I certify that an Examination Committee met on 06 December 2007 to conduct the final examination of SHAHRAM KARIMI GOOGHARI on his Doctor of Philosophy thesis entitled “**Reservoir** **Inflow** **Forecasting** **Using** **Artificial** **Neural** Networks and **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** **Techniques**” in accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the candidate be awarded the relevant degree. Members of the Examination Committee are as follows:

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This paper is dealing with the development of a **forecasting** **system** based on ANFIS, which differs from the traditional **Artificial** **Neural** Networks (ANN) in that it is not fully connected and not all the weights or nodal parameters are modifiable. The model uses a hybrid learning algorithm to identify the parameters for the Sugeno- type **fuzzy** **inference** systems. It applies a combination of the least-squares method and the back-propagation gradient descent method for training the **Fuzzy** **Inference** **System** (FIS) membership function parameters to match the given training data set. Specifically, a back-propagation algorithm is used to optimize the **fuzzy** sets of the premises and a least-squares procedure is applied to the linear coefficients in the consequent terms. In addition, it uses a testing data set for checking the model over fitting. ANFIS is a multilayer **neural** **network**-based **fuzzy** consisted of five layers, in which the training and predicted values are represented by the input and output nodes and the nodes functioning as membership functions (MFs) and rules are presented in the hidden layers. Its topology is shown in Figure 1. During the learning phase of ANFIS, the parameters of the membership functions are changing continuously in order to minimize the error function between the target output and the calculated values. ANFIS has a feed-forward **neural** **network** structure where each layer is a **neuro**-**fuzzy** **system** component. ANFIS is capable to learn and generalize the training data. The consequents of the Takagi-Sugeno (TS) **fuzzy** rules are linear combinations of their preconditions in this method. ANFIS can simply be defined as the combination of ANNs and **fuzzy** logic. This combined **system** has the abilities of deducing knowledge from given, learning, generalization, adaptation and parallelism. FIS is a framework based on **fuzzy** set theory and **fuzzy** if-then rules. The structure of FIS has three main components: a rule base, a database, and a reasoning mechanism. This model is called first-order Sugeno model.

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Another model that is used in this work is **Neuro**-**Fuzzy**, which is the combination of **fuzzy** logic and **neural** networks in order to solve wide variety of real world problems in an effective manner. This combination is for removing the limitation of each model. Since **neural** networks are good at recognizing patterns and not good at explaining how they achieve their decisions. **Fuzzy** logic systems that can give inexact reasons, and explain their decisions well but not good at reaching the rules they use to make those decisions [11]. The ability to model a problem domain **using** a linguistic model instead of complex mathematical is the main advantage of **using** the **Neuro**-**Fuzzy** combination [12]. Therefore, these **techniques** are complementary to be used together [13].

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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.

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IMAGES are often corrupted by impulse noise during image acquisition and transmission over communication channel. Noise elimination and enhancement are essential features in digital image processing. Because the performances of subsequent image processing tasks are strictly dependent on the success of the noise removal operation. However, this is a difficult task because the noise removal operator is imposed with the requirement of preserving useful information in the image while efficiently removing the noise. Digital images are valuable sources of information in many research and application areas including astronomy, biology, medicine, remote sensing, materials science, etc. During image acquisition, digital images are frequently corrupted by noise due to number of imperfections in the imaging process. The image corruption is usually introduced by a nonideal imaging **system** (sensor noise, limited **system** accuracy, finite precision, quantization of image data, etc.) or an imperfect medium between the original scene and the imaging **system** (random scattering, absorption, etc.). The currently available nonlinear filters cannot simultaneously satisfy both of these criteria. The existing filters either suppress the noise at the cost of reduced noise suppression performance. **Neural** networks and **fuzzy** systems had been investigated to the problems in digital signal processing. A **Neuro**-**Fuzzy** **System** is a flexible **system** trained by heuristic learning **techniques** derived from **neural** networks can be viewed as a 3-layer **neural** **network** with **fuzzy** weights and special activation functions is always interpretable as a **fuzzy** **system** uses constraint learning procedures is a function approximation (classifier, controller). In this paper, **Adaptive** **Neuro**-**fuzzy** **Inference** **System** (ANFIS) is presented, which is a **fuzzy** **inference** **system** implemented in the framework of **adaptive** **network**. This ANFIS training algorithm is suggested by Jang. By **using** hybrid learning procedure, the proposed ANFIS can construct an input-output mapping which is based on both human knowledge (in the form of **fuzzy** if- then rules) and learning. The proposed work is carried in two stages. In first stage, noisy image (i.e. received or captured by camera) is denoised by new tristate switching median filter. The denoised image is further enhanced by ANFIS. . The structure of the proposed hybrid filter, to make the process robust against noise, is a combination of nonlinear switching median filter and **neuro**-**fuzzy** **network**. The internal parameters of the **neuro**-**fuzzy** **network** are adaptively optimized by training. The proposed filter is evaluated under

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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).

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ANFIS is a **fuzzy** **inference** **system** (FIS) implemented in the framework of an **adaptive** **fuzzy** **neural** **network**. It combines the explicit knowledge representation of an FIS with the learning power of **artificial** **neural** networks. The objective of ANFIS is to integrate the best features of **fuzzy** systems and **neural** networks. **Using** a given input/output data set, ANFIS constructs a FIS whose membership function parameters are tuned (adjusted) **using** either a back propagation algorithm alone or in combination with a least squares type of method[5] [7]. This adjustment

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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**

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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.

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Recently developed methods used **Artificial** Intelligence (AI) methods, like **Neural** **Network** (NN), **Adaptive** **Neuro**-**Fuzzy** **Inference** **System** (ANFIS), Support Vector Machine (SVM) and Radial Basis Function (RBF), in **forecasting** the demand within the short-term time frame. On the other hand, only historical load data and temperature are used as model input parameters. Very few used relative humidity in addition to load and temperature. There might be other weather variables that affect the load consumption different from temperature and relative humidity, and the relationship between the load and these variables needs to be investigated.

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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.

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(Acedański, 2013).In prediction models, inefficient use of resources increases prediction error. Of the reasons of inefficient use of resources is the lack of proper parameters in models, the lack of proper modeling **techniques**, and the lack of use of indicators that has capable of predicting sufficiently in the **forecasting** process (Günay, 2018). A survey of the GDP in recent years (Fig. 1) shows that the share of the industrial sector has fallen (World Bank). In addition, significant fluctuations have been experienced in the economic environment, which have reduced the predictive power of variables and created uncertainty about the future (Zamanzadeh, 2010). This is why researchers must provide an efficient way to predict industrial production through the planning and managing of the effective parameters to manage of the future trends.

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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.

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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].

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For decades, due to the increase in electrical power demand, we are in need of enlarging transmission capacity by installing 500-kV extra high-voltage power transmission lines in both AC and DC. The impact of electric fields surrounding the transmission line depends strongly on conductor surface potentials, while load currents flowing through the transmission line result in magnetic field distribution. For the 500 kV systems, high current density transmission is the main purpose. It can cause electrical hazards to people or their livestock nearby. Especially when the power **system** was faulted, the short-circuit current is much higher than the normal current loading. This paper focuses on utilization of efficient computing **techniques** to estimate the magnetic field distribution. Obtained estimate solutions can lead to assessment of electrical hazards for 500-kV power transmission systems. This paper presents an online estimation of magnetic fields for live transmission line right of way worker **using** Generalized Regression **Artificial** **Neural** **Network**.

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coefficient higher than 0.9 was obtained. In addition, with the trial and error method, training results showed that the ANN with three hidden layers has the best performance. Consequently, the developed ANN model for predicting density biodiesel blends is shown in Figure 5 and the training parameters can be found in Table 2. The developed **network** architecture has a 2-3-1 configuration with two neurons in the input layer indicating temperature and volume fraction of biodiesel. Three hidden layers with varying neurons and ten neurons in the output layer representing density are used.

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As mentioned earlier, different structures of the ANN models were employed for estimating VW in order to select the suitable parameter set combination of each model for each crop. The statistical measurements of the results obtained by each ANN models in training, validation and testing sets for different crops individually which are summarized in Table 2. According to Table 2, the best number of neurons in hidden layer for MLP model and the best amount of spread (σ) for RBF and GRNN models are mentioned in column 3. The best percent of all data that were used for each set of training, validation, and testing ANN models have been shown in column 4. Column 7 shows the best **network** among the applied ANN models for each crop.

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