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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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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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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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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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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.