and right operation of dams. recently, research has been made focusing on a shift from traditional single objective models to multi–objective models for the planning of multiple reservoir systems in a river basin. In this study the three objectives of meeting irrigation and environmental demand, flood control and recreation (sometimes in conflict with each other) are referred to for a two reservoir system by Goal Programming. Within this framework, the mathematical model of two reservoirs system in Sefidrud watershed (Northern Iran) with the three objectives is formulated and the system parameters and decision variables are defined. The problem involves finding desired water releases from each reservoir in the system in order to satisfy the multiple objectives. With comparing results of optimization models of this study, the model with the higher reliability indices was chosen as the best model. Due to the considerable advantages of linguistic rules in better inferring and interpreting the systems, an adaptive neural based fuzzy inference system (ANFIS) approach is used to consider uncertainties and to achieve a general method for multipurpose multi reservoir systems. The results of the Adaptive Neural Fuzzy Inference System (ANFIS) models shows that they can be applied successfully to provide high accuracy for the management of the reservoir systems.