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

Show more
79 Read more

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 **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** (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 **artificial** **neural** **network** (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning.

Show more
171 Read more

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. 4.2.1.1. 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).

Show more
15 Read more

students. The **Adaptive** **Network** **Fuzzy** **Inference** **System** (ANFIS) will be use as a technique to reasoning the student’s performance. ANFIS stands for **adaptive** **network**-**based** **fuzzy** **inference** **system** or semantically equivalently, **adaptive** **neuro** **fuzzy** **inference** **system**. ANFIS is proposed by Roger Jang from the Tsing Hua University, Taiwan, is a **neural** **network** that is functionally equal to a Sugeno **fuzzy** **inference** 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 **Artificial** **Neural** **Network** (ANN) and **Fuzzy** **Inference** **System** (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).

Show more
23 Read more

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 **artificial** **neural** **network** (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.

Show more
11 Read more

ANFIS was proposed by Roger Jang [18] 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 **neural** **network** 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.

Show more
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). **Artificial** **neural** 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). **Artificial** **neural** **network** 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

Show more
15 Read more

The main purpose of the current research is comparing the results of **Artificial** **Neural** **Network** (ANN) with **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** (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.

Show more
The aim of this research is to develop a predictive model to estimate the groundwater head in Safwan-Zubair area by using an **adaptive** **neural** **fuzzy** **inference** **system** (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 **artificial** **neural** **network** (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.

Show more
12 Read more

10 Read more

The first **artificial** **neural** **network** (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 [30]. 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 **neural** **network** modeling approach are given elsewhere [31].

Show more
38 Read more

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 [9]. A hybrid intelligent **system** is one which combines at least two intelligent systems. A combination of **Artificial** **Neural** **Network** and **Fuzzy** Logic creates **Neuro**-**fuzzy** **system** [10]. **Neuro**-**fuzzy** **system** is realized as a **neural** **network**, in which **fuzzy** **system** parameters are encoded in several layers [11].

Show more
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, **artificial** **neural** **network** (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 [1]. Several studies have been carried out using **fuzzy** logic in hydrology and water resources planning [2]. **Adaptive** **neuro**- **fuzzy** **inference** **system** (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. **Adaptive** **neuro** **fuzzy** **inference** **system** (ANFIS) is a **fuzzy** mapping algorithm that is **based** on Tagaki-Sugeno-Kang (TSK) **fuzzy** **inference** **system** [3] and [4]. ANFIS used for many applications such as, database management, **system**

Show more
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 **fuzzy** **system** 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).

Show more
19 Read more

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 [6] used **Artificial** Immune **System** (AIS) and resulted in 84.5% classification accuracy. The work in [7] utilized a hybrid **Neural** **Network** ANN and **fuzzy** **neural** **network** (FNN) and reached the classification accuracy of 86.8%. On the other hand, [8] developed SAS **based** software by using **neural** **network** ensemble method and obtained 89.01% accu- racy in classification. Recently, a research employed an MLP **Neural** **Network** by using Back propagation algo- rithm which classifies the data into 5 categories with 97.5% accuracy, whereas the SVM **based** **system** achieved 80.41% accuracy. In the presented study, a Multilayer Perceptron **Neural** **Network** (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 [1].

Show more
11 Read more

simulated the shear of RC beams with ANN. Kim and Kim [23] worked on the prediction of the relative crest settlement of concrete-faced rock-fill dams analyzed using an **artificial** **neural** **network** model. Caglar et al. [24] applied the **neural** **network** to dynamic analysis of reinforced concrete buildings. Arslan [25] predicted the tensional strength of RC beams by ANN and com- pared it with building codes. Erdem [26] used ANN to predict the moment capacity of RC slabs in fire. Takagi and Sugeno [27] developed **Fuzzy** **Inference** Systems (FIS) and applied them in modeling and controlling concepts. In addition, Jang introduced ANFIS in his book [28]. Topçu and Saridemir [29] predicted rubberized mortar properties using ANN and **fuzzy** logic. Bil- gehan [30] used ANFIS and **Neural** **Network** (NN) models to estimate critical buckling load.

Show more
16 Read more