Autonomous Vehicle Steering Characteristics of ANNs
|
|
- Vivien Whitehead
- 7 years ago
- Views:
Transcription
1 EE459 Neural Networks Backpropagation Kasin Prakobwaitayakit Department of Electrical Engineering Chiangmai University Background Artificial neural networks (ANNs) provide a general, practical method for learning real-valued, discretevalued, and vector-valued functions from examples. Algorithms such as BACKPROPAGATION use gradient descent to tune network parameters to best fit a training set of input-output pairs. ANN learning is robust to errors in the training data and has been successfully applied to problems such as face recognition/detection, speech recognition, and learning robot control strategies. 1 2 Autonomous Vehicle Steering Characteristics of ANNs Instances are represented by many attribute-value pairs. The target function output may be discrete-valued, realvalued, or a vector of several real- or discrete-valued attributes. The training examples may contain errors. Long training times are acceptable. Fast evaluation of the learned target function may be required. The ability of humans to understand the learned target function is not important. 3 4 Very simple example net input = = Learning problem to be solved Suppose we have an input pattern (0 1) We have a single output pattern (1) We have a net input of -0.1, which gives an output pattern of (0) How could we adjust the weights, so that this situation is remedied and the spontaneous output matches our target output pattern of (1)? 5 6
2 Answer Increase the weights, so that the net input exceeds 0.0 E.g., add 0.2 to all weights Observation: Weight from input node with activation 0 does not have any effect on the net input So we will leave it alone Perceptrons One type of ANN system is based on a unit called a perceptron. The perceptron function can sometimes be written as The space H of candidate hypotheses considered in perceptron learning is the set of all possible real-valued weight vectors. 7 8 Representational Power of Perceptrons Decision surface linear decision surface nonlinear decision surface 9 Programming Example of Decision Surface 10 The Perceptron Training Rule One way to learn an acceptable weight vector is to begin with random weights, then iteratively apply the perceptron to each training example, modifying the perceptron weights whenever it misclassifies an example. This process is repeated, iterating through the training examples as many times as needed until the perceptron classifies all training examples correctly. Weights are modified at each step according to the perceptron training rule, which revises the weight associated with input according to the rule and Delta Rule The delta training rule is best understood by considering the task of training an unthresholded perceptron; that is, a linear unit for which the output o is given by In order to derive a weight learning rule for linear units, let us begin by specifying a measure for the training error of a hypothesis (weight vector), relative to the training examples
3 Visualizing the Hypothesis Space initial weight vector by random minimum error Derivation of the Rule The vector derivative is called the gradient of E with respect to, written The gradient specifies the direction that produces the steepest increase in E. The negative of this vector therefore gives the direction of steepest decrease. The training rule for gradient descent is Derivation of the Rule (cont.) Derivation of the Rule (cont.) The negative sign is presented because we want to move the weight vector in the direction that decreases E. This training rule can also written in its component form The vector of derivatives that form the gradient can be obtained by differentiating E The weight update rule for standard gradient descent can be summarized as which makes it clear that steepest descent is achieved by altering each component of in proportion to EECP0720 Expert Systems Artificial Neural Networks Stochastic Approximation to EECP0720 Expert Systems Artificial Neural Networks Summary of Perceptron Perceptron training rule guaranteed to succeed if training examples are linearly separable sufficiently small learning rate Linear unit training rule uses gradient descent guaranteed to converge to hypothesis with minimum squared error given sufficiently small learning rate even when training data contains noise 17 18
4 EECP0720 Expert Systems Artificial Neural Networks BACKPROPAGATION Algorithm Error Function The Backpropagation algorithm learns the weights for a multilayer network, given a network with a fixed set of units and interconnections. It employs gradient descent to attempt to minimize the squared error between the network output values and the target values for those outputs. We begin by redefining E to sum the errors over all of the network output units where outputs is the set of output units in the network, and t kd and o kd are the target and output values associated with the k th output unit and training example d Architecture of Backpropagation Backpropagation Learning Algorithm Backpropagation Learning Algorithm (cont.) Backpropagation Learning Algorithm (cont.) 23 24
5 Backpropagation Learning Algorithm (cont.) Backpropagation Learning Algorithm (cont.) Inputs To Neurons Arise from other neurons or from outside the network Nodes whose inputs arise outside the network are called input nodes and simply copy values An input may excite or inhibit the response of the neuron to which it is applied, depending upon the weight of the connection Weights Represent synaptic efficacy and may be excitatory or inhibitory Normally, positive weights are considered as excitatory while negative weights are thought of as inhibitory Learning is the process of modifying the weights in order to produce a network that performs some function Output Backpropagation Preparation The response function is normally nonlinear Samples include Sigmoid 1 f ( x) = 1 λx + e Piecewise linear x, if x θ f ( x) = 0, if x < θ 29 Training Set A collection of input-output patterns that are used to train the network Testing Set A collection of input-output patterns that are used to assess network performance Learning Rate-η A scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments 30
6 Network Error Total-Sum-Squared-Error (TSSE) 1 2 TSSE = ( desired actual) 2 patterns outputs Root-Mean-Squared-Error (RMSE) RMSE = 2 * TSSE # patterns *# outputs A Pseudo-Code Algorithm Randomly choose the initial weights While error is too large For each training pattern Apply the inputs to the network Calculate the output for every neuron from the input layer, through the hidden layer(s), to the output layer Calculate the error at the outputs Use the output error to compute error signals for preoutput layers Use the error signals to compute weight adjustments Apply the weight adjustments Periodically evaluate the network performance Face Detection using Neural Networks Face Database Non-Face Database Training Process Output=0, for non-face database Testing Process Output=1, for face database Neural Network Face or Non- Face? 33 Backpropagation Using Advantages Relatively simple implementation Standard method and generally works well Disadvantages Slow and inefficient Can get stuck in local minima resulting in suboptimal solutions 34 Local Minima Alternatives To Gradient Descent Simulated Annealing Advantages Local Minimum Can guarantee optimal solution (global minimum) Disadvantages May be slower than gradient descent Much more complicated implementation Global Minimum 35 36
7 Alternatives To Gradient Descent Genetic Algorithms/Evolutionary Strategies Advantages Faster than simulated annealing Less likely to get stuck in local minima Disadvantages Slower than gradient descent Memory intensive for large nets Momentum Adds a percentage of the last movement to the current movement Momentum Useful to get over small bumps in the error function Often finds a minimum in less steps w(t) = -n*d*y + a*w(t-1) w is the change in weight n is the learning rate d is the error y is different depending on which layer we are calculating a is the momentum parameter Adaptive Backpropagation Algorithm It assigns each weight a learning rate That learning rate is determined by the sign of the gradient of the error function from the last iteration If the signs are equal it is more likely to be a shallow slope so the learning rate is increased The signs are more likely to differ on a steep slope so the learning rate is decreased This will speed up the advancement when on gradual slopes Adaptive Backpropagation Possible Problems: Since we minimize the error for each weight separately the overall error may increase Solution: Calculate the total output error after each adaptation and if it is greater than the previous error reject that adaptation and calculate new learning rates SuperSAB(Super Self-Adapting Backpropagation) Combines the momentum and adaptive methods. Uses adaptive method and momentum so long as the sign of the gradient does not change This is an additive effect of both methods resulting in a faster traversal of gradual slopes When the sign of the gradient does change the momentum will cancel the drastic drop in learning rate This allows for the function to roll up the other side of the minimum possibly escaping local minima 41 42
8 SuperSAB Experiments show that the SuperSAB converges faster than gradient descent Overall this algorithm is less sensitive (and so is less likely to get caught in local minima) Other Ways To Minimize Error Varying training data Cycle through input classes Randomly select from input classes Add noise to training data Randomly change value of input node (with low probability) Retrain with expected inputs after initial training E.g. Speech recognition Other Ways To Minimize Error Adding and removing neurons from layers Adding neurons speeds up learning but may cause loss in generalization Removing neurons has the opposite effect 45
Chapter 4: Artificial Neural Networks
Chapter 4: Artificial Neural Networks CS 536: Machine Learning Littman (Wu, TA) Administration icml-03: instructional Conference on Machine Learning http://www.cs.rutgers.edu/~mlittman/courses/ml03/icml03/
More informationIntroduction to Machine Learning and Data Mining. Prof. Dr. Igor Trajkovski trajkovski@nyus.edu.mk
Introduction to Machine Learning and Data Mining Prof. Dr. Igor Trakovski trakovski@nyus.edu.mk Neural Networks 2 Neural Networks Analogy to biological neural systems, the most robust learning systems
More informationFeed-Forward mapping networks KAIST 바이오및뇌공학과 정재승
Feed-Forward mapping networks KAIST 바이오및뇌공학과 정재승 How much energy do we need for brain functions? Information processing: Trade-off between energy consumption and wiring cost Trade-off between energy consumption
More informationLecture 6. Artificial Neural Networks
Lecture 6 Artificial Neural Networks 1 1 Artificial Neural Networks In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm
More informationAn Introduction to Neural Networks
An Introduction to Vincent Cheung Kevin Cannons Signal & Data Compression Laboratory Electrical & Computer Engineering University of Manitoba Winnipeg, Manitoba, Canada Advisor: Dr. W. Kinsner May 27,
More informationStock Prediction using Artificial Neural Networks
Stock Prediction using Artificial Neural Networks Abhishek Kar (Y8021), Dept. of Computer Science and Engineering, IIT Kanpur Abstract In this work we present an Artificial Neural Network approach to predict
More informationNeural Networks and Support Vector Machines
INF5390 - Kunstig intelligens Neural Networks and Support Vector Machines Roar Fjellheim INF5390-13 Neural Networks and SVM 1 Outline Neural networks Perceptrons Neural networks Support vector machines
More informationSUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK
SUCCESSFUL PREDICTION OF HORSE RACING RESULTS USING A NEURAL NETWORK N M Allinson and D Merritt 1 Introduction This contribution has two main sections. The first discusses some aspects of multilayer perceptrons,
More informationArtificial Neural Networks and Support Vector Machines. CS 486/686: Introduction to Artificial Intelligence
Artificial Neural Networks and Support Vector Machines CS 486/686: Introduction to Artificial Intelligence 1 Outline What is a Neural Network? - Perceptron learners - Multi-layer networks What is a Support
More informationNEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS
NEURAL NETWORK FUNDAMENTALS WITH GRAPHS, ALGORITHMS, AND APPLICATIONS N. K. Bose HRB-Systems Professor of Electrical Engineering The Pennsylvania State University, University Park P. Liang Associate Professor
More informationPerformance Evaluation On Human Resource Management Of China S Commercial Banks Based On Improved Bp Neural Networks
Performance Evaluation On Human Resource Management Of China S *1 Honglei Zhang, 2 Wenshan Yuan, 1 Hua Jiang 1 School of Economics and Management, Hebei University of Engineering, Handan 056038, P. R.
More informationRecurrent Neural Networks
Recurrent Neural Networks Neural Computation : Lecture 12 John A. Bullinaria, 2015 1. Recurrent Neural Network Architectures 2. State Space Models and Dynamical Systems 3. Backpropagation Through Time
More informationSelf Organizing Maps: Fundamentals
Self Organizing Maps: Fundamentals Introduction to Neural Networks : Lecture 16 John A. Bullinaria, 2004 1. What is a Self Organizing Map? 2. Topographic Maps 3. Setting up a Self Organizing Map 4. Kohonen
More informationPerformance Evaluation of Artificial Neural. Networks for Spatial Data Analysis
Contemporary Engineering Sciences, Vol. 4, 2011, no. 4, 149-163 Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis Akram A. Moustafa Department of Computer Science Al al-bayt
More informationNeural network software tool development: exploring programming language options
INEB- PSI Technical Report 2006-1 Neural network software tool development: exploring programming language options Alexandra Oliveira aao@fe.up.pt Supervisor: Professor Joaquim Marques de Sá June 2006
More informationApplication of Neural Network in User Authentication for Smart Home System
Application of Neural Network in User Authentication for Smart Home System A. Joseph, D.B.L. Bong, D.A.A. Mat Abstract Security has been an important issue and concern in the smart home systems. Smart
More informationAnalecta Vol. 8, No. 2 ISSN 2064-7964
EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,
More informationNEURAL NETWORKS A Comprehensive Foundation
NEURAL NETWORKS A Comprehensive Foundation Second Edition Simon Haykin McMaster University Hamilton, Ontario, Canada Prentice Hall Prentice Hall Upper Saddle River; New Jersey 07458 Preface xii Acknowledgments
More informationMachine Learning: Multi Layer Perceptrons
Machine Learning: Multi Layer Perceptrons Prof. Dr. Martin Riedmiller Albert-Ludwigs-University Freiburg AG Maschinelles Lernen Machine Learning: Multi Layer Perceptrons p.1/61 Outline multi layer perceptrons
More informationdegrees of freedom and are able to adapt to the task they are supposed to do [Gupta].
1.3 Neural Networks 19 Neural Networks are large structured systems of equations. These systems have many degrees of freedom and are able to adapt to the task they are supposed to do [Gupta]. Two very
More information6.2.8 Neural networks for data mining
6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural
More informationNeural Computation - Assignment
Neural Computation - Assignment Analysing a Neural Network trained by Backpropagation AA SSt t aa t i iss i t i icc aa l l AA nn aa l lyy l ss i iss i oo f vv aa r i ioo i uu ss l lee l aa r nn i inn gg
More informationData Mining using Artificial Neural Network Rules
Data Mining using Artificial Neural Network Rules Pushkar Shinde MCOERC, Nasik Abstract - Diabetes patients are increasing in number so it is necessary to predict, treat and diagnose the disease. Data
More informationMultiple Layer Perceptron Training Using Genetic Algorithms
Multiple Layer Perceptron Training Using Genetic Algorithms Udo Seiffert University of South Australia, Adelaide Knowledge-Based Intelligent Engineering Systems Centre (KES) Mawson Lakes, 5095, Adelaide,
More informationLecture 8 February 4
ICS273A: Machine Learning Winter 2008 Lecture 8 February 4 Scribe: Carlos Agell (Student) Lecturer: Deva Ramanan 8.1 Neural Nets 8.1.1 Logistic Regression Recall the logistic function: g(x) = 1 1 + e θt
More informationFollow links Class Use and other Permissions. For more information, send email to: permissions@pupress.princeton.edu
COPYRIGHT NOTICE: David A. Kendrick, P. Ruben Mercado, and Hans M. Amman: Computational Economics is published by Princeton University Press and copyrighted, 2006, by Princeton University Press. All rights
More informationA simple application of Artificial Neural Network to cloud classification
A simple application of Artificial Neural Network to cloud classification Tianle Yuan For AOSC 630 (by Prof. Kalnay) Introduction to Pattern Recognition (PR) Example1: visual separation between the character
More informationNeural Network Design in Cloud Computing
International Journal of Computer Trends and Technology- volume4issue2-2013 ABSTRACT: Neural Network Design in Cloud Computing B.Rajkumar #1,T.Gopikiran #2,S.Satyanarayana *3 #1,#2Department of Computer
More informationELLIOTT WAVES RECOGNITION VIA NEURAL NETWORKS
ELLIOTT WAVES RECOGNITION VIA NEURAL NETWORKS Martin Kotyrba Eva Volna David Brazina Robert Jarusek Department of Informatics and Computers University of Ostrava Z70103, Ostrava, Czech Republic martin.kotyrba@osu.cz
More informationSEMINAR OUTLINE. Introduction to Data Mining Using Artificial Neural Networks. Definitions of Neural Networks. Definitions of Neural Networks
SEMINAR OUTLINE Introduction to Data Mining Using Artificial Neural Networks ISM 611 Dr. Hamid Nemati Introduction to and Characteristics of Neural Networks Comparison of Neural Networks to traditional
More informationAPPLICATION OF ARTIFICIAL NEURAL NETWORKS USING HIJRI LUNAR TRANSACTION AS EXTRACTED VARIABLES TO PREDICT STOCK TREND DIRECTION
LJMS 2008, 2 Labuan e-journal of Muamalat and Society, Vol. 2, 2008, pp. 9-16 Labuan e-journal of Muamalat and Society APPLICATION OF ARTIFICIAL NEURAL NETWORKS USING HIJRI LUNAR TRANSACTION AS EXTRACTED
More informationCHAPTER 5 PREDICTIVE MODELING STUDIES TO DETERMINE THE CONVEYING VELOCITY OF PARTS ON VIBRATORY FEEDER
93 CHAPTER 5 PREDICTIVE MODELING STUDIES TO DETERMINE THE CONVEYING VELOCITY OF PARTS ON VIBRATORY FEEDER 5.1 INTRODUCTION The development of an active trap based feeder for handling brakeliners was discussed
More informationComparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification R. Sathya Professor, Dept. of MCA, Jyoti Nivas College (Autonomous), Professor and Head, Dept. of Mathematics, Bangalore,
More informationMANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL
MANAGING QUEUE STABILITY USING ART2 IN ACTIVE QUEUE MANAGEMENT FOR CONGESTION CONTROL G. Maria Priscilla 1 and C. P. Sumathi 2 1 S.N.R. Sons College (Autonomous), Coimbatore, India 2 SDNB Vaishnav College
More informationMachine Learning and Data Mining -
Machine Learning and Data Mining - Perceptron Neural Networks Nuno Cavalheiro Marques (nmm@di.fct.unl.pt) Spring Semester 2010/2011 MSc in Computer Science Multi Layer Perceptron Neurons and the Perceptron
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical
More informationAdvanced analytics at your hands
2.3 Advanced analytics at your hands Neural Designer is the most powerful predictive analytics software. It uses innovative neural networks techniques to provide data scientists with results in a way previously
More informationAn Augmented Normalization Mechanism for Capacity Planning & Modelling Elegant Approach with Artificial Intelligence
An Augmented Normalization Mechanism for Capacity Planning & Modelling Elegant Approach with Artificial Intelligence 13th Annual International Software Testing Conference 2013 Bangalore, 4 th -5 th December
More informationA Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study
211 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (211) (211) IACSIT Press, Singapore A Multi-level Artificial Neural Network for Residential and Commercial Energy
More informationSELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS
UDC: 004.8 Original scientific paper SELECTING NEURAL NETWORK ARCHITECTURE FOR INVESTMENT PROFITABILITY PREDICTIONS Tonimir Kišasondi, Alen Lovren i University of Zagreb, Faculty of Organization and Informatics,
More informationEFFICIENT DATA PRE-PROCESSING FOR DATA MINING
EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More information129: Artificial Neural Networks. Ajith Abraham Oklahoma State University, Stillwater, OK, USA 1 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS
129: Artificial Neural Networks Ajith Abraham Oklahoma State University, Stillwater, OK, USA 1 Introduction to Artificial Neural Networks 901 2 Neural Network Architectures 902 3 Neural Network Learning
More informationPLAANN as a Classification Tool for Customer Intelligence in Banking
PLAANN as a Classification Tool for Customer Intelligence in Banking EUNITE World Competition in domain of Intelligent Technologies The Research Report Ireneusz Czarnowski and Piotr Jedrzejowicz Department
More informationImpact of Feature Selection on the Performance of Wireless Intrusion Detection Systems
2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Impact of Feature Selection on the Performance of ireless Intrusion Detection Systems
More informationNeural Network and Genetic Algorithm Based Trading Systems. Donn S. Fishbein, MD, PhD Neuroquant.com
Neural Network and Genetic Algorithm Based Trading Systems Donn S. Fishbein, MD, PhD Neuroquant.com Consider the challenge of constructing a financial market trading system using commonly available technical
More informationLinear Threshold Units
Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear
More informationHorse Racing Prediction Using Artificial Neural Networks
Horse Racing Prediction Using Artificial Neural Networks ELNAZ DAVOODI, ALI REZA KHANTEYMOORI Mathematics and Computer science Department Institute for Advanced Studies in Basic Sciences (IASBS) Gavazang,
More informationIBM SPSS Neural Networks 22
IBM SPSS Neural Networks 22 Note Before using this information and the product it supports, read the information in Notices on page 21. Product Information This edition applies to version 22, release 0,
More informationLecture 2: The SVM classifier
Lecture 2: The SVM classifier C19 Machine Learning Hilary 2015 A. Zisserman Review of linear classifiers Linear separability Perceptron Support Vector Machine (SVM) classifier Wide margin Cost function
More informationOpen Access Research on Application of Neural Network in Computer Network Security Evaluation. Shujuan Jin *
Send Orders for Reprints to reprints@benthamscience.ae 766 The Open Electrical & Electronic Engineering Journal, 2014, 8, 766-771 Open Access Research on Application of Neural Network in Computer Network
More informationArtificial Neural Network for Speech Recognition
Artificial Neural Network for Speech Recognition Austin Marshall March 3, 2005 2nd Annual Student Research Showcase Overview Presenting an Artificial Neural Network to recognize and classify speech Spoken
More informationTemporal Difference Learning in the Tetris Game
Temporal Difference Learning in the Tetris Game Hans Pirnay, Slava Arabagi February 6, 2009 1 Introduction Learning to play the game Tetris has been a common challenge on a few past machine learning competitions.
More informationPrinciples of Data Mining by Hand&Mannila&Smyth
Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences
More informationBack Propagation Neural Networks User Manual
Back Propagation Neural Networks User Manual Author: Lukáš Civín Library: BP_network.dll Runnable class: NeuralNetStart Document: Back Propagation Neural Networks Page 1/28 Content: 1 INTRODUCTION TO BACK-PROPAGATION
More informationNeural Networks and Back Propagation Algorithm
Neural Networks and Back Propagation Algorithm Mirza Cilimkovic Institute of Technology Blanchardstown Blanchardstown Road North Dublin 15 Ireland mirzac@gmail.com Abstract Neural Networks (NN) are important
More informationUsing Artifical Neural Networks to Model Opponents in Texas Hold'em
CMPUT 499 - Research Project Review Using Artifical Neural Networks to Model Opponents in Texas Hold'em Aaron Davidson email: davidson@cs.ualberta.ca November 28th, 1999 Abstract: This paper describes
More informationNovelty Detection in image recognition using IRF Neural Networks properties
Novelty Detection in image recognition using IRF Neural Networks properties Philippe Smagghe, Jean-Luc Buessler, Jean-Philippe Urban Université de Haute-Alsace MIPS 4, rue des Frères Lumière, 68093 Mulhouse,
More informationElectroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep
Engineering, 23, 5, 88-92 doi:.4236/eng.23.55b8 Published Online May 23 (http://www.scirp.org/journal/eng) Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep JeeEun
More informationOPTIMUM LEARNING RATE FOR CLASSIFICATION PROBLEM
OPTIMUM LEARNING RATE FOR CLASSIFICATION PROBLEM WITH MLP IN DATA MINING Lalitha Saroja Thota 1 and Suresh Babu Changalasetty 2 1 Department of Computer Science, King Khalid University, Abha, KSA 2 Department
More information3 An Illustrative Example
Objectives An Illustrative Example Objectives - Theory and Examples -2 Problem Statement -2 Perceptron - Two-Input Case -4 Pattern Recognition Example -5 Hamming Network -8 Feedforward Layer -8 Recurrent
More informationAmerican International Journal of Research in Science, Technology, Engineering & Mathematics
American International Journal of Research in Science, Technology, Engineering & Mathematics Available online at http://www.iasir.net ISSN (Print): 2328-349, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629
More informationBig Data Analytics Using Neural networks
San José State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research 4-1-2014 Big Data Analytics Using Neural networks Follow this and additional works at: http://scholarworks.sjsu.edu/etd_projects
More informationPhotonic Reservoir Computing with coupled SOAs
Photonic Reservoir Computing with coupled SOAs Kristof Vandoorne, Wouter Dierckx, David Verstraete, Benjamin Schrauwen, Roel Baets, Peter Bienstman and Jan Van Campenhout OSC 2008: August 26 Intelligence
More informationModels of Cortical Maps II
CN510: Principles and Methods of Cognitive and Neural Modeling Models of Cortical Maps II Lecture 19 Instructor: Anatoli Gorchetchnikov dy dt The Network of Grossberg (1976) Ay B y f (
More informationPower Prediction Analysis using Artificial Neural Network in MS Excel
Power Prediction Analysis using Artificial Neural Network in MS Excel NURHASHINMAH MAHAMAD, MUHAMAD KAMAL B. MOHAMMED AMIN Electronic System Engineering Department Malaysia Japan International Institute
More informationForecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network
Forecasting of Economic Quantities using Fuzzy Autoregressive Model and Fuzzy Neural Network Dušan Marček 1 Abstract Most models for the time series of stock prices have centered on autoregressive (AR)
More informationPrediction Model for Crude Oil Price Using Artificial Neural Networks
Applied Mathematical Sciences, Vol. 8, 2014, no. 80, 3953-3965 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43193 Prediction Model for Crude Oil Price Using Artificial Neural Networks
More informationBack Propagation Neural Network for Wireless Networking
International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-4 E-ISSN: 2347-2693 Back Propagation Neural Network for Wireless Networking Menal Dahiya Maharaja Surajmal
More informationFeedforward Neural Networks and Backpropagation
Feedforward Neural Networks and Backpropagation Feedforward neural networks Architectural issues, computational capabilities Sigmoidal and radial basis functions Gradient-based learning and Backprogation
More informationKeywords: Image complexity, PSNR, Levenberg-Marquardt, Multi-layer neural network.
Global Journal of Computer Science and Technology Volume 11 Issue 3 Version 1.0 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172
More informationData Mining Techniques Chapter 7: Artificial Neural Networks
Data Mining Techniques Chapter 7: Artificial Neural Networks Artificial Neural Networks.................................................. 2 Neural network example...................................................
More informationPadma Charan Das Dept. of E.T.C. Berhampur, Odisha, India
Volume 5, Issue 3, March 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Measuring Quality
More informationData-stream Mining for Rule-based Access Control. Andrii Shalaginov, andrii.shalaginov@hig.no 13 th of October 2014 COINS PhD seminar
Data-stream Mining for Rule-based Access Control Andrii Shalaginov, andrii.shalaginov@hig.no 13 th of October 2014 COINS PhD seminar 2 / 182 OVERALL PICTURE OF PHD Work towards Data-driven Reasoning for
More informationThe PageRank Citation Ranking: Bring Order to the Web
The PageRank Citation Ranking: Bring Order to the Web presented by: Xiaoxi Pang 25.Nov 2010 1 / 20 Outline Introduction A ranking for every page on the Web Implementation Convergence Properties Personalized
More informationChapter 12 Discovering New Knowledge Data Mining
Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to
More informationComputational Neural Network for Global Stock Indexes Prediction
Computational Neural Network for Global Stock Indexes Prediction Dr. Wilton.W.T. Fok, IAENG, Vincent.W.L. Tam, Hon Ng Abstract - In this paper, computational data mining methodology was used to predict
More informationNeural Networks algorithms and applications
Neural Networks algorithms and applications By Fiona Nielsen 4i 12/12-2001 Supervisor: Geert Rasmussen Niels Brock Business College 1 Introduction Neural Networks is a field of Artificial Intelligence
More informationComparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
Volume 3, No. 8, August 2012 Journal of Global Research in Computer Science REVIEW ARTICLE Available Online at www.jgrcs.info Comparison of Supervised and Unsupervised Learning Classifiers for Travel Recommendations
More informationTRAINING A LIMITED-INTERCONNECT, SYNTHETIC NEURAL IC
777 TRAINING A LIMITED-INTERCONNECT, SYNTHETIC NEURAL IC M.R. Walker. S. Haghighi. A. Afghan. and L.A. Akers Center for Solid State Electronics Research Arizona State University Tempe. AZ 85287-6206 mwalker@enuxha.eas.asu.edu
More informationNEURAL NETWORKS AND REINFORCEMENT LEARNING. Abhijit Gosavi
NEURAL NETWORKS AND REINFORCEMENT LEARNING Abhijit Gosavi Department of Engineering Management and Systems Engineering Missouri University of Science and Technology Rolla, MO 65409 1 Outline A Quick Introduction
More informationPlanning Workforce Management for Bank Operation Centers with Neural Networks
Plaing Workforce Management for Bank Operation Centers with Neural Networks SEFIK ILKIN SERENGIL Research and Development Center SoftTech A.S. Tuzla Teknoloji ve Operasyon Merkezi, Tuzla 34947, Istanbul
More informationLogistic Regression for Spam Filtering
Logistic Regression for Spam Filtering Nikhila Arkalgud February 14, 28 Abstract The goal of the spam filtering problem is to identify an email as a spam or not spam. One of the classic techniques used
More informationA Content based Spam Filtering Using Optical Back Propagation Technique
A Content based Spam Filtering Using Optical Back Propagation Technique Sarab M. Hameed 1, Noor Alhuda J. Mohammed 2 Department of Computer Science, College of Science, University of Baghdad - Iraq ABSTRACT
More informationGLOVE-BASED GESTURE RECOGNITION SYSTEM
CLAWAR 2012 Proceedings of the Fifteenth International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, Baltimore, MD, USA, 23 26 July 2012 747 GLOVE-BASED GESTURE
More informationTennis Winner Prediction based on Time-Series History with Neural Modeling
Tennis Winner Prediction based on Time-Series History with Neural Modeling Amornchai Somboonphokkaphan, Suphakant Phimoltares, and Chidchanok Lursinsap Abstract Tennis is one of the most popular sports
More informationA Time Series ANN Approach for Weather Forecasting
A Time Series ANN Approach for Weather Forecasting Neeraj Kumar 1, Govind Kumar Jha 2 1 Associate Professor and Head Deptt. Of Computer Science,Nalanda College Of Engineering Chandi(Bihar) 2 Assistant
More informationARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION
1 ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION B. Mikó PhD, Z-Form Tool Manufacturing and Application Ltd H-1082. Budapest, Asztalos S. u 4. Tel: (1) 477 1016, e-mail: miko@manuf.bme.hu
More informationIntroduction to Machine Learning Using Python. Vikram Kamath
Introduction to Machine Learning Using Python Vikram Kamath Contents: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Introduction/Definition Where and Why ML is used Types of Learning Supervised Learning Linear Regression
More informationSupporting Online Material for
www.sciencemag.org/cgi/content/full/313/5786/504/dc1 Supporting Online Material for Reducing the Dimensionality of Data with Neural Networks G. E. Hinton* and R. R. Salakhutdinov *To whom correspondence
More informationNeural Networks: a replacement for Gaussian Processes?
Neural Networks: a replacement for Gaussian Processes? Matthew Lilley and Marcus Frean Victoria University of Wellington, P.O. Box 600, Wellington, New Zealand marcus@mcs.vuw.ac.nz http://www.mcs.vuw.ac.nz/
More informationARTIFICIAL NEURAL NETWORKS FOR ADAPTIVE MANAGEMENT TRAFFIC LIGHT OBJECTS AT THE INTERSECTION
The 10 th International Conference RELIABILITY and STATISTICS in TRANSPORTATION and COMMUNICATION - 2010 Proceedings of the 10th International Conference Reliability and Statistics in Transportation and
More informationMethod of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks
Method of Combining the Degrees of Similarity in Handwritten Signature Authentication Using Neural Networks Ph. D. Student, Eng. Eusebiu Marcu Abstract This paper introduces a new method of combining the
More informationAN APPLICATION OF TIME SERIES ANALYSIS FOR WEATHER FORECASTING
AN APPLICATION OF TIME SERIES ANALYSIS FOR WEATHER FORECASTING Abhishek Agrawal*, Vikas Kumar** 1,Ashish Pandey** 2,Imran Khan** 3 *(M. Tech Scholar, Department of Computer Science, Bhagwant University,
More informationNeural Network Add-in
Neural Network Add-in Version 1.5 Software User s Guide Contents Overview... 2 Getting Started... 2 Working with Datasets... 2 Open a Dataset... 3 Save a Dataset... 3 Data Pre-processing... 3 Lagging...
More informationThe Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy
BMI Paper The Effects of Start Prices on the Performance of the Certainty Equivalent Pricing Policy Faculty of Sciences VU University Amsterdam De Boelelaan 1081 1081 HV Amsterdam Netherlands Author: R.D.R.
More informationStock Market Prediction System with Modular Neural Networks
Stock Market Prediction System with Modular Neural Networks Takashi Kimoto and Kazuo Asakawa Computer-based Systems Laboratory FUJTSU LABORATORES LTD., KAWASAK 1015 Kamikodanaka, Nakahara-Ku, Kawasaki
More informationAn Artificial Neural Networks-Based on-line Monitoring Odor Sensing System
Journal of Computer Science 5 (11): 878-882, 2009 ISSN 1549-3636 2009 Science Publications An Artificial Neural Networks-Based on-line Monitoring Odor Sensing System Yousif Al-Bastaki The College of Information
More informationDYNAMIC LOAD BALANCING OF FINE-GRAIN SERVICES USING PREDICTION BASED ON SERVICE INPUT JAN MIKSATKO. B.S., Charles University, 2003 A THESIS
DYNAMIC LOAD BALANCING OF FINE-GRAIN SERVICES USING PREDICTION BASED ON SERVICE INPUT by JAN MIKSATKO B.S., Charles University, 2003 A THESIS Submitted in partial fulfillment of the requirements for the
More informationThe general inefficiency of batch training for gradient descent learning
Neural Networks 16 (2003) 1429 1451 www.elsevier.com/locate/neunet The general inefficiency of batch training for gradient descent learning D. Randall Wilson a, *, Tony R. Martinez b,1 a Fonix Corporation,
More informationINTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr.
INTELLIGENT ENERGY MANAGEMENT OF ELECTRICAL POWER SYSTEMS WITH DISTRIBUTED FEEDING ON THE BASIS OF FORECASTS OF DEMAND AND GENERATION Chr. Meisenbach M. Hable G. Winkler P. Meier Technology, Laboratory
More information