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The significant wave height is a critical parameter in the design and analysis of marine structures, as well as in their operational use. Consequently, predicting this parameter greatly contributes to improving the design and analysis of marine structures. Various modeling approaches for wave characteristics include numerical, empirical, and artificial intelligence models. This study employs the SWAN model, which is a third-generation model for the simulation and estimation of wave characteristics. Furthermore, soft computing models, including individual and hybrid artificial intelligence models such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Emotional Artificial Neural Networks (EANN), have been utilized for wave height prediction, using data from the Amirabad buoy for validation purposes. In this research, the model inputs consist of wind speed, while the outputs are the wave heights. The analysis of the different models was carried out using statistical metrics, including bias, root mean square error, coefficient of variation, and coefficient of determination. The evaluation of the models using these statistics indicates an acceptable agreement between the significant wave heights estimated by the SWAN model and the buoy data. Additionally, each of the three artificial intelligence models mentioned demonstrates a relatively accurate capability in predicting wave height. A comparison of the results from the artificial intelligence models revealed that the Support Vector Machine model exhibited higher accuracy than the others. The Support Vector Machine model serves as an alternative method to the SWAN model or other numerical techniques, enhancing modeling outcomes when wave height data is unavailable or lacks the necessary statistical quality.
 

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The purpose of this research is to present a model for evaluating knowledge management empowerment based on Fuzzy Inference System (FIS). First, the initial model was compiled based on the review of the literature. After presenting the initial model to the experts, empowerment factors and their indexes were chosen by the Delphi Fuzzy Technique according to experts’ revisions and inputs. Culture, Structure, Information Technology and Leadership were confirmed as dimensions involved in empowerment. In order to gather information and extract FIS’ rules according to experts’ knowledge and experiences, interviews were conducted. With FIS outputs, knowledge management empowerments’ status would be determined. This evaluation helps managers perform the gap analysis between the existent knowledge management empowerment levels and the desired ones and helps them in decision making. After system design and evaluating its’ credibility, a questionnaire was used for measuring knowledge management empowerment factors and their indexes (FIS inputs) at Pasargad Bank.  FIS outputs show that the knowledge management empowerment situation at Pasargad Bank is at medium levels. Finally, in order to improve the firm’s current situation, certain solutions were provided. According to the literature review, evaluating knowledge management empowerment factors in the banking sector and using the Fuzzy Inference System for the evaluation of the knowledge management empowerment factors were innovations of this research.

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The main purpose of this research is to present a model for evaluating knowledge management enablers based on Fuzzy Inference System (FIS) to examine Pasargad bank situation. First of all, initial model compiled based on literature review. After presenting compiled model to experts, enablers and their indexes were concluded by Delphi Fuzzy Technique according to experts’ revisions and new ideas. In this model, Culture, Structure, Information Technology and Leadership were confirmed as enablers’ dimensions. For gathering information and extracting FIS rules according experts’ knowledge and experiences, semi structured interviews have been done. After system designing and crediting, a questionnaire was used for gathering data to measure knowledge management enablers and their indexes (FIS inputs) at Pasargad Bank. FIS output shows that knowledge management enablers situation at Pasargad Bank is at medium level. Evaluating knowledge management enablers at banking domain and using Fuzzy Inference System for evaluating knowledge management enablers were innovations of this research.

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Time and uncertainty play a crucial role in the strategic planning process [1]. Many industries have collapsed or been knocked out of the competition due to unforeseen able changes in the environment and their forecast about the future failed. Organizations are faced with unpredictable changes in new technologies, products and market places and their planned strategies are not able to respond to such a dynamic and changeable environment. These sorts of pressures are increasing in future because of the rapid developments of technology, economics and community. Needless to say, the future is not predictable but it is noteworthy that organizations can prepare themselves to face such changes and this readiness results in competitive advantages. The more the uncertainties, the more considerable the competitive advantages of organizations devised robust and stable strategies against uncertainty will be. This paper aims at introducing a method that enables organizations to draw up robust strategies in uncertain situations and leads to formulation of strategies to immunize them against environment changes. The method put forth in this paper has combined 'scenario planning method' and 'fuzzy inference system' with traditional strategic planning by adopting a novel and creative approach. Using the values of uncertain factors in the external environment, this method designs some probable forthcoming scenarios of the organization and then based on fuzzy information defined by experts for fuzzy inference systems, defines a robust strategy to deal with the designed scenarios. This method assists a manager and an organizational strategic planner in their evaluations of future environment and provides them with deep understanding of their planned strategies to keep their competitive advantages in the unstable and unsettled future.

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Estimating the size of shadow economy is of special importance in setting macroeconomic variables and fiscal policies. In recent years, the fuzzy inference sets have been used for measuring shadow economy. In this paper, we present eight new fuzzy indicators for modeling and estimating the size of shadow economy. Thus, according to Lucas definition, we divide the shadow economy into four sectors and define two indicators for each sector. After three fuzzy inference phases, we measure the size of shadow economy. Our results indicate that the effect of production household on Iran’s shadow economy size is decreasing; and irregular, informal and illegal sectors impact size of shadow economy. In addition, the size of Iran’s shadow economy is estimated around 13 percent of GDP, on average, over 1970- 2007.  

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Detection of tool wear and breakage during machining operations is one of the major problems in control and optimization of the automatic machining process. In this study, the relationship between tool wear with vibration in the two directions, one in the machining direction and the other perpendicular to machining direction was investigated during face milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable sandvik insert and ck45 work piece were used in the experiments. Tool wear was measured by a microscope. It was observed that there was an increase in vibration amplitude with increasing tool wear. In this study adaptive neuro - fuzzy inference systems (ANFIS) and multi-layer perceptron neural network (MLPNN) were implemented for classification of tool wear. In this study for the first time, five different states of tool wear was used for accurate tool wear classification. Also to accuracy and speed of the network Principle Component Analysis (PCA) was implemented. Using PCA, the input matrix size was reduced to an acceptable order causing more efficient networks. ANFIS and MLP were trained using feature vectors extracted from the spectrum frequency and time signals. The results showed that for 86 final measurements, the ANFIS and MLP networks were successful in classifying different tool wear state correctly for 91 and 82 percent, respectively. ANFIS due to its high efficiency in diagnosing tool wear and breakage can be proposed as proper technique for intelligent fault classification.

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In this paper, an intelligent robust controller is proposed for a class of nonlinear systems in presence of uncertainties and bounded external disturbances. The proposed method is based on a combination of terminal sliding mode control and adaptive neuro-fuzzy inference system with bee’s algorithm training. For this purpose, a sliding surface is firstly designed based on terminal sliding control method. This sliding surface is considered as input for the intelligent controller which is an adaptive neuro-fuzzy inference system and using it, terminal sliding mode control law without the switching part is approximated. In the proposed method, an intelligent bee’s algorithm is also used for updating the weights of the adaptive neuro-fuzzy inference system. Compared with fast terminal sliding mode control, the proposed controller provides advantages such as robustness against uncertainty and disturbance, simplicity of controller structure, higher convergence speed compared with similar conventional methods and chattering-free control effort. The method is applied to an atomic force microscope for nano manipulation. The simulation results show the robustness and effectiveness of the proposed method.

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The purpose of this research is to design a fuzzy inference system through the most significant influential factors and indicators on evaluation the success of ERP implementation. The intention has been to provide a comprehensive set of practical indicators in order to evaluate and improve the success of implementing enterprise resource planning (ERP) system based on deeply exploring the broad literature and to identify the most significance factors and indicators of ERP implmenetation. Through evaluating the identified indicators by domain experts, the most influential indicators have been used in designing the fuzzy inference system for evaluating the success of ERP implementation. Using this approach, the fuzzy system is designed so as to evaluate the success of implementing ERP system. The findings include the identification of the most influential indicators on the success of ERP system implementation as well as to design a fuzzy inference system to evaluate the success rate of enterprise resource planning system in organizations. Following the creation of the fuzzy inference system, organizations could measure their success in implementing enterprise resource planning system and thereby reduce their probability of failure in implementing ERP system.

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This research aims to describe a novel model, namely Hybrid Adaptive-Neuro Fuzzy Inference System-Particle Swarm Optimization (ANFIS-PSO), for predicting corrosion rate of 3C steel considering different marine environment factors. In the present research, five parameters (temperature, dissolved oxygen, salinity, pH, and oxidation–reduction potential) were used as input variables, with corrosion rate being the only output variable. In the proposed hybrid ANFIS-PSO model, the PSO served as a tool to automatically search for and update optimal parameters for the ANFIS, so as to improve generalizability of the model. Eeffectiveness of the hybrid model was then compared those to two other models, namely Adaptive-Neuro Fuzzy Inference System–Genetic Algorithm (ANFIS-GA) and Support Vector Regression (SVR) models, by evaluating their results against the same experimental data. The results showed that the proposed hybrid model tends to produce a lower prediction error than those of ANFIS-GA and SVR with the same training and testing datasets. Indeed, the hybrid ANFIS-PSO model provides engineers with an applicable and reliable tool to conduct real-time corrosion prediction of 3C steel considering different marine environment factors.

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In this paper, a control method based on fuzzy systems is presented to drive and keep state of a sample quantum system into a pre-defined region. The considered quantum system is a third-order quantum system and the model of the system is bilinear model. In addition, measurements of the system in the defined region are obtained at each times by considering the effects of such measurements in the internal state of the system. The effect of unwanted inputs and structural uncertainties also are considered as bounded uncertainties in the system’s Hamiltonian. In this paper, it is assumed that the initial state of the system is determined and internal state system are available as the feedback signals at each instant of time. In the proposed control approach, an acceptable region is firstly defined around the desired final state. Then, an adaptive neuro-fuzzy inference system improved using imperialist competitive algorithm is used for driving the system’s state toward the desired final state within this region. In addition, a fuzzy supervisor is utilized to adjust a control parameter for preserving the state of the quantum system inside the defined region. Simulation results, obtained by applying the proposed method to a sample third-order quantum system in presence of bounded uncertainties show the applicability and effectiveness of the method for controlling the quantum systems.

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Construction of buildings and structures causes to compact of soil particles and soil settlement. Hence, determination and prediction of soil settlement in the stability of structures, resulting from the applied loads, is necessary before construction. As a result of consolidation test that is relatively time-consuming and costly testing, compression index (Cc) is used to get the amount of settlement. In fact, soil settlement can cause extensive damage to a project in some cases. In order To prevent these damages, correct prediction can be useful for safe designing of structures. Cc may be as a function of various parameters such as initial void ratio of soil, moisture of liquid limit, moisture of plastic limit, plasticity index, relative density, and so on. By considering the longtime of consolidation test, researchers have tried to find relationship between these parameters and Cc from the past until now. For this reason they tried to connect Cc to other physical measurable properties of the soil.

In the past, some researchers have indirectly tried to measure these parameters. In this regard, several empirical single-parameter approaches are proposed to estimate Cc. Due to non-linear relationship between Cc and relevant parameters, Adaptive Neuro-Fuzzy Inference System (ANFIS) has found as an application to solve such non-linear problems and cases where an accurate understanding of the problem is required. ANFIS is a multilayer feed forward networks that is combination of Fuzzy Inference System (FIS) and Neural Network (NN). NN has ability to learn the input and output data and FIS is also capable for map the input space to the output space. ANFIS is a powerful tool to solve complex and nonlinear problems using the two mentioned features and also language power of FIS and numerical power of adaptive nervous system.

In this paper, Compression index (Cc) is modeled by ANFIS. Two ANFIS model were created by subtractive clustering (SC) and Fuzzy c-means clustering (FCM), respectively, and then trained. By data clustering, collection of training data is divided into a number of fuzzy clusters and each cluster representing the system behavior. The data were collected from the Soil Mechanics Laboratory of Mashhad city. ANFIS input parameters are taken according to the same parameters that commonly chosen in most of empirical models for determining Cc that easily determined in the laboratory. These input parameters include initial void ratio (e0), liquid limit (LL) and plastic limit (PL).
The number of required iterations for training (Epochs) in two ANFIS model, neighborhood radius (ra) in SC and number of clusters (NC) in FCM are optimized using trial and error method. After the end of solving and optimization of ANFIS models, the SC-FIS model was found in ra = 0.25 and NC =18 and the FCM-FIS model was obtained in NC = 20 with highest accuracy for prediction. Results showed both ANFIS model have a high capacity and appropriate forecasting for Cc prediction with chosen inputs parameters. Compared to the SC-FIS model, FCM-FIS is conducted prediction with higher accuracy. Using presented ANFIS models, can be predict the Cc of soils whose characteristics are within the specifications soils that used in this modeling with high accuracy and do not need to conduct consolidation tests that are very time consuming and costly.

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Inherent ambiguity and uncertainty in the nature of human resource because of bounded rationality and cognitive limitations always make difficult to predict behaviors of such complex system. Thus, predicting in this area necessitates modeling approaches to model ambiguity as part of system. The purpose of this study is to apply artificial intelligence and advance optimization algorithms to modeling personnel efficiency. To do so, it uses “Emotional Quotient (EQ)” and “individual characteristics” as input variables and “responsibility”, “work speed” and “work accuracy” as output variables. In order to model personnel efficiency, an Adaptive Neuro-Fuzzy Inference Optimized System (ANFIS) is introduced. This system utilizes genetic algorithm and Singular Value Decomposition (SVD) method. It can predict personnel efficiency with minimum training error, minimum predicting error and maximum adaptability to the real efficiency. It is worth mentioning that, for 84% to 96% of records, the extracted models are the same as the real efficiency.

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Adaptive neuro-fuzzy inference system (neuro-fuzzy or ANFIS) is a well-known hybrid neuro-fuzzy network for modeling complex systems. In this system ,the most frequently used fuzzy clustering method is the fuzzy subtractive clustering algorithm. In this algorithm, a cluster with a certain degree has each data point, explained by a membership function level. In this study, ANFIS model was used for prediction of weight reduction (%), solid gain (%),water loss (%) and rehydration (%) of banana slices dehydrated by osmosis-ultrasound method. The ANFIS model was developed with 3 inputs of sonication power (at three levels of 0, 75 and 150 watts), ultrasound treatment time (at three times of 10, 15 and 20 minutes) and sucrose solution concentration (at three levels of 30, 45 and 60 °Brix) to predict the characteristics of dehydrated banana slices. The calculated coefficient of determination values for prediction of weight reduction (%), solid gain (%),water loss (%) and rehydration (%) of dehydrated banana slices using the ANFIS-based subtractive clustering algorithm were 0.93, 0.95, 0.94, and 0.91, respectively. In general, it can be said that the high coefficients of determination between the experimental results and the outputs of the ANFIS model indicate acceptable accuracy and usability this method in controlling complex processes in the food industry, including dehydration and drying processes.

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ANFIS (Adaptive neuro-fuzzy inference system) is a combined neuro-fuzzy method for modeling transport phenomena (mass and heat) in the food processing. In this study, first, an infrared dryer was used to dry the extracted gum from quince seed. Then, ANFIS method was used to modeling and predicting the weight changes percentage of this gum when drying in infrared dryer. In the infrared dryer, the effect of samples distance from the radiation lamp and the effect of the gum thickness inside the container on the drying time and the weight loss percentage of quince seed gum during drying time were investigated. The results of drying of this gum by infrared method showed that by reducing the samples distance from the heat source from 10 to 5 cm, the average drying time of quince seed gum decreased from 58.0 minutes to 29.3 minutes (thickness 1.5 cm). Also, by reducing the gum thickness in the sample container from 1.5 to 0.5 cm, the average drying time of the extracted gum decreased from 45.7 minutes to 19.3 minutes (distance 7.5 cm). The ANFIS model was developed with 3 inputs of drying time, samples distance from heat source and gum thickness in the sample container to predict the weight changes percentage of this gum when drying in infrared dryer. The calculated coefficients of determination values for predicting the weight loss percentage of gum using the ANFIS-based subtractive clustering algorithm was 0.983. In general, it can be said that the high coefficients of determination between the experimental results and the outputs of the ANFIS model indicate the acceptable accuracy and usability of this method in modeling heat and mass transfer processes in the food industry.
 

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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.

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Teachers evaluation as one of the essential educational needs has an important role in improving educational quality and developing smart schools. The lack of implementation of a systematic and scientific evaluation system not only will discourage committed teachers, but also will have consequences such as reduced commitment and leave the organizational work. Therefore, the present study attempts to present a fuzzy inference system (FIS) model to evaluate the performance of teachers working in smart schools of Yazd province. In this regard, after reviewing the literature and identifying the factors affecting performance evaluation in two sections of computer literacy and classroom performance, the fuzzy Delphi technique was used to get experts agreement (20 experts) on the model’s final criteria. The ambiguity and complexity of performance appraisal criteria, especially qualitative criteria, as well as the use of verbal variables for extracting expert opinions, led to the use of fuzzy inference system for analyzing model components. In the case of the under study schools, the output of the system indicates that the status of smart school teachers in the basic courses and content production criteria is at intermediate level, in the Internet and educational supplementary software is at a relatively high level, and in the educational content development and electronic evaluation is at a low level. Based on these findings, teachers performance in smart schools was evaluated as medium level. Finally, some suggestions were proposed to improve the current status of smart schools.