...Development of Software Packages for neural networks using qualitative visualization *Dr.Osamah Abdulgader Al- Rababah and ** Alaa Ismat Al-Attili ***Dr.khalid Shraidah *King Abdul Aziz University- KSA-JEDDA-21931 ALKamell College – Computer Department **AL-Zahra College for Women (University College) - IT Department | | ***King Abdul Aziz University- KSA-JEDDA-21931 ALKamell College – Computer Department | | Abstract The paper suggests an improved neuro-simulators block diagram, which not only extends the functionality of the software package, but also allows rapid learning in the development of the package due to the qualitative visualization of different phases of network development. Key words: Information Technology , Artificial Neural Networks (NN), Network Operation Visualization Tools , Artificial Neuron . 1. Introduction In the last few decades interest has risen in a new application area of applied mathematics, information technology specializing in artificial Neural Networks (NN). The relevance of the research in this line is confirmed by the various spheres of the NN application, which are being widely used now to solve applied tasks. NNR was actively used in applications where conventional algorithmic approach to solving problems turned out to be ineffective. Some of those applications: automation of pattern recognition process, adaptive control, functional approximation forecasting, creation of expert...
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...investigative purposes. Keeping in mind the end goal to beat that, PC vision system is used, which incorporates picture handling calculation. Manual distinguishing proof for the leaves sometimes prompts more imperative qualification because of twin. Consequently, validation of leaves is greatly required for restorative purposes. The main answer for this issue is Image Processing. The components of a leaf are removed utilizing GLCM Technique and a nearest match is taken to distinguish which class it fits in with. In this Paper, the execution examination of counterfeit neural system calculation and Levenberg Marquardt Algorithm is compared. In the Levenberg Marquardt calculation, the damping parameter assumes an essential part which accomplishes for the more noteworthy joining rate. Index Terms—Artificial Neural Network, GLCM Technique,Identification of leaves,Levenberg-Marquardt Algorithm,Probabilistic Neural Network. ———————————————————— 1 Introduction L EAVESconstitute the most basic part of a plant. Leaf recognizable...
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...EEL5840: Machine Intelligence Introduction to feedforward neural networks Introduction to feedforward neural networks 1. Problem statement and historical context A. Learning framework Figure 1 below illustrates the basic framework that we will see in artificial neural network learning. We assume that we want to learn a classification task G with n inputs and m outputs, where, y = G(x) , (1) x = x1 x2 … xn T and y = y 1 y 2 … y m T . (2) In order to do this modeling, let us assume a model Γ with trainable parameter vector w , such that, z = Γ ( x, w ) (3) where, z = z1 z2 … zm T . (4) Now, we want to minimize the error between the desired outputs y and the model outputs z for all possible inputs x . That is, we want to find the parameter vector w∗ so that, E ( w∗ ) ≤ E ( w ) , ∀w , (5) where E ( w ) denotes the error between G and Γ for model parameter vector w . Ideally, E ( w ) is given by, E(w) = ∫ y – z 2 p ( x ) dx (6) x where p ( x ) denotes the probability density function over the input space x . Note that E ( w ) in equation (6) is dependent on w through z [see equation (3)]. Now, in general, we cannot compute equation (6) directly; therefore, we typically compute E ( w ) for a training data set of input/output data, { ( x i, y i ) } , i ∈ { 1, 2, …, p } , (7) where x i is the n -dimensional input vector, x i = x i 1 x i 2 … x in T (8) x2 y2 … … Unknown mapping G xn ym z1 z2 Trainable model Γ … zm -1- model outputs y1 … inputs x1...
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...Title :Wind speed prediction using Artificial Neural Network (ANN) Abstract : The crisis of fossil based fuel around the world has led to the research of Renewable Energy sources. One of the oldest sources of Renewable energy was using the wind to generate electrical or mechanical power using windmills. To use it efficiently the wind speed which determines the wind power must be known beforehand. Wind speed is a random variable depending on meteorological variables like atmospheric pressure,temperature,relative humidity & such. Methods that are currently being applied to predict wind speed are Statistical, Intelligent systems, Time series, Fuzzy logic, neural networks.Our focus will be on using Artificial Neural Network to predict the wind speed in daily basis in this report. Chapter 1 1.1 Introduction Bangladesh has a 724 lm long coastal area where south-westerly tradewind& sea breeze makes the usage of wind as a renewable energy source very visible. But, not much systematic wind study has been made, adequate information on the wind speed over the country and particularly on wind speeds at hub heights of wind machines is not available. A previous study (1986) showed that for the wind monitoring stations of Bangladesh Meteorological Department (BMD) the wind speed is found to be low near the ground level at heights of around 10 meter. Chittagong – Cox’s Bazar seacoast and coastal off-shore islands appeared to have better wind speeds. Measurements...
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...NEURAL NETWORKS by Christos Stergiou and Dimitrios Siganos | Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. Contents: 1. Introduction to Neural Networks 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison 2. Human and Artificial Neurones - investigating the similarities 2.1 How the Human Brain Learns? 2.2 From Human Neurones to Artificial Neurones 3. An Engineering approach 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron 4. Architecture of neural networks 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons 5. The Learning Process 5.1 Transfer Function 5.2 An Example to illustrate the above teaching procedure 5.3 The Back-Propagation Algorithm 6. Applications of neural networks 6.1 Neural networks in practice 6.2 Neural networks in medicine 6.2.1 Modelling and Diagnosing the Cardiovascular...
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...ARTIFICIAL NEURAL NETWORKS METHODOLOGICAL ADVANCES AND BIOMEDICAL APPLICATIONS Edited by Kenji Suzuki Artificial Neural Networks - Methodological Advances and Biomedical Applications Edited by Kenji Suzuki Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ivana Lorkovic Technical Editor Teodora Smiljanic Cover Designer Martina Sirotic Image Copyright Bruce Rolff, 2010. Used under license from Shutterstock.com First published March, 2011 Printed in...
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...A Review of ANN-based Short-Term Load Forecasting Models Y. Rui A.A. El-Keib Department of Electrical Engineering University of Alabama, Tuscaloosa, AL 35487 Abstract - Artificial Neural Networks (AAN) have recently been receiving considerable attention and a large number of publications concerning ANN-based short-term load forecasting (STLF) have appreared in the literature. An extensive survey of ANN-based load forecasting models is given in this paper. The six most important factors which affect the accuracy and efficiency of the load forecasters are presented and discussed. The paper also includes conclusions reached by the authors as a result of their research in this area. Keywords: artificial neural networks, short-term load forecasting models Introduction Accurate and robust load forecasting is of great importance for power system operation. It is the basis of economic dispatch, hydro-thermal coordination, unit commitment, transaction evaluation, and system security analysis among other functions. Because of its importance, load forecasting has been extensively researched and a large number of models were proposed during the past several decades, such as Box-Jenkins models, ARIMA models, Kalman filtering models, and the spectral expansion techniques-based models. Generally, the models are based on statistcal methods and work well under normal conditions, however, they show some deficiency in the presence of an abrupt change in environmental or sociological variables...
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...http://ml.memect.com Contents 1 Artificial neural network 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Improvements since 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.1 Network function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3.2 Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.3 Learning paradigms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.4 Learning algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Employing artificial neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.1 Real-life applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5.2 Neural networks and neuroscience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.6 Neural network software . . . . . . . . . . . . . ....
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...– MGT 501 Neural Network Technique Outline * Overview ………………………………………………………….……… 4 * Definition …………………………………………………4 * The Basics of Neural Networks……………………………………………5 * Major Components of an Artificial Neuron………………………………..5 * Applications of Neural Networks ……………….9 * Advantages and Disadvantages of Neural Networks……………………...12 * Example……………………………………………………………………14 * Conclusion …………………………………………………………………14 Overview One of the most crucial and dominant subjects in management studies is finding more effective tools for complicated managerial problems, and due to the advancement of computer and communication technology, tools used in management decisions have undergone a massive change. Artificial Neural Networks (ANNs) is an example, knowing that it has become a critical component of business intelligence. The below article describes the basics of neural networks as well as some work done on the application of ANNs in management sciences. Definition of a Neural Network? The simplest definition of a neural network, particularly referred to as an 'artificial' neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen who defines a neural network as follows: "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs."Neural Network Primer: Part...
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...Oil Price Prediction using Artificial Neural Networks Author: Siddhant Jain, 2010B3A7506P Birla Institute of Technology and Science, Pilani Abstract: Oil is an important commodity for every industrialised nation in the modern economy. The upward or downward trends in Oil prices have crucially influenced economies over the years and a priori knowledge of such a trend would be deemed useful to all concernd - be it a firm or the whole country itself. Through this paper, I intend to use the power of Artificial Neural Networks (ANNs) to develop a model which can be used to predict oil prices. ANNs are widely used for modelling a multitude of financial and economic variables and have proven themselves to be a very powerful tool to handle volumes of data effectively and analysing it to perform meaningful calculations. MATLAB has been employed as the medium for developing the neural network and for efficiently handling the volume of calculations involved. Following sections shall deal with the theoretical and practical intricacies of the aforementioned model. The appendix includes snapshots of the generated results and other code snippets. Artificial Neural Networks: Understanding To understand any of the ensuing topics and the details discussed thereof, it is imperative to understand what actually we mean by Neural Networks. So, I first dwell into this topic: In simplest terms a Neural Network can be defined as a computer system modelled on the human brain and nervous system...
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...Estimation of diabetic retinopathy with artery/vein classification in retinal images using Artificial Neural Network Leshmi Satheesh M.Tech Student,Dept. of Electronics & Communication Mohandas College of Engineering, Kerala University Trivandrum-695541, Kerala, India Email: leshmi24@gmail.com Abstract—Diabetic retinopathy is the single largest explanation for sight loss and visual impairment in eighteen to sixty five year olds . Damage of blood vessels in the eye and the formation of lesions in the retina are the earliest signs of diabetic retinopathy. Efficient image processing and analysis algorithms have to be developed for the automated screening programs to work robustly and effectively. For the detection of vascular changes...
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...TURBMW06_013234761X.QXD 3/7/07 8:07 PM ONLINE CHAPTER Page 1 Neural Networks 6 for Data Mining Learning Objectives ◆ Understand the concept and different types of artificial neural networks (ANN) ◆ Learn the advantages and limitations of ANN ◆ Understand how backpropagation neural networks learn ◆ Understand the complete process of using neural networks ◆ Appreciate the wide variety of applications of neural networks N eural networks have emerged as advanced data mining tools in cases where other techniques may not produce satisfactory predictive models. As the term implies, neural networks have a biologically inspired modeling capability, but are essentially statistical modeling tools. In this chapter, we study the basics of neural network modeling, some specific applications, and the process of implementing a neural network project. 6.1 Opening Vignette: Using Neural Networks to Predict Beer Flavors with Chemical Analysis 6.2 Basic Concepts of Neural Networks 6.3 Learning in Artificial Neural Networks (ANN) 6.4 Developing Neural Network–Based Systems 6.5 A Sample Neural Network Project 6.6 Other Neural Network Paradigms 6.7 Applications of Artificial Neural Networks 6.8 A Neural Network Software Demonstration 6.1 OPENING VIGNETTE: USING NEURAL NETWORKS TO PREDICT BEER FLAVORS WITH CHEMICAL ANALYSIS Coors Brewers Ltd., based in Burton-upon-Trent, Britain’s brewing capital, is proud of having the United Kingdom’s top beer brands...
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...classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered model. Further, the model is trained with Back Propagation (BP) algorithm and GA (Genetic Algorithm) and classification accuracies are compared. It is revealed from the simulation that our suggested model can be a very good candidate for many applications as these are simple with good performances. Keywords- ANN, Genetic Algorithm, Data classification I. INTRODUCTION Data classification is a classical problem extensively studied by statisticians and machine learning researchers. It is an important problem in variety of engineering and scientific disciplines such as biology, psychology, medicines, marketing, computer vision, and artificial intelligence [1]. The goal of the data classification is to classify objects into a number of categories or classes. Given a dataset, its classification may fall into two tasks i) supervised classification in which given data object is identified as a member of predefined class Unsupervised classification (or also known as Clustering) in which the data...
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...classification problems. The GA is used for optimally finding out the number of neurons in the single hidden layered model. Further, the model is trained with Back Propagation (BP) algorithm and GA (Genetic Algorithm) and classification accuracies are compared. It is revealed from the simulation that our suggested model can be a very good candidate for many applications as these are simple with good performances. Keywords- ANN, Genetic Algorithm, Data classification I. INTRODUCTION Data classification is a classical problem extensively studied by statisticians and machine learning researchers. It is an important problem in variety of engineering and scientific disciplines such as biology, psychology, medicines, marketing, computer vision, and artificial intelligence [1]. The goal of the data classification is to classify objects into a number of categories or classes. Given a dataset, its classification may fall into two tasks i) supervised classification in which given data object is identified as a member of predefined class Unsupervised classification (or also known as Clustering) in which the data...
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...UNIVERSITY INTERNATIONAL CONFERENCE ON ENGINEERING, NUiCONE-2012, 06-08DECEMBER, 2012 1 Thermal Power Plant Analysis Using Artificial Neural Network Purva Deshpande1, Nilima Warke2, Prakash Khandare3, Vijay Deshpande4 VESIT, Chembur, purva.deshpande@yahoo.co.in, nilimavwarke@gmail.com 3,4 Mahagenco, Mumbai. khandarepk@gmail.com, vijay.deshpande.gmit@gmail.com 1,2 Abstract--Coal-based thermal power stations are the leaders in electricity generation in India and are highly complex nonlinear systems. The thermal performance data obtained from MAHAGENCO KORADI UNIT 5 thermal power plant shows that heat rate and boiler efficiency is changing constantly and the plant is probably losing some Megawatts of electric power, and more fuel usage thus resulting in much higher carbon footprints. It is very difficult to analyse the raw data recorded weekly during the full power operation of the plant because a thermal power plant is a very complex system with thousands of parameters. Thus there is a need for nonlinear modeling for the power plant performance analysis in order to meet the growing demands of economic and operational requirements. The intention of this paper is to give an overview of using artificial neural network (ANN) techniques in power systems. Here Back Propagation Neural Network (BPNN) and Radial Basis Neural Network (RBNN) are used for comparative purposes to model the thermodynamic process of a coal-fired power plant, based on actual plant...
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