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Full-Text Articles in Physical Sciences and Mathematics
Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke
Evaluation Of Artificial Neural Network Methods To Forecast Short-Term Solar Power Generation: A Case Study In Eastern Mediterranean Region, Heli̇n Bozkurt, Ramazan Maci̇t, Özgür Çeli̇k, Ahmet Teke
Turkish Journal of Electrical Engineering and Computer Sciences
Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, …
Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga
Distributed Denial Of Service Attack Detection In Cloud Computing Using Hybridextreme Learning Machine, Gopal Singh Kushwah, Virender Ranga
Turkish Journal of Electrical Engineering and Computer Sciences
One of the major security challenges in cloud computing is distributed denial of service (DDoS) attacks. In these attacks, multiple nodes are used to attack the cloud by sending huge traffic. This results in the unavailability of cloud services to legitimate users. In this research paper, a hybrid machine learning-based technique has been proposed to detect these attacks. The proposed technique is implemented by combining the extreme learning machine (ELM) model and the blackhole optimization algorithm. Various experiments have been performed with the help of four benchmark datasets namely, NSL KDD, ISCX IDS 2012, CICIDS2017, and CICDDoS2019, to evaluate the …
Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali
Effects Of Covid-19 On Electric Energy Consumption In Turkey And Ann-Basedshort-Term Forecasting, Harun Özbay, Adem Dalcali
Turkish Journal of Electrical Engineering and Computer Sciences
: Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes now using the advantages of today's technology. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important to provide a reliable electricity supply for humanity so that people can perform all these services. In this study, the outlook of energy in Turkey was examined. The current energy consumption and investments were examined. Then, the precautions by the government in the …
User Profiling For Tv Program Recommendation Based On Hybrid Televisionstandards Using Controlled Clustering With Genetic Algorithms And Artificial Neuralnetworks, İhsan Topalli, Selçuk Kilinç
User Profiling For Tv Program Recommendation Based On Hybrid Televisionstandards Using Controlled Clustering With Genetic Algorithms And Artificial Neuralnetworks, İhsan Topalli, Selçuk Kilinç
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificial intelligence and the latest technologies developed for the television area is expanded further using controlled clustering with genetic algorithms (CCGA). For this purpose, genetic algorithms (GAs), artificial neural networks (ANNs), and hybrid broadcast broadband television (HbbTV) are combined to get the users' television viewing habits and to create profiles. Then television programs are recommended to the users based on that profiling. The data gathered by the developed HbbTV application for previous studies are reused in this study. These data are employed to cluster users. …
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Improving The Efficiency Of Dnn Hardware Accelerator By Replacing Digitalfeature Extractor With An Imprecise Neuromorphic Hardware, Majid Mohammadi Rad, Omid Sojodishijani
Turkish Journal of Electrical Engineering and Computer Sciences
Mixed-signal in-memory computation can drastically improve the efficiency of the hardware implementing machine learning (ML) algorithms by (i) removing the need to fetch neural network parameters from internal or external memory and (ii) performing a large number of multiply-accumulate operations in parallel. However, this boost in efficiency comes with some disadvantages. Among them, the inability to precisely program nonvolatile memory devices (NVM) with neural network parameters and sensitivity to noise prevent the mixed-signal hardware to perform a precise and deterministic computation. Unfortunately, these hardware-specific errors can get magnified while propagating along with the layers of the deep neural network. In …
Distribution Network Reconfiguration Based On Artificial Networkreconfiguration For Variable Load Profile, Hesham Hanie Youssef, Hazlie Bin Mokhlis, Mohamad Sofian Abu Talip, Mohammad Alsamman, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor
Distribution Network Reconfiguration Based On Artificial Networkreconfiguration For Variable Load Profile, Hesham Hanie Youssef, Hazlie Bin Mokhlis, Mohamad Sofian Abu Talip, Mohammad Alsamman, Munir Azam Muhammad, Nurulafiqah Nadzirah Mansor
Turkish Journal of Electrical Engineering and Computer Sciences
Network reconfiguration is a process to change the open-switches in distribution system for a minimum power loss. In the past, metaheuristic techniques were applied widely for network reconfiguration with consideration of a fixed loading profile. When the loading changes, the current configuration may not be the optimal one. Thus, the technique needs to be executed to find a new optimal configuration based on the latest loading. The process is time-consuming since metaheuristic techniques commonly require high computational times and produces inconsistent results. Therefore, this paper proposes a network reconfiguration technique based on artificial neural network (ANN) for variable loading conditions. …
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Prediction Of Railway Switch Point Failures By Artificial Intelligence Methods, Burak Arslan, Hasan Ti̇ryaki̇
Turkish Journal of Electrical Engineering and Computer Sciences
In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with …
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Performance Tuning For Machine Learning-Based Software Development Effort Prediction Models, Egemen Ertuğrul, Zaki̇r Baytar, Çağatay Çatal, Ömer Can Muratli
Turkish Journal of Electrical Engineering and Computer Sciences
Software development effort estimation is a critical activity of the project management process. In this study, machine learning algorithms were investigated in conjunction with feature transformation, feature selection, and parameter tuning techniques to estimate the development effort accurately and a new model was proposed as part of an expert system. We preferred the most general-purpose algorithms, applied parameter optimization technique (GridSearch), feature transformation techniques (binning and one-hot-encoding), and feature selection algorithm (principal component analysis). All the models were trained on the ISBSG datasets and implemented by using the scikit-learn package in the Python language. The proposed model uses a multilayer …
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Automated Elimination Of Eog Artifacts In Sleep Eeg Using Regression Method, Mehmet Dursun, Seral Özşen, Sali̇h Güneş, Bayram Akdemi̇r, Şebnem Yosunkaya
Turkish Journal of Electrical Engineering and Computer Sciences
Sleep electroencephalogram (EEG) signal is an important clinical tool for automatic sleep staging process. Sleep EEG signal is effected by artifacts and other biological signal sources, such as electrooculogram (EOG) and electromyogram (EMG), and since it is effected, its clinical utility reduces. Therefore, eliminating EOG artifacts from sleep EEG signal is a major challenge for automatic sleep staging. We have studied the effects of EOG signals on sleep EEG and tried to remove them from the EEG signals by using regression method. The EEG and EOG recordings of seven subjects were obtained from the Sleep Research Laboratory of Meram Medicine …
Transformer Incipient Fault Diagnosis On The Basis Of Energy-Weighted Dga Usingan Artificial Neural Network, Md Danish Equbal, Shakeb Ahmad Khan, Tarikul Islam
Transformer Incipient Fault Diagnosis On The Basis Of Energy-Weighted Dga Usingan Artificial Neural Network, Md Danish Equbal, Shakeb Ahmad Khan, Tarikul Islam
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, a transformer incipient fault diagnosis model has been developed with the help of an artificial neural network (ANN), taking into account the difference in the energy required to produce the different fault gases. The key fault gases are indicative of the fault type prevailing in the transformer. However, in conventional studies, the energy difference in fault gas formation is not considered while adopting the key gas method for fault diagnosis. In this work, a weighting factor has been used to take into account this relative difference in energy requirement for various fault gas formations. The fault gas …
A New Approach For Digital Image Watermarking To Predict Optimal Blocks Using Artificial Neural Networks, Raheleh Khorsand Movaghar, Hossein Khaleghi Bizaki
A New Approach For Digital Image Watermarking To Predict Optimal Blocks Using Artificial Neural Networks, Raheleh Khorsand Movaghar, Hossein Khaleghi Bizaki
Turkish Journal of Electrical Engineering and Computer Sciences
In this paper, we propose a novel nonblind digital image watermarking based on discrete wavelet transform and singular value decomposition. This robust scheme takes advantage of artificial neural networks for selecting suitable image blocks in which the watermark signal can be embedded. Local characteristics of the blocks such as luminance and texture sensitivity are the main criteria that the selections are based on. Generally, selection is based on a prediction of the results with the objective of transparency and watermark resilience. In other words, before embedding the water mark signal, it is estimated which blocks would be the best for …
Probabilistic Day-Ahead System Marginal Price Forecasting With Ann For The Turkish Electricity Market, Erdem Özgüner, Osman Bülent Tör, Ali̇ Nezi̇h Güven
Probabilistic Day-Ahead System Marginal Price Forecasting With Ann For The Turkish Electricity Market, Erdem Özgüner, Osman Bülent Tör, Ali̇ Nezi̇h Güven
Turkish Journal of Electrical Engineering and Computer Sciences
This study presents a system day-ahead hourly market clearing price forecasting tool for the day-ahead (DA) market and a system DA hourly marginal price forecasting tool for the real-time market of the Turkish electric market (TEM). These forecasting tools are developed based on artificial neural networks (ANNs). A series of historical price data of the TEM are utilized to model and optimize the ANN structure and to develop the ANN-based price forecasting tool. The methodology used to select the optimum ANN architecture provides the minimum daily mean absolute percentage error for both day-ahead market prices in the TEM. Performances of …
Online Monitoring And Accident Diagnosis Aid System For The Nur Nuclear Research Reactor, Amina Nasrine Allalou, Mohamed Tadjine, Mohamed Seghir Boucherit
Online Monitoring And Accident Diagnosis Aid System For The Nur Nuclear Research Reactor, Amina Nasrine Allalou, Mohamed Tadjine, Mohamed Seghir Boucherit
Turkish Journal of Electrical Engineering and Computer Sciences
This paper deals with the design of a computerized monitoring and diagnosis aid system (CMDAS) for the Nur Nuclear Research Reactor based on real-time plant-specific safety parameters. The CMDAS carries out early detection and identification of accidents that might affect this reactor using supervised neural networks. The graphical programming language LabVIEW is used for creating a human--operator interface, networking, embedding the diagnosis procedure, and handling and storing the data. The methodology presented in this paper can be adapted for any nuclear research reactor.
An Approach Based On Neural Computation To Simulate Transition Metals Using Tight Binding Measurements, Adel Belayadi, Boualem Bourahla, Leila Ait-Gougam, Fawzia Mekideche-Chafa
An Approach Based On Neural Computation To Simulate Transition Metals Using Tight Binding Measurements, Adel Belayadi, Boualem Bourahla, Leila Ait-Gougam, Fawzia Mekideche-Chafa
Turkish Journal of Physics
A theoretical study of neural networks modeling, based on the tight binding approach, is proposed in this study. The aim of the present contribution is to establish a network topology to compute the binding energy parameters of transition metals. However, because of the different types of crystallization fcc, bcc, hcp, and sc of transition metals, neural network topology determination cannot be easily established, i.e. it would not be able to collect the data to feed the neurocomputing model. Hence, in order to overcome this problem, it would be helpful to distinguish one common structure from fcc, bcc, hcp, and sc. …
Short-Term Load Forecasting Without Meteorological Data Using Ai-Based Structures, İdi̇l Işikli Esener, Tolga Yüksel, Mehmet Kurban
Short-Term Load Forecasting Without Meteorological Data Using Ai-Based Structures, İdi̇l Işikli Esener, Tolga Yüksel, Mehmet Kurban
Turkish Journal of Electrical Engineering and Computer Sciences
STLF is used in making decisions about economical power generation capacity, fuel purchasing, safety assessment, and power system planning in order to have economical power conditions. In this study, Turkey's 24-hour-ahead load forecasting without meteorological data is studied. ANN, wavelet transform and ANN, wavelet transform and RBF NN, and EMD and RBF NN structures are used in STLF procedures. Local holidays' historical load data are changed into data with normal day characteristics, and the estimation results of these days are not included in error computation. To obtain more accurate results, a regulation on forecasted loads is proposed. Regulated and unregulated …
An E-Nose-Based Indoor Air Quality Monitoring System: Prediction Of Combustible And Toxic Gas Concentrations, Beki̇r Mumyakmaz, Keri̇m Karabacak
An E-Nose-Based Indoor Air Quality Monitoring System: Prediction Of Combustible And Toxic Gas Concentrations, Beki̇r Mumyakmaz, Keri̇m Karabacak
Turkish Journal of Electrical Engineering and Computer Sciences
A system for monitoring and predicting indoor air quality level is proposed in this paper. The system comprises a computer with a monitoring program and a sensor cell, which has an array of metal oxide gas sensors along with a temperature and humidity sensor. The gas sensors in the cell have been chosen to detect only hydrogen, methane, and carbon monoxide gases. Methane was selected as a representative for indoor combustible gases, and carbon monoxide was used to represent indoor toxic gases. Hydrogen was used as an interfering (and also combustible) gas in the study. A number of experiments were …
Epilepsy Diagnosis Using Artificial Neural Network Learned By Pso, Nesi̇be Yalçin, Gülay Tezel, Ci̇han Karakuzu
Epilepsy Diagnosis Using Artificial Neural Network Learned By Pso, Nesi̇be Yalçin, Gülay Tezel, Ci̇han Karakuzu
Turkish Journal of Electrical Engineering and Computer Sciences
Electroencephalogram (EEG) is used routinely for diagnosis of diseases occurring in the brain. It is a very useful clinical tool in the classification of epileptic seizures and the diagnosis of epilepsy. In this study, epilepsy diagnosis has been investigated using EEG records. For this purpose, an artificial neural network (ANN), widely used and known as an active classification technique, is applied. The particle swarm optimization (PSO) method, which does not need gradient calculation, derivative information, or any solution of differential equations, is preferred as the training algorithm for the ANN. A PSO-based neural network (PSONN) model is diversified according to …
Forecasting The Day-Ahead Price In Electricity Balancing And Settlement Market Of Turkey By Using Artificial Neural Networks, Mehmet Ali̇ Kölmek, İsa Navruz
Forecasting The Day-Ahead Price In Electricity Balancing And Settlement Market Of Turkey By Using Artificial Neural Networks, Mehmet Ali̇ Kölmek, İsa Navruz
Turkish Journal of Electrical Engineering and Computer Sciences
In determination of electric energy price, most price information coming from bilateral contracts is effective, but the importance of the spot market (pool market) price cannot be ignored. Forecasting the spot market price is very crucial, especially for companies actively participating in the spot market and giving purchase and sale bids. An artificial neural network is a way frequently used for price forecasting research. In this study, simulation studies about price modeling via artificial neural networks and proper artificial neural network configurations are examined. After selection of different network topologies and parameters, attempts are made to observe network performance by …
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
A Comparative Study Of Two Different Fpga-Based Arrhythmia Classifier Architectures, Ahmet Turan Özdemi̇r, Kenan Danişman
Turkish Journal of Electrical Engineering and Computer Sciences
Early diagnosis of dangerous heart conditions is very important for the treatment of heart diseases and for the prevention of sudden cardiac death. Automatic electrocardiogram (ECG) arrhythmia classifiers are essential to timely diagnosis. However, most of the medical diagnosis systems proposed in the literature are software-based. This work focused on the hardware implementation of a mobile artificial neural network (ANN)-based arrhythmia classifier that is implemented on a field programmable gate array (FPGA) as a single chip solution, as an alternative to various software models of ANNs. Due to the parallel nature of ANNs, hardware implementation of ANNs needs a large …
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Comparison Of Different Methods For Determining Diabetes, Mehmet Recep Bozkurt, Ni̇lüfer Yurtay, Zi̇ynet Yilmaz, Cengi̇z Sertkaya
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, the Pima Indian Diabetes dataset was categorized with 8 different classifiers. The data were taken from the University of California Irvine Machine Learning Repository's web site. As a classifier, 6 different neural networks [probabilistic neural network (PNN), learning vector quantization, feedforward networks, cascade-forward networks, distributed time delay networks (DTDN), and time delay networks], the artificial immune system, and the Gini algorithm from decision trees were used. The classifier's performance ratios were studied separately as accuracy, sensitivity, and specificity and the success rates of all of the classifiers are presented. Among these 8 classifiers, the best accuracy and …
Comparison Of Ais And Fuzzy C-Means Clustering Methods On The Classification Of Breast Cancer And Diabetes Datasets, Seral Özşen, Rahi̇me Ceylan
Comparison Of Ais And Fuzzy C-Means Clustering Methods On The Classification Of Breast Cancer And Diabetes Datasets, Seral Özşen, Rahi̇me Ceylan
Turkish Journal of Electrical Engineering and Computer Sciences
Data reduction is an indispensable part of pattern classification processes in many cases. If the number of samples is excessive, sample reduction or data reduction algorithms can be used for an effective processing time and reliable successive results. Many methods have been used for data reduction. Fuzzy c-means is one of these methods and it is widely used in such applications as clustering algorithms. In this study, we applied a different clustering algorithm, an artificial immune system (AIS), for the data reduction process. We realized the performance evaluation experiments on the standard Chainlink and Iris datasets, while the main application …
A Method Based On The Van Der Hoven Spectrum For Performance Evaluation In Prediction Of Wind Speed, Eli̇f Kaya, Burak Barutçu, Şükran Si̇bel Menteş
A Method Based On The Van Der Hoven Spectrum For Performance Evaluation In Prediction Of Wind Speed, Eli̇f Kaya, Burak Barutçu, Şükran Si̇bel Menteş
Turkish Journal of Earth Sciences
Development of techniques for accurate assessment of wind power potential at a site is very important for the planning and establishment of a wind energy system. The most important defining character of the wind and the problems related with it lie in its unpredictable variation. Van der Hoven constructed a wind speed spectrum using short-term and long-term records of wind in Brookhaven, NY, USA, in 1957 and showed the diurnal and turbulent effects. His spectrum suggests that there is a substantial amount of wind energy in 1-min periodic variations. The aim of this paper is to evaluate the results of …
Performance Evolution Of A Newly Developed General-Use Hybrid Ais-Ann System: Aaa-Response, Seral Özşen, Sali̇h Güneş
Performance Evolution Of A Newly Developed General-Use Hybrid Ais-Ann System: Aaa-Response, Seral Özşen, Sali̇h Güneş
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, we have developed a nonlinear recognition system in the artificial immune systems (AIS) field named `AaA-response (artificial neural network (ANN)-aided AIS-response)', which is different from previous AIS methods in that it uses a different modeling strategy in the formation of the memory response. Because it also uses ANNs in the determination of the correct output, it can be seen as a hybrid system that involves AIS and ANN approaches. Unlike the other AIS methods, AaA-response uses multiple system units (or antibodies) to form an output for a presented input. This property gives the proposed system the ability …
Training Data Optimization For Anns Using Genetic Algorithms To Enhance Mppt Efficiency Of A Stand-Alone Pv System, Ahmet Afşi̇n Kulaksiz, Ramazan Akkaya
Training Data Optimization For Anns Using Genetic Algorithms To Enhance Mppt Efficiency Of A Stand-Alone Pv System, Ahmet Afşi̇n Kulaksiz, Ramazan Akkaya
Turkish Journal of Electrical Engineering and Computer Sciences
Maximum power point tracking (MPPT) algorithms are used to force photovoltaic (PV) modules to operate at their maximum power points for all environmental conditions. In artificial neural network (ANN)-based algorithms, the maximum power points are acquired by designing ANN models for PV modules. However, the parameters of PV modules are not always provided by the manufacturer and cannot be obtained readily by the user. Experimental measurements implemented in the overall PV system may be used to obtain the ANN dataset. One drawback of this method is that the generalization ability of the neural network usually degrades and some data reducing …
Fully Parallel Ann-Based Arrhythmia Classifier On A Single-Chip Fpga: Fpaac, Ahmet Turan Özdemi̇r, Kenan Danişman
Fully Parallel Ann-Based Arrhythmia Classifier On A Single-Chip Fpga: Fpaac, Ahmet Turan Özdemi̇r, Kenan Danişman
Turkish Journal of Electrical Engineering and Computer Sciences
Recognition of cardiac arrhythmias by electrocardiogram (ECG) is an important issue for diagnosis of cardiac abnormalities. Many studies on recognition of cardiac arrhythmias by ECG, using various techniques, have been performed in the past 20 years. Artificial neural networks (ANNs) are the most widely used tool in medical diagnosis systems (MDS) because of their powerful prediction characteristics. An ANN model is inspired by real biological neural networks, with a parallel structure that is potentially fast for computation. However, the suggested ANN architectures in the literature can only be run sequentially, on powerful processors, due to their complexity. Our approach enables …
An Ann Based Approach To Improve The Distance Relaying Algorithm, Hassan Khorashadi Zadeh, Zuyi Li
An Ann Based Approach To Improve The Distance Relaying Algorithm, Hassan Khorashadi Zadeh, Zuyi Li
Turkish Journal of Electrical Engineering and Computer Sciences
This paper presents an artificial neural network- (ANN) based approach to improve the performance of the distance relaying algorithm. The proposed distance relay uses magnitudes of voltages and currents as input signals to find fault locations. In this approach, an ANN has been included in the protection algorithm as an extension of the existing methods, which improves the reliability of the protection operation. The design procedure of the proposed relay is presented in detail. Simulation studies are performed and the influence of changing system parameters, such as fault resistance and source impedance, is studied. Performance studies show that the proposed …
Artificial Neural Design Of Microstrip Antennas, Nurhan Türker, Fi̇li̇z Güneş, Tülay Yildirim
Artificial Neural Design Of Microstrip Antennas, Nurhan Türker, Fi̇li̇z Güneş, Tülay Yildirim
Turkish Journal of Electrical Engineering and Computer Sciences
A general design procedure is suggested for microstrip antennas using artificial neural networks and this is demonstrated using rectangular patch geometry. In this design procedure, synthesis is defined as the forward side and then analysis as the reverse side of the problem. Worked examples are given using the most efficient materials.
Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik
Differentiating Type Of Muscle Movement Via Ar Modeling And Neural Network Classification, Beki̇r Karlik
Turkish Journal of Electrical Engineering and Computer Sciences
The aim of this study is to classify electromyogram (EMG) signals for controlling multifunction proshetic devices. An artificial neural network (ANN) implementation was used for this purpose. Autoregressive (AR) parameters of $a_1, a_2, a_3, a_4$ and their signal power obtained from different arm muscle motions were applied to the input of ANN, which is a multilayer perceptron. At the output layer, for 5000 iterations, six movements were distinguished at a high accuracy of 97.6%.