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This paper presents a method to estimate a transformer health condition based on diagnostic tests. A feed forward artificial neural network (FFANN) is used to find the health index of the transformer. The health index is used to find the health condition of the transformer. The training of the FFANN is done using real measurements of 59 working transformers. The testing of the trained neural network...
In this paper a Fuzzy Sliding Mode (FSM) control strategy is proposed and also Genetic Algorithms are employed to find the sliding parameters and membership functions of fuzzy part. Furthermore, due to conflicting between objective functions, means that as one objective function improves, another one deteriorates; there is a set of optimal solutions, well-known as Pareto optimal solutions. Therefore,...
In this study we describe the development of a six Degree of Freedom (6 DOF) pose estimation model of a tracked object and 3D user interface using stereo vision and Infra-Red (IR) cameras in the Matlab/Simulink and C# environments. The raw coordinate values of the IR light sources located on the tracked object are detected, digitized and Bluetooth broadcast by IR cameras and associated circuitry within...
In this study we propose a genetic algorithm to select best features for Web page classification problem to improve accuracy and run time performance of the classifiers. The increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines' performance. To determine whether a Web page belongs...
In this paper, robust Pareto multi-objective optimum design of vehicle vibration model having parameters with probabilistic uncertainties is considered. In order to achieve optimum robust design against probabilistic uncertainties existing in reality, a multi-objective uniform-diversity genetic algorithm (MUGA) in conjunction with Monte Carlo simulation is used for Pareto optimum robust design of...
Wavelet transforms and wavelet based tools are widely used in EEG signal analysis. However, they are mainly used for seizure analysis or feature extraction tools for classification. In this paper, discrete wavelet transform (DWT) is used to answer a critical question of Brain Computer Interface (BCI). For a robot navigation application, if the moments that decision of the user changes are known, then...
Modern manufacturing techniques depend upon systems that produce high volumes with consistent quality in order to ensure maximum productivity. One source of reduced productivity is equipment failure. To minimize these production losses, we propose an intelligent system that is incorporated into the architecture of a machine for detecting the onset of equipment malfunctioning, and to generate corrective...
This paper discusses signature verification and recognition using a new approach that depends on a neural network which enables the user to recognize whether a signature is original or a fraud. The user introduces into the computer the scanned images, modifies their quality by image enhancement and noise reduction techniques, to be followed by feature extraction and neural network training, and finally...
An adaptive neural control scheme based on a new observer applied to quadrotors Helicopter is proposed in this paper. This technique is realized by using two parallel feedforward Artificial Neural Networks (ANN) for each subsystem of the quadrotor. The first one estimates on line the equivalent control term and the second ANN generates observer's corrective term. The main purpose in our work is to...
In this paper, a simple, accurate, fast and reliable black-box modeling is presented for the noise characterization of a microwave transistor using GRNN. GRNN-based modeling is applied to a chosen microwave transistor VMMK 1225 with an optimized training data set and the results are given in details.
Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods...
Feature extraction techniques play a vital part in pattern recognition applications. In order to achieve the best performance in a particular classification problem, the most appropriate feature extractor for the problem is pursued. In this paper, a Pseudo-Zernike Moments based model is used as the feature extractor due to its reliability in illumination and rotation invariant multi-class object classification...
In this paper, I propose a novel hybrid approach of license plate recognition system based on Neural Network and Image Correlation for classification of characters. I used image processing for segmentation. The purpose of this study is to develop a more reliable hybrid system than individual one. The license plate number of the vehicles taken from an acceptable distance from it up to 10m. This hybrid...
In this paper, multi-objective evolutionary Pareto optimal design of Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling of nonlinear systems using input-output data sets with probabilistic uncertainties. In this way, A Monte Carlo Simulation (MCS) is first performed to generate input-output data set using some probabilistic distributions. Multi-objective uniform-diversity genetic...
This paper presents comparison of different classification algorithms which are Linear Discriminant Analysis, Support Vector Machines and Neural networks for EEG signals recorded during mental and motor tasks from a subject. The purpose was to determine an optimum classification scheme that could be efficiently used in a brain-computer interface application. Each EEG data set were first excluded from...
This paper presents a nonlinear controller based on an inverse neural network model of the system under control. The neural controller is implemented as a Radial Basis Function (RBF) network trained with the powerful fuzzy means algorithm. The resulting controller is tested on a nonlinear DC motor control problem and the results illustrate the advantages of the proposed approach.
One of the topics during the last 30 years much research has been allocated to cost estimation for software projects. Important issues in the field of software engineering capabilities estimate size and effort required for development of software projects. Cost estimates must be made at the beginning of the project, and principally at the beginning of projects through cost and set new work requirements...
The paper presents a new methodology of finding and estimating main features of time series to achieve reduction of their components and thus providing the compression of information contained in it keeping the selected features invariant. The presented compression algorithm is based on estimation of truncated time series components in such a way that the spectrum functions of both original and truncated...
Game theory is a branch of mathematics and is a powerful tool for analyzing resource conflicts. In wireless communication systems, power and bandwidth (spectrum) are two fundamental and conflicting resources. Efficient use of these resources in the operation of wireless communication systems is challenging. In this paper, the application of game theory for studying uplink power control in code-division...
In this paper, the effects of tuning the kernel bandwidth for an online LSSVM are investigated. LSSVM is used to obtain a model of the system, and based on this model information, an adaptive PID is designed to control the plant. The kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by discarding redundant or irrelevant features,...
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