Sharif Digital Repository

The author utilized neuro-fuzzy based group method of data handling (NF-GMDH) to predict the local scour depth around pile groups under clear-water conditions. They collected the datasets from literature. To predict the local scour by using NF-GMDH, nine dimensional parameters were considered to define a functional relationship between input and output variables. The results of NF- GMDH networks were compared with that of the empirical equations. However, the collected datasets for pile group scouring, the method of implementing the empirical formula to calculate scour depth, and using the equation of Sheppard et al. (2004) suggested for single pier to predict local scouring around pile... 

In this article, I have tried to design an intelligent system which can separate and classify perfect and defective circular plates according to their size. After preprocessing, specifications of defects and size are determined through image processing, and finally, a system is proposed through perceptron neural networks methods, neuro fuzzy method, and Lvq to separate these products on basis of their size and defects. In the designing of this system, when input and its related intend is obvious before training network, perceptron neural networks give more exact results. If input and its related output have been clarified but the output have been related to some sub-inputs, lvq method is... 

Forecasting is a challenging problem in highly nonlinear dynamic systems. The main goal in development of forecasting models in complex systems is to produce a model that can accurately behave similar to the main system. In problems such as air pollution forecasting, the presence of uncertainties and nonlinearities affects the model's precision. In this paper, ozone concentration, which is well-known as an index for air pollution is forecasted using neuro-fuzzy models. Causal variables are integrated in the models in order to enhance the model's performance. The results are compared to a fuzzy logic approach to demonstrate reliability and accuracy of the proposed model using real observed... 

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our... 

Lateral spreading generated by earthquake induced liquefaction, is a major cause for significant damage to the engineered structures, during earthquakes. Knowing the amount of displacement which is likely to occur due to the lateral spreading, will lead to better construction policies, and will reduce unexpected damages. A Neuro-Fuzzy model based on subtractive clustering is developed to predict the amount of lateral spreading expected to occur due to an earthquake. A large database containing the case histories of observed lateral spreading during seven major earthquakes of the past is used for training and evaluating the models. The results of this study show that Neuro-Fuzzy method serves... 

Magnetic flux leakage is the most widely used method for oil and gas pipeline non destructive testing. The saturation level of the sample under test has a significant effect on its efficiency; therefore, the magnetizing yoke requires an elaborate design. The finite element method is the conventional approach used for this purpose, but it is very time consuming. In this article, a neuro-fuzzy method is presented to model the behavior of the magnetizing yoke. Modeling a few different designs with the finite element method and using the results for training the neuro-fuzzy model eradicates the necessity of modeling a huge number of designs with the finite element method. The acquired... 

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes... 

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement... 

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we presented nonlinear predictor and simulator models for a real cement... 

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes... 

Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based... 

In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor cross-bar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits.... 

Today the need for fault diagnosis in polymer electrolyte membrane fuel cells (PEMFCs) is felt more than ever to increase the useful life and durability of the cell. The present study proposes an indirect in-situ experimental-based algorithm for diagnosing the moisture content issues in a three-cell stack. Three adaptive neuro-fuzzy inference systems (ANFIS) approximate the system outputs (cells voltages, cathodic and anodic pressure drop) in normal conditions. The values of Pearson's correlation coefficients (0.998, 0.983, and 0.995 for outputs, respectively) show the high quality of the modeling. In unknown operating conditions, the residuals of experimental and ANFIS values are compared... 

The disguise of traditional monopolistic electricity markets into deregulated competitive ones has made 'price forecasting' a crucial strategy for both producers and consumers: for the producers, to maximize their profit and hedge against price volatilities and for the consumers to manage their utility. Electricity price forecasting has thus emerged as a progressive field of study and numerous researches have been conducted to improve and optimize the price forecast methods. This paper proposes a precise and computationally efficient method to perform price forecasting in deregulated power markets. A locally linear neuro-fuzzy model is developed for price forecasting. The model is trained by... 

The stability issue of mobile manipulators, particularly when the end-effector and the vehicle have to follow a predefined trajectory (for some special duties like painting a plane or carrying a light load), is a crucial subject and needs special attention. In this paper, by utilizing the manipulator compensation motions, the instantaneous proper configuration for a redundant mobile robot is determined. A fast methodology taking into account the dynamic interaction between the manipulator and the vehicle is proposed for enhancing the tipover stability (i.e. stability against overturning) of the mobile manipulator by employing the soft computing approach including a genetic algorithm, neural... 

In this paper, first we present a new explanation for the relationship between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. This shows us that neural networks are working in the same way as logical circuits when the connection between them is through the fuzzy logic. However, themain difference between them is that logical circuits can be constructed without using any kind of optimization-based learning methods. Based on these results, we propose a new neuro-fuzzy computing system. As verified by simulation results, it can effectively be implemented on the memristor crossbar structure and... 

In this paper an approach based on system identification is used for fault detection in a nuclear reactor. A continuous-time Extended Kalman Filter (EKF) is presented, which allows the parameters of the nonlinear system to be estimated. Also a fault tolerant control system is designed for the nuclear reactor during power changes operation. The proposed controller is an adaptive critic-based neuro-fuzzy controller. Performance of the controller in terms of transient response and robustness against failures is very good and considerable  

Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the... 

Human bodies use the electrical currents to make the muscles move. Disconnection of the electrical signals between the brain and the muscles as a result of spinal cord injuries, causes paralysis below the level of injury. Functional electrical stimulation (FES) is used to stimulate the peripheral nerves of the disabled limbs. The level of these electrical signals should be selected so that the desired tasks are done successfully. Applying the appropriate controller which can result a human like behaviour and the accomplishment of the desired tasks has become a significant research area. In this paper, the multi-input multi-output (MIMO) musculoskeletal model of human arm with six muscles is...