RESEARCH PAPERS

artificial neural network 2013




VGT and EGR Control of Common-Rail Diesel Engines Using an Artificial Neural Network
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In diesel engines, variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) systems are used to increase engine specific power and reduce NOx emissions, respectively. Because the dynamics of both the VGT and EGR are highly nonlinear and 

Familial or Sporadic Idiopathic Scoliosis--classification based on artificial neural networkand GAPDH and ACTB transition profile
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Background Importance of hereditary factors in the etiology of Idiopathic Scoliosis is widely accepted. In clinical practice some of the IS patients present with positive familial history of the deformity and some do not. Traditionally about 90% of patients have been considered 

Finding diversity for building one-day ahead Hydrological Ensemble Prediction System based on artificial neural network stacks
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In this study, we addressed the application of Artificial Neural Networks (ANN) in the context of Hydrological Ensemble Prediction Systems (HEPS). Such systems have become popular in the past years as a tool to include the forecast uncertainty in the decision making 

Serum Biomarkers for Ovarian Cancer by Screening With Surface-Enhanced Laser Desorption/Ionization Mass Spectrometry and the Artificial Neural Network
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 Objective: The purpose of this study was to screen potential serum tumor biomarkers for the 

Developing a Hotel Investment Decision model-An Application of Artificial Neural Network
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ABSTRACT Fast growth of China's hotel industry has led to overdevelopment. Many hotels have been suffering from low occupancy and showing sluggish performance. The purpose of this study is to develop a hotel investment decision model for China's hotel industry using 

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly
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Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the 

Comparison Between an Artificial Neural Network and Logistic Regression in Predicting Long Term Kidney Transplantation Outcome
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Predicting clinical outcome following a specific treatment is a challenge that sees physicians and researchers alike sharing the dream of a crystal ball to read into the future. In Medicine, several tools have been developed for the prediction of outcomes following drug treatment 

Modeling and Forecasting Stock Prices Using an Artificial Neural Network and Imperialist Competitive Algorithm
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ABSTRACT In recent years, computer has become powerful tool for prediction of economical and financial variables. Different techniques of topics related to artificial intelligence, machine learning, and expert systems extended their place in the economic and financial 

Dual Band Microstrip Antenna Design Using Artificial Neural Network
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ABSTRACT This paper presents novel coaxial probe feed, microstrip antenna with dual bandwidth design using a soft computing tool Artificial Neural Network. By varying the position of the feed, dual bandwidths of 8.08% and 8.15% is achieved which is further 

A Review: Influence of electrode geometry and process parameters on surface quality and MRR in EDM using Artificial Neural Network
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ABSTRACT Electrical Discharge Machining (EDM) is a non conventional machining process, where electrically conductive materials are machined by using precisely controlled sparks that occur between an electrode and a work piece in the presence of a dielectric 

Using artificial neural network models for eutrophication prediction
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ABSTRACT Artificial neural network (ANN), a data driven modeling approach, is proposed to predict the water quality indicators of Lake Fuxian, the deepest lake of southwest China. To determine the non-linear relationships between the water quality factors and the 

Analysis on the Stability of Reservoir Soil Slope Based on Fuzzy Artificial Neural Network
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ABSTRACT Owing to the fact that the relation between the reservoir soil slope stability and its influencing factors is complicated and fuzzy, a method-fuzzy neural network to analyze the reservoir soil slope stability is proposed. The method infuses fuzzy reasoning process into 

Optimization of Prediction Error in CO2 Laser Cutting process by Taguchi Artificial Neural Network Hybrid with Genetic algorithm
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ABSTRACT Simulation and prediction of CO2 laser cutting of Perspex glass has been done by feed forward back propagation Artificial Neural Network (ANN). Experimental data of Taguchi orthogonal array L9 was used to train the ANN model. The simulation results 

Optimization of traits to increasing barley grain yield using an artificial neural network
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ABSTRACT The grain yield (Y) of crops is determined by several Y components that reflect positive or negative effects. Conventionally, ordinary Y components are screened for the highest direct effect on Y. Increasing one component tends to be somewhat 

Daily Discharge Forecasting using artificial neural network and Support Vector Machine model
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ABSTRACT Successful management of water resources requires directional, comprehensive and systematic approaches in order to remove consumers need considering accelerated process of water-related problems and increased demands. In this regard, utilization of 

Applying Artificial Neural Network Hadron-Hadron Collisions at LHC
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High Energy Physics (HEP) targeting on particle physics, searches for the fundamental par- ticles and forces which construct the world surrounding us and understands how our uni- verse works at its most fundamental level. Elementary particles of the Standard Model are 

Forecasting Latin-American yield curves: An artificial neural network approach
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ABSTRACT This document explores the predictive power of the yield curves in Latin America (Colombia, Mexico, Peru and Chile) taking into account the factors set by the specifications of NelsonSiegel and Svensson. Several forecasting methodologies are contrasted: an 

Statistical and Artificial Neural Network based Analysis of Fault in an Automobile Engine
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ABSTRACT The paper deals with the problem of fault detection in an automobile engine using acoustic signal. The objective is to categorize the acoustic signals of engines into healthy and faulty state. Acoustic emission signals are generated from automobile engines in both 

A COMPARISON STUDY FOR INTRUSION DATABASE (KDD99, NSL-KDD) BASED ON SELF ORGANIZATION MAP (SOM) ARTIFICIAL NEURAL NETWORK
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ABSTRACT Detecting anomalous traffic on the internet has remained an issue of concern for the community of security researchers over the years. The advances in the area of computing performance, in terms of processing power and storage, have fostered their ability to host 

Real Time Recognition of Handwritten Devnagari Signatures without Segmentation UsingArtificial Neural Network
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ABSTRACT Handwritten signatures are the most commonly used method for authentication of a person as compared to other biometric authentication methods. For this purpose Neural Networks (NN) can be applied in the process of verification of handwritten signatures that 

Solution to the unit commitment problem using an artificial neural network
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ABSTRACT This paper proposes a real-time solution to the unit commitment problem by considering different constraints like ramp-up rate, unit operation emissions, next hours load, and minimum down time. In this method, an optimized trade-off between cost and 

VECTOR QUANTIZATION AND ENHANCED RESILIENT BACKPROPAGATION ARTIFICIAL NEURAL NETWORK FOR INTRUSION CLASSIFICATION
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RS Naoum, ZN Al-Sultani ABSTRACT Network-based computer systems play increasingly vital roles in modern society; they have become the target of intrusions by our enemies and criminals. Intrusion detection system attempts to detect computer attacks by examining various data records 

FINGERPRINT RECOGNITION USING ARTIFICIAL NEURAL NETWORK
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ABSTRACT N Network s The ability of the ANN to learn given patterns makes them suitable for such applications. Fingerprint recognition is one such area that can be used as a means of biometric verification where the ANN can play a critical rule. An ANN can be configured 

OF DROP IMPACT DAMAGE AREA ON WGFRP COMPOSITE LAMINATE THROUGH ACOUSTIC EMISSION DATA USING ARTIFICIAL NEURAL NETWORK
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P Ramasamy, GR Priya ,aksheyaa.com ABSTRACT On-line monitoring of drop impact damage is carried out on composite material through Acoustic Emission technique. The acquired Acoustic Emission (AE) signals during impact test were analysed. The significant AE parameters such as signal strength, counts, 

FPGA Implementation of Artificial Neural Network
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AND, NAND OR and NOR functions have been Synthesized and Implemented on Spartan 3 FPGA. It has been trained with Perceptron Convergence Algorithm. The Implemented Perceptrons have been verified by using Modelsim by creating an exhaustive testbench. 

A Comparison Study of the Performance of the Fourier Transform Based Algorithm and theArtificial Neural Network Based Algorithm in Detecting Fabric Texture
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A Harjoko, S Hartati, D Trisnawarman, UT FTI ,shartati.staff.ugm.ac.id ABSTRACT Two methods, based on digital image processing technique, for detecting fabric texture defect have been developed. In the first method, the detection was based on the statistics of the Fourier spectrum. The statistics used were: the average, the highest pixel 

ARTIFICIAL NEURAL NETWORK AIDED RETINA BASED BIOMETRIC IDENTIFICATION SYSTEM
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ABSTRACT Artificial Neural Network (ANN) s are efficient means of prediction, optimization and recognition. Retina is a unique biometric pattern that can be used as a part of a verification system. An ANN can be configured and trained to handle such variations observed in the 

Artificial Neural Network Based DTC Driver for PMSM
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F Korkmaz, MF akir, I Topaloglu, R Grbz ABSTRACT This paper deals with Direct Torque Control (DTC) of Permanent Magnet Synchronous Motors (PMSM's) with using artificial neural networks. PMSM's are increasing application areas such as traction, electric and hybrid vehicles, aerospace and servo 

Artificial Neural Network Applicability in Business Forecasting
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V Sharma ,ermt.net ABSTRACT The application of computer is involved in almost every field in the modern age. It is used from making bus fare ticket to control the critical process involved in nuclear reactor. The ultimate goal of using computer is to increase speed and accuracy. The computer 

An Artificial neural network (ANN) based solution approach to FMS loading problem
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ABSTRACT In this Paper, the FMS loading problem is solved with the bi-criterion objective to minimize the system unbalance and maximizing the throughput by the use of artificial neural networkin the presence of available machine time and tool slots as constraints. The 

Functional Link Artificial Neural Network for Denoising of Image
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ABSTRACT Digital image denoising is crucial part of image preprocessing. The application of denoising procesn satellite image data and also in television broadcasting. Image data sets collected by image sensors are generally contaminated by noise. Furthermore, noise can 

Modeling the biomass pelleting process using artificial neural network
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A Zafari, MH Kianmehr, R Abdolahzadeh ,icecs2013.ir ABSTRACT Artificial neural networks are powerful tools for modeling of extrusion processes of biomass materials. In order to study the pelleting process, composted municipal solid waste (MSW) pellets were produced under controlled conditions. The aims of the study were to 

Prediction of concrete temperature during curing using regression and artificial neural network
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ABSTRACT Cement hydration plays vital role in temperature development of early-age concrete due to the heat generation. Concrete temperature affects on workability and its measurement is an important element in any quality control program. In this regard, a 

Artificial Neural Network Methodology for Modeling and Resource use Optimization in Rice Yield
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ABSTRACT Artificial neural networks (ANN), viz. Multilayer Perceptron (MLP) using Neuro Solutions 5.0 having 2 numbers of Hidden Layers and 50 Neurons per Hidden Layer has been used here to predict the inputs for a given value of output. Another artificial neural 

Artificial neural network based classification technique for minutiae verification
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S Nuri ,netjournals.org ABSTRACT The theory behind the fingerprint verification based on minutiae matching, was in detail studied. The performance of the developed system was evaluated on database with 2 fingerprints from 20 different people. The test showed that the system and algorithm is 

Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier
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HS Hota, AK Shrivas, SK Singhai ,Training ABSTRACT -Due to increased bandwidth and strong infrastructure available for accessing internet, internet users are growing rapidly. Internet users frequently use e-mail for fast data communication of audio, vedio and textual data but at the same time they are facing 

Realization of Artificial Neural Network in Power systemMicro-grids: A Review
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ABSTRACT Environmental concerns and rising energy consumption has offered new opportunities for public use of renewable energy sources. In the current power and energy scenario, distributed generation (DG) has created a lot of interest across the globe due to 

Classification of Software Projects using k-Means, Discriminant Analysis and Artificial Neural Network
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ABSTRACT An attempt is made in this paper to identify the groups of software development projects which demonstrate the significance of comparable characteristics based on various parameters associated with the Source Lines of Code (SLOC). Initially, software projects 

Sensitivity analysis of the artificial neural network outputs in Friction Stir Lap Joining of Aluminum to Brass
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M Akbari, MH Shojaeefard, M Tahani, F Farhani ,downloads.hindawi.com ABSTRACT In this research, Al-Mg and CuZn34 alloys were lap joined using friction stir welding during the aluminum alloy sheet was placed on the CuZn34. In addition, the mechanical properties of each sample were characterized using shear tests. Scanning 

Artificial Neural Network for Ecosystems Analysis (ANEA), A Neural Ecosystem Analyzer
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ABSTRACT The application of environmental science especially in ecological systems provide many opportunities and challenges for the technologies of modelling, control and analysis. This paper presents an overview of the impact of Artificial Intelligence techniques 

Short Term Load Forecasting using Artificial Neural Network
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AI Melhum, L abd allateef Omar, SA Mahmood ABSTRACT Load forecasting helps an electric utility to make important decisions including decisions on purchasing and generating electric power, load switching, and infrastructure development. Load forecasts are extremely important for developing country like Iraq, 

Modeling of Power Consumption in Turning of Ferrous and Nonferrous Materials usingArtificial Neural Network.
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ABSTRACT Development of artificial neural network (ANN) for prediction of power consumption in the turning of ferrous and nonferrous materials has been the subject of the present paper. ANN was trained through field data obtained on the basis of random plan 

Forecasting of Shares Price by Using Error Backward Propagation Algorithm of Artificial Neural Network (Case Study of Industry is Non-Metallic Mineralization
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ABSTRACT Investment and capital accumulation in stock exchange is an important part of economy. since opening stock exchange markets, investors looking for a way for forecasting shares price. Some software, hardware and different analysis were invented and used for 

APPLICATION OF ARTIFICIAL NEURAL NETWORK IN ATMOSPHERIC REFRACTIVITY PROFILE AT ABUJA, NIGERIA
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GA AGBO, GF IBEH, JE EKPE, DU ONAH ,jbasicphyres-unizik.org ABSTRACT Refractivity profile variation in troposphere is one of the aspects that influences long-distance terrestrial electromagnetic wave propagation and consequently, the performance of communication systems. In this study, Artificial Neural Network (ANN) 

Artificial Neural Network: An Emergent Technology to Disaster Mitigation
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G Dutt, AK Bhatt ,Pragyaan ABSTRACT Natural Disasters like earthquakes, floods, cyclones, epidemics, tsunamis, and landslides in any region, results in loss of life and property. Rapid advancement in technology in all these sectors could be deployed efficiently tackling the challenges 

Artificial neural network approaches for the sorption isotherms, enthalpy and entropy of heat sorption of two types block rubber products
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Y Tirawanichakul, J Tasara, S Tirawanichakul ,rdo.psu.ac.th ABSTRACT Knowledge of the temperature and relative humidity (or water activity) dependence of moisture sorption phenomena of agro-industrial products provides valuable information about changes related to the thermodynamics of the system. Thus the moisture sorption 

Study of Artificial Neural Network Accuracy Compared to Statistical Model of Logistic Regression for Prediction of Iranian Firms Bankruptcy
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ABSTRACT The use of financial ratios for predicting companies' bankruptcy has always been considered by universities and economical institutions especially banks and other financial organizations. In such studies, statistical models like multiple distinctive analyses 

Application of Artificial Neural Network for Modelling of Traffic Noise on Roads in Delhi
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ABSTRACT Pollution is always a matter of concern but when it comes to noise human beings were always ignorant to it. Some of the models developed were UK's CORTN model, USA's FHWA, STACO model in Spain. Most of the models developed have used traffic 

Investigating the Accuracy of Artificial Neural Network for Forecasting Share Price of Various Industries in Tehran's Stock Exchange
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ABSTRACT Accuracy in forecasting the price of share in the stock exchange is important for shareholders and investors because it helps them to decide whether to investment or not and a correct decision will give them the highest return on their investment. The purpose 

ARTIFICIAL NEURAL NETWORK WITH HYBRID TAGUCHI-GENETIC ALGORITHM FOR NONLINEAR MIMO MODEL OF MACHINING PROCESSES
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ABSTRACT This paper developed an artificial neural network (ANN) model with hybrid Taguchi- genetic algorithm (HTGA) for the nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model finds the optimal parameters (ie 

ARTIFICIAL NEURAL NETWORK APPROACH FOR MODELING OF NI (II) ADSORPTION FROM AQUEOUS SOLUTION USING AEGEL MARMELOS FRUIT
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ABSTRACT The rapid increase in population and growth of industrialization worldwide has resulted in deterioration in quality of water. Industrial processes have introduced substantial amounts of potentially toxic heavy metals into the environment. Which are non- 

OF YIELDS IN GLUCOSYLATION OF RGOCALCIFEROL THROUGH RESPONSE SURFACE METHODOLOGY AND ARTIFICIAL NEURAL NETWORK 
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B Manohar, S Divakar ,cibtech.org ABSTRACT Glucosidase from sweet almond catalyzed synthesis of 20-O-(D- glucopyranosyl) ergocalciferol was analyzed using response surface methodology (RSM) and artificial neural network (ANN) analysis. In RSM a central composite rotatable design 

Predicting Energy Requirement for Cooling the Building Using Artificial Neural Network
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R Kumar, RK Aggarwal, JD Sharma, S Pathania ,researchpub.org ABSTRACT This paper explores total cooling load during summers and total carbon emissions of a six storey building by using artificial neural network (ANN). Parameters used for the calculation were conduction losses, ventilation losses, solar heat gain and internal gain. 

Implementation of Fast Artificial Neural Network for Pattern Classification on Heterogeneous System
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DS Bharangar, A Doeger, YK Mittal ABSTRACT Neural networks have been part of an attempt to emulate the learning curve of the human nervous system. Graphics Processing Units (GPUs) that come with a Graphics card have hundreds of processing cores, and have highly parallel architecture. Because 

Application of Artificial Neural Network to Analyze and Predict the Tensile Strength of Shielded Metal Arc Welded Joints under the Influence of External
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ABSTRACT The present study is concerned with the effect of welding current, voltage, welding speed and external magnetic field on tensile strength of shielded metal arc welded mild steel joints. Mild steel plates of 6 mm thickness were used as the base material for 

Optimized FPGA Implementation of an Artificial Neural Network for Function Approximation
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R Elmissaoui, A Sakly, F M'Sahli ,rspublication.com ABSTRACT This paper describes a generalized technique for designing, simulating and implementing an Artificial Neural Network (ANN) using Field Programmable Gate Array (FPGA). For a hardware implementation of the network, FPGA can exploit the parallelism 

PREDICTION OF SURFACE SOIL COLOR USING ETM+ SATELLITE IMAGES AND ARTIFICIAL NEURAL NETWORK APPROACH
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E NOSHADI, HALI BAHRAMI, S ALAVIPANAH ,ecisi.com ABSTRACT Surface soil color (SC) is an important physical soil property. It is frequently used by soil scientists to determine soil characteristics, soil process and soil type. The conventional method of soil color measurement including visual and spectral 

of injection timings on performance and emissions of a biodiesel engine operated on blends of Honge methyl ester and prediction using artificial neural network
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In the present work, biodiesel prepared from Honge oil (Pongamia) was used as a fuel in C. I engine. Performance studies were conducted on a single cylinder four-stroke water-cooled compression ignition engine connected to an eddy current dynamometer. Experiments 

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Networkand Multiple Regression Approach
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YE Shao ,downloads.hindawi.com ABSTRACT Because the volume of currency issued by a country always affects its interest rate, price index, income levels and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. 

Neural Network and Artificial Immune Algorithms for the Classification of Medical Data Series
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W Wajs ,journals.bg.agh.edu.pl We intent to demonstrate that the immune system concept can be used as a computational tool for data classification and prediction. The immune system has several useful ideas from the viewpoint of data manipulation. The immune network theory hypothesizes the activities 

neural networks load forecasting research papers



Mapping neural networks into rule sets and making their hidden knowledge explicit application to spatial load forecasting
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Abstract This paper presents a mathematical transform that maps artificial neural networks into rulebased fuzzy inference systems. This allows one to make explicit the knowledge implicitly captured by a trained neural network. This result is exact and its application is

Short term load forecasting using artificial neural networks for the west of Iran
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ABSTRACT In this study, the use of neural networks to study the design of Short-Term Load Forecasting (STLF) Systems for the west of Iran was explored. The three important architectures of neural networks named Multi Layer Perceptron (MLP), Elman Recurrent

Medium term electric load forecasting using TLFN neural networks
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ABSTRACT This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which

Application of neural networks in short-term load forecasting
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ABSTRACT Artificial neural network is a computational intelligence technique that has found major applications in engineering and science. One of them is to design short-term load forecasting systems (STLF) which due to its complicated and nonlinear nature, the study of

A comparison of Artificial Neural Networks algorithms for short term load forecasting in Greek intercontinental power system
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ABSTRACT The objective of this paper is to compare the performance of different Artificial Neural Network (ANN) training algorithms regarding the prediction of the hourly load demand of the next day in intercontinental Greek power system. These techniques are:(a)

Application and comparison of several artificial neural networks for forecasting the Hellenic daily electricity demand load
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ABSTRACT This paper introduces an approach based on artificial neural networks in order to forecast the Hellenic daily electricity demand load. Several structures, learning algorithms and transfer functions were tested in order to produce a model with the best generalising

Electric load forecasting by neural networks considering various load types
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ABSTRACT This paper proposes an electric load forecasting method by artificial neural networks considering various load types. The proposed method consists of two forecasting steps. In step 1, all load types data of three time zones to characterize the daily load are

Optimization of echo state neural networks for electrical load forecasting
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Abstract. Forecasting abilities of" Echo State" neural networks for energy load were optimized and compared the predictive abilities against results achieved by strong wordwide contest in Eunite Competition# 1. The test data are real, attached to a specific region. The

A Fine Load Forecasting Using Neural Networks and Fuzzy Neural Networks
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ABSTRACT A methodology for short term load forecasting based on artificial neural network and fuzzy logic is presented in this paper. At first this problem is solved using only artificial neural networks with and without temperature effects. Then the proposed method has

Medium to Long-Term Peak Load Forecasting for Riyadh City Using Artificial Neural Networks
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Abstract Load forecasting plays a paramount role in the operation and management of power systems. Accurate estimation of future power demands for various lead times facilitates the task of generating power reliably and economically. The forecasting of future

Neural networks applied to spatial load forecasting in GIS.
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ABSTRACT Quality spatial load forecasting is a major prerequisite for energy distribution systems planning. The load evolution outline depends on the urban expansion and its land usage. This paper presents a methodology for knowledge extraction of the data provided

Electricity load forecasting using artificial neural networks
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ABSTRACT Load forecasting is an essential part of an efficient power system planning and operation. This research work is on short term electricity load forecasting using Artificial Neural Network (ANN) and Ogbomoso a city in Nigeria is considered as a case study.

Short term load forecasting with multilayer perceptron and recurrent neural networks
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Short-term load forecasting (STLF) plays a major role in economic optimization and reliable operation of electric utility companies. It is a vital tool to predict power system loads. Particularly, STLF has an influence on maintenance planning, economic operation of

Peak Load Forecasting Using Optimal Linear Combinations of Artificial Neural Networks
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ABSTRACT A new approach for daily Peak Load forecasting using combinations of trained Artificial Neural Networks (ANNs) is presented in this study. Two different methods constrained and unconstrained are used to identify various combinations of ANNs for

Short Term Load Forecasting Using Articial Neural Networks for the West of Iran
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ABSTRACT In this study, the use of neural networks to study the design of Short-Terrn Load Forecasting (STLF) Systems for the west of Iran was explored. The three important architectures of neural networks named Multi Layer Perceptron (MLP), Elman Recurrent

Short Term Load Forecasting Using Predictive Modular Neural Networks
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Abstract In this paper we present an application of predictive modular neural networks (PREMONN) to short term load forecasting. PREMONNs are a family of probabilistically motivated algorithms which can be used for time series prediction, classification and

Short Term Load Forecasting Using Artificial Neural Networks
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ABSTRACT Load forecasting refers to the prediction of future load conditions based on present or historical data. This is important especially for transmission planning and economic dispatch. In this paper, an Artificial Neural Network (ANN) is trained using

Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks
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ABSTRACT The ability of Artificial Neural Network (ANN) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real concern. As the most applicable network, the ANN with multi-layer back propagation

Load Forecasting Using New Error Measures In Neural Networks
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ABSTRACT Load forecasting plays a key role in helping an electric utility to make important decisions on power, load switching, voltage control, network reconfiguration, and infrastructure development. It enhances the energy-efficient and reliable operation of a

Short term load forecasting using neural networks
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Summary This thesis deals with a project on short term load forecasting with neural networks. It concerns the forecasting of electrical load for several days in advance, which are done on the basis of historical load, weather and calendar variables. The research here is


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