Recently, Melesse et al. (2011) estimated suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA using ANN modeling approach. They trained a MLP ANN with an error back propagation algorithm, using historical daily and weekly hydro-climatologic data [precipitation, P(t), current discharge, Q(t), antecedent discharge, Q(t−1), and antecedent sediment load, SL(t−1)], to predict the suspended sediment load SL(t) at the selected monitoring station. They evaluated performance of the ANN using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). They compared the results from ANN model with results from MLR, multiple non-linear regression (MNLR) and autoregressive integrated moving average (ARIMA) process using correlation coefficient, R, mean absolute percent error (MAPE) and model efficiency (E). ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA approaches. The modeling approach, which they presented in their work, can be potentially used to reduce the frequency of costly operations for sediment measurement, where hydrological data is readily available
Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non- invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an AdaptiveNeuro-FuzzyInferenceSystem (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificialneuralnetwork (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.
In this section, the sensitivity analysis is performed based on the eﬀect of each individual non-dimensional input parameter on the output value. In other words, in order to understand the correlation of each input parame- ter with the output parameter using ANN and ANFIS models, ANN1, and ANFIS1 were constructed based on the raw data as their input parameters. In addition, in ANN2 and ANIS 2, (S/A) was added as an additional parameter to the already existing ones in ANN1 and ANFIS1. Therefore, the eﬀect of (S/A) on the prediction accuracy of the model was investigated. In the same way, all the other non-dimensional elements i.e. (W/C), (W/T), (A/C), (R/R) were added to ANN1 and ANFIS 1 individ- ually and constructed in a separate network. The details of these ANN and ANFIs models are shown in Table 4. 188.8.131.52. Sensitivity analysis using ANN model. Figs. 10–15 show the relationship between the target and output com- pressive strength of ANN1 to ANN6, respectively, for both the training and validation steps. According to these ﬁg- ures, the performance of ANN1 to ANN6 is shown based on correlation coeﬃcient (R).
students. The AdaptiveNetworkFuzzyInferenceSystem (ANFIS) will be use as a technique to reasoning the student’s performance. ANFIS stands for adaptivenetwork-basedfuzzyinferencesystem or semantically equivalently, adaptiveneurofuzzyinferencesystem. ANFIS is proposed by Roger Jang from the Tsing Hua University, Taiwan, is a neuralnetwork that is functionally equal to a Sugeno fuzzyinference model. ANFIS can serve as a basic for constructing a set of fuzzy if-then rules with appropriate membership functions to generate stipulated input-output pairs. (Jang, 1993). According to Prof. Ajith Abraham, a Neuro-Fuzzy (NF) system is a combination of ArtificialNeuralNetwork (ANN) and FuzzyInferenceSystem (FIS) in such a way that ANN learning algorithms are used to determine the parameters of FIS. ANN and FIS are both very powerful soft computing tools for solving a problem without having to analyze the problem itself in detail (Abraham, 2000).
In this study, we have been able to develop an architecture model that combines the knowledge-base and reasoning features of fuzzy logic (FL), with self-learning capacity of artificialneuralnetwork (ANN) to diagnose Hepatitis B disease by computing the intensity levels of its attack. Medical field refers to diagnosis as an approach of recognizing a disease through the analysis of underlying physiological symptoms. Hepatitis is a chronic liver disease that can easily decompensate to cirrhosis disease if its severity is not properly checked with appropriate tools. This study can improve the quality of liver disease diagnosis and provide appropriate information for medical practitioners in the process of administering treatment. This work could be further expanded for improved optimal solutions by integrating Genetic Algorithm (GA) with ANFIS.
network. The computation that each neuron performs, along with the way they are interconnected, decides particular type of neuralnetwork. The network usually consists of an input layer, some hidden layers and an output layer. The information contained in the input layer is mapped to the output layer through the hidden layers . The number of hidden layers and neurons within each layer can be designed by the complexity of the problem and data set. The estimation problem using neuralnetwork models has three successive steps: model building or neuralnetwork architecture, the learning or training procedure, and the testing procedure. An important stage when accommodating a neuralnetwork is the training step, in which an input is introduced to the network together with the desired outputs, the weights and bias values are initially chosen randomly and the weights are adjusted so that the network attempts to produce the desired output. The weights, after training, contain meaningful information, whereas, before training, they are random and have no meaning. When a satisfactory level of performance is reached, the training stops, and the network uses these weights to make decisions . A back propagation (BP) algorithm was chosen to calculate the weight values of the network. BP is composed of two phases. The knowledge is processed from the input layer to the output layer by means of a feedforward phase. In the BP phase, the difference between network output values obtained in feed forwarding and desired output value is compared with previously determined difference tolerance and the error in output layer is calculated. This error value is propagated backward to update the links in the input layer .
ANFIS was proposed by Roger Jang  from Tsing Hua University, Taiwan in the year 1993. ANFIS is a combination of ANN and Fuzzy logic system. ANFIS consists of five layers. The first layer consists of membership functions as computation layers. The membership can be any type of membership function. The second layer is used to find minimum or product of inputs from rule base. The third layer is used to normalize the weights for the network. The fourth layers output is the linear function of third layers output and generates the rule output. In fifth layer each rule output is summed up. ANFIS has smoothness due to fuzzy interpolation and adaptability due to neuralnetwork back propagation. ANFIS approach is based on many inputs and a single output. In this paper two inputs, current and flux linkage and a single output rotor position is taken for analysis. The single output is manipulated by the degree of membership function. ANFIS uses two algorithms. They are least mean square algorithm and back propagation algorithm. Back propagation is used as reverse pass and least mean square uses forward pass.
According to the recent researches, data driven induc- tive methods are popular. Because first, they have been extracted from the data and relation between them. Sec- ond, they tend to perform better in reproducing exist- ing spatial patterns (Overmars et al. 2007; Koomen et al. 2015). Artificialneural networks with capacity of nonlinear, parallel and highly complex processing have been employed in many fields such as climate fore- casting (Panagoulia 2006), agricultural land suitability assessment (Wang 1994), remote sensing (Morris et al. 2005) and land use change and urban growth modeling (Tayyebi et al. 2011; Pijanowski et al. 2002, 2009, 2014). Artificialneuralnetwork is a powerful tool in environ- mental modeling (Li and Yeh 2001). The ability to learn is the most important feature of this method. In the other words, the network uses data to identify patterns and relationships among the data. According to Almeida et al. (2008), Li and Yeh (2002), ANN method has the abil- ity to capture the non-linear relationships presented in many geographic phenomena (Li and Yeh 2002; Li et al. 2003). Thus, it can be used due to this ability to com- pute the conversion probabilities for competing multiple land uses. There is a general consensus among research- ers in the field of urban modeling that empiricism is a
The main purpose of the current research is comparing the results of ArtificialNeuralNetwork (ANN) with AdaptiveNeuro-FuzzyInferenceSystem (ANFIS) with regard to determination of the importance of soil properties affecting clay dispersibility. After taking samples from two depths of 0-40 and 40-80 cm, the spontaneous and mechanical dispersions of clay were recorded using both weighing and turbidimetric methods. To determine the degree of importance of soil properties affecting clay dispersibility, first ANNs and ANFIS in MATLAB Software were determined, using all research variables. After determining less effective properties and omitting them, the mentioned networks with the remaining variables including percentage of clay and sand, soil reaction, Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR) were measured and the degree of importance of each variable in clay dispersibility was determined. Finally, the results of ANNs and ANFIS were compared by calculation of validation parameters. Existence of high correlation between calculated values for weighing and turbidimetric methods showed a linear relationship between the two methods. In general, in both depths and for both weighing and turbidimetric methods, the sensitivity of clay dispersibility to the percentage of the clay, sand and SAR, was higher than any other variable. Although the results obtained from the validation statistics indicate high accuracy of both ANN and ANFIS models, the last model showed relatively better results as compared to ANN model.
The aim of this research is to develop a predictive model to estimate the groundwater head in Safwan-Zubair area by using an adaptiveneuralfuzzyinferencesystem (ANFIS). This area represents the southern sector of the Iraqi Desert, an arid region with scarce and limited resources. The data required for building the ANFIS model are generated using MODFLOW model (V.5.3). MODFLOW model was calibrated based on field measurements during one year. MODFLOW model generated (3797) hydraulic head values during each month. 70% of these values (2658 samples) was used for training, 30% of these values (1139 samples) was used for checking. The accuracy of the ANFIS models are compared with previous work based on artificialneuralnetwork (ANN) technique. Different combination of successive hydraulic heads and recharge rates of groundwater is used as input variables. There is no significant increase in the estimation accuracy when adding another input variable (recharge rate). Because the amount of this variable is very little, so its influence on the results was imperceptible. A comparison of ANFIS and ANN shows that the ANFIS model performs preferable than the ANN model on the checking phase. ANFIS model combines both fuzzy logic basics and neural networks; thus their properties can be utilized in one frame. It can be concluded, the ANFIS model appears to be more convenient than the ANN model for predicting groundwater hydraulic head from related input data.
Fuzzy systems, ArtificialNeuralNetwork (ANN), adaptivenetwork-basedinference, neuro-fuzzy and genetic fuzzy systems are types of new generation of simulation and modelling methods called artificial intelligent-based modelling methods that is applicable in all fields of science. In the field of civil and material engineering, it has been applied to simulate non-linear and complex behaviour for various properties of construction materials in recent years . Fuzzy systems, is particularly useful in the engineering applications where classical approaches fail or they are too complicated to be used. ANFIS is a class of adaptive networks which has the advantages of ANN and linguistic interpretability of FuzzyInference Systems (FIS) [2, 19]. Application of ANFIS was first proposed by Jang (1993)  used ANFIS to predict the CS of high performance conventional concrete from fresh concrete properties. Sadrmomtazi et al. (2013)  applied ANFIS analysis to study the relation between CS of lightweight concrete and mixing proportion.
ArtificialNeuralNetwork (ANN) or neural networks are a kind of information processing paradigm. These are inspired by the working of biological nervous systems such as brain. ANN process information in the same manner as biological nervous systems do. Neural networks are composed of highly interconnected processing elements called neurons. These neurons operate in parallel to solve a specific problem. Patterns to be analyzed are presented to the network via the 'input layer'. Getting a specific target output from a particular input requires neural networks to be adjusted, or trained.
The first artificialneuralnetwork (ANN) was invented in 1958 by psychologist Frank Rosenblatt called ‘perceptron’. ANN is a computational model, which replicates the function of a biological network composed of neurons. ANN is often used to model complex nonlinear functions in various applications. The basic unit in the ANN is the neuron. Neurons are connected to each other by links known as synapses. Andersen et al. (1990) first applied ANN in the welding area to predict weld bead shape for the gas tungsten arc welding (GTAW) process . Researchers have also applied ANN to develop predictive models for FSW joints [18-23]. A multilayer perceptron ANN system has three layers which are input, hidden, and output layers. The input layer consists of all the input factors. Information from input layer is then processed in the hidden layers, and then followed by the output layer (Figure 1). Details on the neuralnetwork modeling approach are given elsewhere .
to the data. Characteristic features of frequency domain vibration signals have been used as inputs to ANN consisting of one input, one hidden and one-output layer each. The ANN is trained using multiple layer feed forward back propagation Marquardt Algorithm. The ANN was used for diagnosis and quantification of faults. Wavelet transform approach enables instant to instant observation of different frequency components over the full spectrum . A hybrid intelligent system is one which combines at least two intelligent systems. A combination of ArtificialNeuralNetwork and Fuzzy Logic creates Neuro-fuzzysystem . Neuro-fuzzysystem is realized as a neuralnetwork, in which fuzzysystem parameters are encoded in several layers .
Neural networks approaches have been successfully applied in a number of diverse fields, including water resources. In the hydrological context, recent experiments have reported that artificialneural networks (ANN) may offer a promising alternative for modeling hydrological variables (e.g., rainfall–runoff, streamflow, suspended sediment) (Minnes and Hall 1996; Jain et al. 1999). However, the application of ANN to evapotranspiration modeling is limited in the literature. Kumar et al. (2002) used a multilayer perceptron (MLP) with backpropagation training algorithm for estimation of ET o . They used various ANN architectures and found that
The hydrologic behavior of rainfall-runoff process is very complicated phenomenon which is controlled by large number of climatic and physiographic factors that vary with both the time and space. The relationship between rainfall and resulting runoff is quite complex and is influenced by factors relating the topography and climate. In recent years, artificialneuralnetwork (ANN), fuzzy logic, genetic algorithm and chaos theory have been widely applied in the sphere of hydrology and water resource. ANN have been recently accepted as an efficient alternative tool for modeling of complex hydrologic systems and widely used for prediction. Some specific applications of ANN to hydrology include modeling rainfall-runoff process. Fuzzy logic method was first developed to explain the human thinking and decision system by . Several studies have been carried out using fuzzy logic in hydrology and water resources planning . Adaptiveneuro- fuzzyinferencesystem (ANFIS) which is integration of neural networks and fuzzy logic has the potential to capture the benefits of both these fields in a single framework. ANFIS utilizes linguistic information from the fuzzy logic as well learning capability of an ANN. Adaptiveneurofuzzyinferencesystem (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) fuzzyinferencesystem  and . ANFIS used for many applications such as, database management, system
to simple formulation and the ease of application. However, there are some disadvantages of ANN method. The network structure is hard to determine and it is usually determined using a trial and error approach, i.e., sensitivity analysis (ASCE Task Committee, 2000; Kisi, 2004b). Its training algorithm has the danger of getting stuck into local minima, etc. The ability of an ANN to extrapolate is limited when the input values in the prediction phase are far from the domain of the training data set. In this sense, an ANN is not very capable when it comes to extrapolation. An ANN model has a major drawback compared to physically based models, in that a new input variable that was not used in the training phase cannot be introduced to the model in the prediction phase, i.e., the number of input variables should be the same during the training and prediction phases (Sha, 2007; Dogan et al., 2008). On the contrary, the ANFIS models combine the transparent, linguistic representation of a fuzzysystem with the learning ability of the ANN. Therefore, they can be trained to perform an input/output mapping just as with an ANN, but with the additional benefit of being able to provide the set of rules on which the model is based. This gives further insight into the process being modeled (Sayed et al., 2003).
Until now, various classification algorithms have been employed on heart disease data set and high classification accuracies have been reported in the last decade. Cleveland heart disease database is one of the most accurate existing databases. Robert Detrano created this database in V.A. Medical Center, Long Beach and Cleveland Clinic Foundation in 1988. Since 1988, researchers worked a lot on classification of its data by using various classification algorithms and they obtained different accuracy results. The work presented in  used Artificial Immune System (AIS) and resulted in 84.5% classification accuracy. The work in  utilized a hybrid NeuralNetwork ANN and fuzzyneuralnetwork (FNN) and reached the classification accuracy of 86.8%. On the other hand,  developed SAS based software by using neuralnetwork ensemble method and obtained 89.01% accu- racy in classification. Recently, a research employed an MLP NeuralNetwork by using Back propagation algo- rithm which classifies the data into 5 categories with 97.5% accuracy, whereas the SVM basedsystem achieved 80.41% accuracy. In the presented study, a Multilayer Perceptron NeuralNetwork (MLPNN) with three layers is employed and compared with Support Vector Machine (SVM). Results indicated that a MLPNN with back propagation was more successful than support vector machine for diagnosing heart disease .
simulated the shear of RC beams with ANN. Kim and Kim  worked on the prediction of the relative crest settlement of concrete-faced rock-fill dams analyzed using an artificialneuralnetwork model. Caglar et al.  applied the neuralnetwork to dynamic analysis of reinforced concrete buildings. Arslan  predicted the tensional strength of RC beams by ANN and com- pared it with building codes. Erdem  used ANN to predict the moment capacity of RC slabs in fire. Takagi and Sugeno  developed FuzzyInference Systems (FIS) and applied them in modeling and controlling concepts. In addition, Jang introduced ANFIS in his book . Topçu and Saridemir  predicted rubberized mortar properties using ANN and fuzzy logic. Bil- gehan  used ANFIS and NeuralNetwork (NN) models to estimate critical buckling load.
Artificialneuralnetwork (ANN) is an advanced mathematical technique which uses intelligent learning paradigms and having several implementation fields such as social, science and engineering fields. The architectural structure of model consists of three layers such as input, hidden and output . In  each node in input layer transfers the value belonging to independent variable to the intermediate node and data coming to intermediate layer are combined in determined rules and transformed then mapped to target value in output layer. There is only one node in artificialneuralnetwork output layer which has been founded for credit rating. Artificialneural networks not to require to provide independent variables distributive characteristics or assumptions and they could model all non-linear relationships between input-output variables .