Sharif Digital Repository

The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is... 

A novel hybrid algorithm based on a genetic algorithm and particle swarm optimization to design a fuzzy neural network, named self-organizing fuzzy neural network based on GA and PSO (SOFNNGAPSO), to implement Takagi-Sugeno (TS) type fuzzy models is proposed in this paper. The proposed algorithm, as a new hybrid algorithm, consists of two phases. A tuning based on TS's fuzzy model is applied to identify the fuzzy structure, and also a fuzzy cluster validity index is utilized to determine the optimal number of clusters. To obtain a more precision model, GA and PSO are performed to conduct fine tuning for the obtained parameter set of the premise parts and consequent parts in the... 

Wireless Ad-hoc Networks have been proposed for variety of applications. In this paper our goal is to find crisis points in the roads, such as accident locations, and asphalt-destruction points, which may cause traffics along the roads. The applied method for traffic prediction is based on back propagation algorithm. In this paper the length of packets are assumed to be constant. Along the road the traffic is modeled by a Poisson Process. With this model we can produce packets. The number of packets which are sending from a typical node to the other nodes is controlled by a fuzzy system. By appropriate training of a neural network, we have shown that, having the traffic packets at time (t)... 

This paper presents an approach that integrates clinical methods with Neuro-Fuzzy system in order to diagnose Neonatal Jaundice in newborns. First, a fuzzy logic system designed with medical rules to model the uncertainty that exists in medical diagnosis. Then a fuzzy neural network with an evolutionary learning helps the system to learn the new data gained from the patient and to help the fuzzy system to update itself in an online manner. By combining the aforementioned systems, the proposed approach can help physicians to diagnose jaundice with low risk cost associated with this disease. © 2006 IEEE  

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... 

The purpose of this article is to develop a new method to estimate annual energy output for a given wind turbine in any region which should be easy to use and has satisfactory accuracy. To do this, hourly wind speeds of 25 different stations in Netherlands, output power curve of S47 wind turbine and fuzzy modeling techniques and artificial neural networks were used and a model is developed to estimate annual energy output for S47 wind turbine in different regions. Since this model has three inputs (average wind speed, standard deviation of wind speed, and air density of that region), this model is easy to use. The accuracy of this method is compared with the accuracy of conventional methods... 

The neutron noise is defined as the stationary fluctuation of the neutron flux around its mean value due to the induced perturbation in the reactor core. The neutron noise analysis may be useful in many applications like noise source reconstruction. To identify the noise source, calculated neutron noise distribution of the detectors is used as input data by the considered unfolding algorithm. The neutron noise distribution of the VVER-1000 reactor core is calculated using the developed computational code based on Galerkin Finite Element Method (GFEM). The noise source of type absorber of variable strength is considered in the calculation. The computational code developed based on An Adaptive... 

Various systems with nonlinearity can be modeled by utilizing uncertain linear-in-parameter models. In this paper, the uncertain parameters are in the form of Z-number coefficients. Fuzzy equations are utilized to represent the models of the uncertain nonlinear systems. The solutions associated with fuzzy equations are considered to be controllers while the desired references are outputs. The existence condition associated with the solution is laid down. Two various structure of neural networks are applied for approximating solutions of fuzzy equations with Z-number coefficients. The suggested techniques are validated by implementing an example. © 2018 The Authors. Published by Elsevier B.V  

This study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (Rv), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R2) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model. © 2018, © 2018 Taylor & Francis... 

Untethered small-scale robots can accomplish tasks which are not feasible by conventional macro robots. In the current research, we have designed and fabricated a miniature magnetic robot actuated by an external magnetic field. The proposed robot has two coaxial wheels and one magnetic dipole which is capable of rolling and moving on the surface by variation in the direction of magnetic field. To generate the desired magnetic field, a Helmholtz electromagnetic coil is manufactured. To steer the robot to the desired position, at first the robot dynamics is investigated, and subsequently a controller based on a neuro-fuzzy network has been designed. Finally, the proposed controller is... 

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural... 

Ink drop spread (IDS) is the engine of an active learning method, which is the methodology of soft computing. IDS, as a pattern-based processing unit, extracts useful information from a system that is subjected to modeling. In spite of its excellent potential to solve problems such as classification and modeling compared with other soft-computing tools, finding its simple and fast hardware implementation is still a challenge. This paper describes a new hardware implementation of the IDS method that is based on the memristor crossbar structure. In addition to simplicity, being completely real time, having low latency, and the ability to continue working properly after the occurrence of power... 

An integrated fuzzy logic-neural network methodology is presented as a mean to improve the reconstruction of the performance map of axial compressors and fans. The learning capability of artificial neural network technique is integrated to the knowledge aspect of fuzzy inference system to offer enhanced prediction capabilities compared to using a single methodology independently. The proposed technique incorporates information of experimental data on surge, operating, and choke lines at any arbitrary but fixed rotational speed. A comparison of the predicted results with experimental data reveals a very good agreement. The proposed technique has the capability to model the nonlinear surge... 

In this study, the problem of variable bit rate (VBR) video traffic prediction is addressed. VBR traffic prediction is necessary in dynamic bandwidth allocation for multimedia quality of service control strategies. Autoregressive (AR) models have been widely used in VBR traffic prediction where the least mean square (LMS)-based methods were utilised for parameter estimation. However, they are ineffective when the traffic is dynamic in nature. In this study, using the Brock, Dechert, and Scheinkman (BDS) test, it is shown that the video traffic is non-linear. Kernel is an efficient tool to convert non-linear data into linear one in a higher-dimensional space. The kernel LMS (KLMS) method is... 

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects... 

Coordination strategies in large-scale systems are mainly based on two principles: interaction prediction and interaction balance. Using these principles, Model coordination and Goal coordination were proposed. The interactions in the first method and the Lagrangian coefficients in the second method were considered as coordination parameters. In this paper, the concept of coordination is introduced within the framework of two-level large-scale systems and a new intelligent approach for Model coordination is introduced. For this purpose, the system is decomposed into several subsystems, and the overall problem is considered as an optimization problem. With the aim of optimization, the control... 

In stressful situations such as severe accidents in nuclear power plants, operators need support tools to ease decision making in the selection of accident management measures. Following the Three Mile Island (TMI) accident in 1979, the first severe accident in a nuclear power plant, Accident Management Support Tools (AMSTs) were extensively developed and installed in a number of nuclear power plants. Lessons learned from the Fukushima accident highlighted the importance of accident management in mitigation severe accidents and suggested the reconsideration of accident management programs, which in turn created the need for AMSTs adaption and modernization. This paper provides the first... 

Developing a computational model for studying tribological performance is essential for computing accurate life cycle of various materials. Caused by the existence of complicated and nonlinear interactions between material surfaces, exact modeling of wear behavior is very difficult. Artificial intelligence (AI) can be used in distinguishing similar patterns in experimental data and predictive modeling of a certain material’s wear behavior. In this paper, artificial neural networks (ANNs) approach, adaptive neural-based fuzzy inference system (ANFIS) technique, and fuzzy clustering method (FCM) are used to develop a simple, accurate, and applicable model for predicting the wear behavior of... 

As noteworthy properties of amine aqueous solutions, the densities and viscosities of aqueous N-Methyldiethanolamine (MDEA) solutions and mixtures of MDEA with 2-Amino-2-methyl-1-propanol (AMP), Diisopropanolamine (DIPA), Monoethanolamine (MEA), and Diethanolamine (DEA) were estimated under CO2 gas loading using Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Artificial Neural Network (MLPANN), Support Vector Machine (SVM), and Least Square Support Vector Machine (LSSVM). The density and viscosity were estimated as a function of temperature, CO2 loading, pressure, and molecular weight of mixtures. In this regard, the actual data points were collected from the... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of...