...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...
Words: 7306 - Pages: 30
...– 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...
Words: 3829 - Pages: 16
...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...
Words: 3437 - Pages: 14
...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...
Words: 7770 - Pages: 32
...SEGMENTATION WITH NEURAL NETWORK B.Prasanna Rahul Radhakrishnan Valliammai Engineering College Valliammai Engineering College prakrish_2001@yahoo.com krish_rahul_1812@yahoo.com Abstract: Our paper work is on Segmentation by Neural networks. Neural networks computation offers a wide range of different algorithms for both unsupervised clustering (UC) and supervised classification (SC). In this paper we approached an algorithmic method that aims to combine UC and SC, where the information obtained during UC is not discarded, but is used as an initial step toward subsequent SC. Thus, the power of both image analysis strategies can be combined in an integrative computational procedure. This is achieved by applying “Hyper-BF network”. Here we worked a different procedures for the training, preprocessing and vector quantization in the application to medical image segmentation and also present the segmentation results for multispectral 3D MRI data sets of the human brain with respect to the tissue classes “ Gray matter”, “ White matter” and “ Cerebrospinal fluid”. We correlate manual and semi automatic methods with the results. Keywords: Image analysis, Hebbian learning rule, Euclidean metric, multi spectral image segmentation, contour tracing. Introduction: Segmentation can be defined as the identification of meaningful image components. It is a fundamental task in image processing providing the basis for any kind of...
Words: 2010 - Pages: 9
...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...
Words: 43079 - Pages: 173
...Neural Networks for Matching in Computer Vision Giansalvo Cirrincione1 and Maurizio Cirrincione2 Department of Electrical Engineering, Lab. CREA University of Picardie-Jules Verne 33, rue Saint Leu, 80039 Amiens - France exin@u-picardie.fr Universite de Technologie de Belfort-Montbeliard (UTBM) Rue Thierry MIEG, Belfort Cedex 90010, France maurizio.cirricione@utbm.fr 1 2 Abstract. A very important problem in computer vision is the matching of features extracted from pairs of images. At this proposal, a new neural network, the Double Asynchronous Competitor (DAC) is presented. It exploits the self-organization for solving the matching as a pattern recognition problem. As a consequence, a set of attributes is required for each image feature. The network is able to find the variety of the input space. DAC exploits two intercoupled neural networks and outputs the matches together with the occlusion maps of the pair of frames taken in consideration. DAC can also solve other matching problems. 1 Introduction In computer vision, structure from motion (SFM) algorithms recover the motion and scene parameters by using a sequence of images (very often only a pair of images is needed). Several SFM techniques require the extraction of features (corners, lines and so on) from each frame. Then, it is necessary to find certain types of correspondences between images, i.e. to identify the image elements in different frames that correspond to the same element in the scene. This paper...
Words: 3666 - Pages: 15
...1. Describe (a) the basic structure of and (b) the learning process for a 3-layer artificial neural network. A 3-layer artificial neural network consists of an input, output and a hidden layer in the middle. For e.g. To recognize male and female faces, the input layer would be made up of a computer program analyzing a camera shot. The output layer would be the word male or female appearing on the screen. The hidden layer is where all action takes place and connections are made between input and output. In an ANN these connections are mathematical. It works by learning from success (hits) and failures (misses) by making adjustments in these mathematical connections. 2. According to Churchland, why does intrapersonal (within one person) moral conflict occur? Intrapersonal moral conflict occurs when some contextual feature is alternately magnified or minimized and one’s overall perceptual take flips back and forth between two distinct activation patterns in the neighborhood of 2 distinct prototypes. In such case, an individual is morally conflicted eg. Should I protect a friends feeling by lying about someone’s hurtful slur or should I tell him the truth? 3. According to Churchland, when should moral correction occur and why? According to Churchland, moral correction should occur at an early age, before child turns into a young adult. Reasons - 1. Firstly, cognitive plasticity and eagerness to imitate found...
Words: 549 - Pages: 3
...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...
Words: 3399 - Pages: 14
...First International Conference on Emerging Trends in Engineering and Technology Rough Set Approach for Feature Reduction in Pattern Recognition through Unsupervised Artificial Neural Network A. G. Kothari A.G. Keskar A.P. Gokhale Rucha Pranjali Lecturer Professor Professor Deshpande Deshmukh agkothari72@re B.Tech Student B.Tech Student diffmail.com Department of Electronics & Computer Science Engineering, VNIT, Nagpur Abstract The Rough Set approach can be applied in pattern recognition at three different stages: pre-processing stage, training stage and in the architecture. This paper proposes the application of the Rough-Neuro Hybrid Approach in the pre-processing stage of pattern recognition. In this project, a training algorithm has been first developed based on Kohonen network. This is used as a benchmark to compare the results of the pure neural approach with the RoughNeuro hybrid approach and to prove that the efficiency of the latter is higher. Structural and statistical features have been extracted from the images for the training process. The number of attributes is reduced by calculating reducts and core from the original attribute set, which results into reduction in convergence time. Also, the above removal in redundancy increases speed of the process reduces hardware complexity and thus enhances the overall efficiency of the pattern recognition algorithm Keywords: core, dimensionality reduction, feature extraction, rough sets, reducts, unsupervised ANN as any...
Words: 2369 - Pages: 10
...www.elsevier.com/locate/atoures Annals of Tourism Research, Vol. 32, No. 1, pp. 93–111, 2005 Ó 2005 Elsevier Ltd. All rights reserved. Printed in Great Britain 0160-7383/$30.00 doi:10.1016/j.annals.2004.05.001 MARKET SEGMENTATION A Neural Network Application Jonathan Z. Bloom University of Stellenbosch, South Africa Abstract: The objective of the research is to consider a self-organizing neural network for segmenting the international tourist market to Cape Town, South Africa. A backpropagation neural network is used to complement the segmentation by generating additional knowledge based on input–output relationship and sensitivity analyses. The findings of the self-organizing neural network indicate three clusters, which are visually confirmed by developing a comparative model based on the test data set. The research also demonstrated that Cape Metropolitan Tourism could deploy the neural network models and track the changing behavior of tourists within and between segments. Marketing implications for the Cape are also highlighted. Keywords: segmentation, SOM neural network, input–output analysis, sensitivity analysis, deployment. Ó 2005 Elsevier Ltd. All rights reserved. ´ ´ Resume: Segmentation du marche: une application du reseau neuronal. Le but de la ´ ´ recherche est de considerer un reseau neuronal auto-organisateur pour segmenter le marche ´ ´ ´ touristique international a Cape Town, en Afrique du Sud. On utilise un reseau neuronal de ` ´ retropropogation pour...
Words: 7968 - Pages: 32
...stronger trend. In this paper we investigate the use of the Hurst exponent to classify series of financial data representing different periods of time. Experiments with backpropagation Neural Networks show that series with large Hurst exponent can be predicted more accurately than those series with H value close to 0.50. Thus Hurst exponent provides a measure for predictability. KEY WORDS Hurst exponent, time series analysis, neural networks, Monte Carlo simulation, forecasting In time series forecasting, the first question we want to answer is whether the time series under study is predictable. If the time series is random, all methods are expected to fail. We want to identify and study those time series having at least some degree of predictability. We know that a time series with a large Hurst exponent has strong trend, thus it’s natural to believe that such time series are more predictable than those having a Hurst exponent close to 0.5. In this paper we use neural networks to test this hypothesis. Neural networks are nonparametric universal function approximators [9] that can learn from data without assumptions. Neural network forecasting models have been widely used in financial time series analysis during the last decade [10],[11],[12]. As universal function approximators, neural networks can be used for surrogate predictability. Under the same conditions, a time series with a smaller forecasting error than another is said to be more predictable. We study the Dow-Jones...
Words: 1864 - Pages: 8
...Stereoscopic Building Reconstruction Using High-Resolution Satellite Image Data Anonymous submission Abstract—This paper presents a novel approach for the generation of 3D building model from satellite image data. The main idea of 3D modeling is based on the grouping of 3D line segments. The divergence-based centroid neural network is employed in the grouping process. Prior to the grouping process, 3D line segments are extracted with the aid of the elevation information obtained by using area-based stereo matching of satellite image data. High-resolution IKONOS stereo images are utilized for the experiments. The experimental result proved the applicability and efficiency of the approach in dealing with 3D building modeling from high-resolution satellite imagery. Index Terms—building model, satellite image, 3D modeling, line segment, stereo I. I NTRODUCTION Extraction of 3D building model is one of the important problems in the generation of an urban model. The process aims to detect and describe the 3D rooftop model from complex scene of satellite imagery. The automated extraction of the 3D rooftop model can be considered as an essential process in dealing with 3D modeling in the urban area. There has been a significant body of research in 3D reconstruction from high-resolution satellite imagery. Even though a natural terrain can be successfully reconstructed in a precise manner by using correlation-based stereoscopic processing of satellite images [1], 3D building reconstruction...
Words: 2888 - Pages: 12
...International Review of Business Research Papers Vol.2. No.4. December 2006, Pp. 39-50 eBusiness-Process-Personalization using Neuro-Fuzzy Adaptive Control for Interactive Systems Zunaira Munir1 , Nie Gui Hua2 , Adeel Talib3 and Mudassir Ilyas4 ‘Personalization’, which was earlier recognized as the 5th ‘P’ of e-marketing , is now becoming a strategic success factor in the present customer-centric e-business environment. This paper proposes two changes in the current structure of personalization efforts in ebusinesses. Firstly, a move towards business-process personalization instead of only website-content personalization and secondly use of an interactive adaptive scheme instead of the commonly employed algorithmic filtering approaches. These can be achieved by applying a neuro-intelligence model to web based real time interactive systems and by integrating it with converging internal and external e-business processes. This paper presents a framework, showing how it is possible to personalize e-business processes by adapting the interactive system to customer preferences. The proposed model applies Neuro-Fuzzy Adaptive Control for Interactive Systems (NFACIS) model to converging business processes to get the desired results. Field of Research: Marketing, e-business 1. Introduction: As Kasanoff (2001) mentioned, the ability to treat different people differently is the most fundamental form of human intelligence. "You talk differently to your boss than to...
Words: 4114 - Pages: 17
...EXECUTIVE SUMMARY INTRODUCTION/BACKGROUND The objective of the thesis is to predict and optimize the mechanical properties of Aircraft fuselage aluminium (AA5083). Firstly, data-driven modelling techniques such as Artificial Neural – Fuzzy networks and regressive analysis are used and by making the effective use of experimental data, FIS membership function parameters are trained. At the core, mathematical model that functionally relates tool rotational speed and forward movement per revolution to that of Yield strength, Ultimate strength and Weld quality are obtained. Also, simulations are performed, and the actual values are compared with the predicted values. Finally, multi-objective optimization of mechanical properties fuselage aluminium was undertaken using Genetic Algorithm to improve the performance of the tools industrially. AIMS AND OBJECTIVES Objectives of the dissertation include Understanding the basic principles of operation of Friction Stir Welding (FSW). Gaining experience in modelling and regressive analysis. Gaining expertise in MATLAB programming. Identifying the best strategy to achieve the yield strength, Ultimate Tensile strength and Weld quality of Friction Stir Welding. Performing optimization of mechanical properties of FSW using Genetic Algorithm. I To draw conclusions on prediction of mechanical properties of FSW optimization of aircraft fuselage aluminium. ACHIEVEMENTS The basic principles of friction welding of the welding...
Words: 9686 - Pages: 39
