Matlab Neural Network Toolbox documentation.pdf

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Neural Network Toolbox™ provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.
Revision History June 1992 First printing April 1993 Second printing January 1997 Third printing July 1997 Fourth printing January 1998 Fifth printing Revised for Version 3( Release 11) September 2000 Sixth printing Revised for Version 4(Release 12) June 2001 Seventh printing Minor revisions release 12.1 July 2002 Online onl Minor revisions Release 13) January 2003 Online only Minor revisions (release 13SP1) June 2004 Online only Revised for Version 4.0.3(Release 14) October 2004 Online onl Revised for Version 4.0.4(Release 14SP1) October 2004 Online only8 Eighth printin Revised for Version 4. 0.4 March 2005 Revised for Version 4.0.5(Release 14SP2) March 2006 Online onl, Revised for Version 5.0(Release 2006a) September 2006 Ninth printing Minor revisions (release 2006b) March 2007 Online only Minor revisions(release 2007a) September 2007 Online only Revised for Version 5. 1( Release 2007b) March 2008 Online only Revised for Version 6.0 Release 2008a) October 2008 Online only Revised for Version 6.0.1(Release 2008b) March 2009 Online only Revised for Version 6.0.2(Release 2009a) September 2009 Online only Revised for Version 6.0.3(Release 2009b) March 2010 Online onl Revised for Version 6.0.4( Release 2010a) September 2010 Tenth printing Revised for Version 7.0( Release 2010b) April 2011 Online only Revised for Version 7.0.1(Release 2011a) September 2011 Online only Revised for Version 7.0.2 (Release 2011b) March 2012 Online onl Revised for Version 7.0.3 (Release 2012a September 2012 Online only Revised for Version 8.0 (Release 2012b) March 2013 Online only Revised for Version 8.0.1Release 2013a September 2013 Online only Revised for Version 8. 1(Release 2013b) March 2014 Online only Revised for Version 8.2 (Release 2014a October 2014 Online onl Revised for Version 8.2.1( Release 2014b) March 2015 Online only Revised for version 8.3(Release 2015a) September 2015 Online only Revised for Version 8.4 Release 2015b March 2016 Online only Revised for Version 9.0( Release 201 6a) Contents Acknowledgments Acknowledgments Getting Started Neural Network Toolbox Product Description Key Features Neural networks Overview 1-3 Using Neural Network Toolbox Automatic Script Generation Neural Network Toolbox applications 1-7 Neural Network Design Steps............. 9 Fit Data with a Neural network Defining a Problem 1-10 Using the Neural Network Fitting Tool 1-11 Using Command-Line Functions 1-22 Classify Patterns with a Neural Network 1-31 Defining a Problem 1-31 Using the Neural Network Pattern Recognition Tool 1-33 Using Command-Line Functions 1-45 Cluster Data with a Self-Organizing Map 1-53 Defining a problem 1-53 Using the Neural network Clustering Tool 1-54 Using Command-Line functions 1-63 Neural Network Time Series Prediction and modeling 1-69 Defining a Problem Using the Neural Network Time Series Tool"∵… 1-69 1-70 Using Command-Line Functions 1-82 Parallel Computing on CPUs and GPUs 1-93 Parallel computing Toolbox 1-93 Parallel cpu workers 1-93 GPU Computing 1-94 Multiple gPu/cpu computing 1-94 Cluster Computing with matlAB Distributed Computing Server 1-95 Load Balancing, Large problems, and beyond 1-95 Neural Network Toolbox Sample Data Sets 1-96 Glossary Contents Acknowledgments Acknowledgments Acknowledgments The authors would like to thank the following people Joe Hicklin of Math Works for getting Howard into neural network research years ago at the University of Idaho, for encouraging Howard and mark to write the toolbox, for to help with the toolbox in many ways, and for being such a good friend, for continuing providing crucial help in getting the first toolbox Version 1.0 out the door, for continuing Roy Lurie of Math Works for his continued enthusiasm for the possibilities for Neural Network ToolboxTM software Mary Ann Freeman of Math Works for general support and for her leadership of a great team of people we enjoy working with Rakesh Kumar of Math Works for cheerfully providing technical and practical help encouragement, ideas and always going the extra mile for us Alan La Fleur of Math Works for facilitating our documentation work Stephen Vanreusel of Math Works for help with testing Dan Doherty of math Works for marketing support and ideas and programming the dynamic training algorithms described in "Time Series and g Orlando de Jesus of Oklahoma State University for his excellent work in developing Dynamic Systems"and in programming the neural network controllers described in Neural Network Control Systems"in the Neural Network Toolbox User 's guide Martin T Hagan, Howard B Demuth, and Mark Hudson Beale for permission to include various problems, examples, and other material from Neural Network design January, 1996 Getting 9 Started Neural Network Toolbox Product Description"on page 1-2 Neural Networks Overview'on page 1-3 Using Neural Network Toolbox on page 1-5 Neural Network Toolbox Applications "on page 1-7 “ Neural Network Design Steps” on page1-9 "Fit Data with a Neural Network " on page 1-10 Classify Patterns with a Neural Network"on page 1-31 Cluster data with If-O g Map on pag 1-53 Neural Network Time Series Prediction and Modeling "on page 1-69 Parallel computing on CPUs and gPus"on page 1-93 " Neural Network Toolbox Sample Data Sets on page 1-96 1 Getting Started Neural Network Toolbox Product Description Create, train and simulate neural networks Neural Network Toolbox provides algorithms, functions, and apps to create train visualize, and simulate neural networks. You can perform classification, regression clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control The toolbox includes convolutional neural network and autoencoder deep learnin g algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors GPUS, and computer clusters using Parallel computing Toolbox Key Features Deep learning, including convolutional neural networks and autoencoders Parallel computing and gPu support for accelerating training(with Parallel Computing toolbox) Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LvQ), time-delay, nonlinear autoregressive(NARX), and recurrent eural network rNN) Unsupervised learning algorithms, including self-organizing maps and competitive layers Apps for data-fitting, pattern recognition, and clustering Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance Simulink blocks for building and evaluating neural networks and for control systems applications 1-2

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