tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File Exchange - product:"Neural Network Toolbox"User-contributed code library2014-11-28T14:46:17-05:00571100tag:www.mathworks.com,2005:FileInfo/35822003-06-10T16:07:40Z2014-11-28T18:14:54ZAdaptive FilteringMATLAB files to implement all Adaptive Filtering Algorithms in this book.<p>MATLAB files to implement all Adaptive Filtering Algorithms in the book by Paulo S. R. Diniz,Adaptive Filtering Algorithms and Practical Implementation, Third Edition, Springer, New York, 2008
<br />This book presents a concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduated or first-year graduate courses in adaptive signal processing and adaptive filters. </p>
<p>The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference. </p>
<p>This third edition contains a substantial amount of new material: Two new chapters on data-selective and blind signal processing. It also contains an enlarged discussion of linear-constrained Wiener filters and LMS algorithms, and affine projection algorithms. MATLAB codes for are available for all algorithms. </p>
<p>An instructor`s manual, a set of master transparences, and MATLAB codes for all of the algorithms described in the text are also available. Useful to both professional researchers and students, the text includes hundreds of problems, numerous examples, and over 150 illustrations. It is of primary interest to those working in signal processing, communications, and circuits and systems. </p>
<p>It will also be of interest to those working in power systems,networks, learning systems, and intelligent systems.</p>
<p>For book ordering information, please visit: <a href="http://www.mathworks.com/support/books/book48941.html">http://www.mathworks.com/support/books/book48941.html</a></p>Paulo S. R. Dinizhttp://www.mathworks.com/matlabcentral/fileexchange/authors/9277MATLAB 6.0 (R12)MATLABNeural Network ToolboxImage Processing ToolboxControl System ToolboxCommunications System ToolboxRobust Control ToolboxSignal Processing Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484902014-11-18T01:40:21Z2014-11-24T07:01:04ZLynx MATLAB ToolboxA toolbox for the design of complex machine learning experiments<p>Lynx is a research-oriented MATLAB toolbox for designing in a fast way supervised machine learning experiments. Details of a simulation can be specified under a configuration file, and the toolbox takes charge of loading data, partitioning it, testing the algorithms and visualizing the results. Additionally, it has support for parallelizing the experiments, and enabling GPU support. This makes large experiments easily repeatable and modifiable.
<br />We have currently pre-implemented several algorithms (e.g. support vector machines, kernel ridge regression...), optimization routines (grid-search procedures, searching the optimal feature subset...), and datasets.
<br />You can see examples of use (taken from my research papers) on:
<br /><a href="http://ispac.ing.uniroma1.it/scardapane/software/code/">http://ispac.ing.uniroma1.it/scardapane/software/code/</a>
<br />Please do not hesitate to contact me for any help. The toolbox has been tested on MATLAB R2013a.</p>Simonehttp://www.mathworks.com/matlabcentral/fileexchange/authors/427352MATLAB 8.1 (R2013a)MATLAB Distributed Computing ServerMATLAB Report GeneratorNeural Network ToolboxParallel Computing ToolboxStatistics ToolboxMATLAB3162784214773215282191224093273842806731272falsetag:www.mathworks.com,2005:FileInfo/477842014-11-21T15:44:24Z2014-11-21T15:44:24ZMachine Learning for Mining & Metals2 examples for Machine Learning for Mining, Regression & Classification<p>As presented in the Webinar "Machine Learning for the Mining Industry"
<br />Example 1:
<br />Goal is to build a model that can detect defects in steel plate
<br />manufacturing.
<br />This demo shows:
<br /> a) Machine Learning techniques (Neural Networks, Naive Bayesian, Tree Bagger), Feature Selection,
<br /> b)Parallel Computing
<br /> c) MATLAB Compiler & Builder Ex
<br /> d) Spreadsheet Link</p>
<p>Example 2:
<br />This demo shows how machine learning can be used to improve the accuracy of modelling and predicting the impurities output of an iron ore processing plant. A number of variables in the plant were measured over time including the silica (SiO2) and magnesia (MgO) concentration at the output of the plant. The goal of the modelling is to determine what parameters in the plant need to be adjusted to keep the silica and magnesia concentration at the desired level. The demo uses 1 year of real data captured in an iron-ore processing plant. </p>
<p>This demo shows:
<br /> a) Preparing time series data for analysis
<br /> b) Interpolating missing data
<br /> c) Time aligning and joining multiple data sets using tables
<br /> d) Using decision trees and neural networks to improve the model calculated using multiple linear regression
<br /> e) Using sequential feature selection to identify the important parameters in the plant</p>David Willinghamhttp://www.mathworks.com/matlabcentral/fileexchange/authors/29788MATLAB 8.3 (R2014a)MATLAB Builder EXMATLAB CompilerNeural Network ToolboxParallel Computing ToolboxStatistics ToolboxMATLABSystem Identification ToolboxDatafeed ToolboxSystem Identification ToolboxDatafeed Toolboxfalsetag:www.mathworks.com,2005:FileInfo/329052011-09-15T11:11:57Z2014-11-20T07:51:34ZNeural network simple programs for beginners Simple programs demonstrating Artificial network using Matlab . <p>The tutorial contains programs for PERCEPTRON and LINEAR NETWORKS
<br /> Classification with a 2-input perceptron
<br /> Classification with a 3-input perceptron
<br /> Classification with a 2-neuron perceptron
<br /> Classification with a 2-layer perceptron
<br />Pattern association with a linear neuron
<br /> Training a linear layer
<br /> Adaptive linear layer
<br /> Linear prediction
<br /> Adaptive linear prediction
</p>Sayed Abulhasan Quadrihttp://www.mathworks.com/matlabcentral/fileexchange/authors/145340MATLAB 7.8 (R2009a)MATLABNeural Network Toolboxfalsetag:www.mathworks.com,2005:FileInfo/485142014-11-19T18:19:21Z2014-11-19T18:19:21ZFast Translation Invariant Multiscale Image Denoising (2D, 3D, Poisson, Gaussian images)Efficient algorithms to calculate the translation invariant operator for general multiscale approach<p>If you have a multiscale likelihood-based image denoising approach, then consider to implement this toolbox with the potential to boost the performance of your proposed approach but in a very efficient way. Translation Invariant (TI) cycle spinning is an effective method for removing artifacts from images. This toolbox can calculate the TI version for general multiscale likelihood methods in O(n logn) time (assuming the original method uses O(n) time), for both Gaussian noised and Poisson noised images. It also can be used for general multiscale denoising approaches provided by the users. Please refer to the "DEMO" files for the demonstration. The corresponding paper is extremely useful to understand the mechanisms of the algorithm, which can be found at <a href="http://www4.stat.ncsu.edu/~ghosal/papers/TI_denoise.pdf">http://www4.stat.ncsu.edu/~ghosal/papers/TI_denoise.pdf</a>. Please feel free to contact the author Meng Li (email: <a href="mailto:mli9@ncsu.edu">mli9@ncsu.edu</a>) for any comments or suggestions.</p>MENGhttp://www.mathworks.com/matlabcentral/fileexchange/authors/248176MATLAB 8.2 (R2013b)MATLABNeural Network ToolboxEconometrics ToolboxImage Processing ToolboxStatistics Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484752014-11-16T14:24:57Z2014-11-16T14:24:57ZConfusion matrix for classified imageConfusion matrix for classified image using "all_data_classification.m" which is uploaded<p>This is the code to generated confusion matrix for the image classified by the code "all_data_classification.m". It is classified into 5 classes by using training areas "5_class_test.csv" and image "all_class.csv". With the help of confusion matrix classification accuracy can be computed.</p>Dr.Varsha Turkarhttp://www.mathworks.com/matlabcentral/fileexchange/authors/515016MATLAB 8.0 (R2012b)MATLABNeural Network Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484742014-11-16T14:20:34Z2014-11-16T14:20:34ZNeural network classifierImage classification using neural network classifier<p>This code is written for image classification using Matlab newff function. You can refer Crab classification which is given in Matlab help. This is a supervised classification technique. Appropriate training areas are selected for each class. Training should be given to the neural network using training areas. Here .CSV (comma seprated value) file is used to store training areas (download "5_class_test.csv") and the corresponding class. Once the neural network is trained the entire image can be converted to .CSV file for exmaple if the size of the RGB image is 5 rows and 5 columns then the csv file will have 3 columns and 25 rows (download "all_class.csv").. Each row will be one pixel of the image and each column will be one band. First column will be red band 2nd will be green and 3rd will be blue band.Once the CSV file for the entire image is ready it is given to the trained neural network. Since the image used here is big the code takes more time to classify. The required CSV files are also uploaded along with this code. The input image is not given as it comes under copy right. Here the input data is in a float format</p>Dr.Varsha Turkarhttp://www.mathworks.com/matlabcentral/fileexchange/authors/515016MATLAB 8.0 (R2012b)MATLABNeural Network Toolboxfalsetag:www.mathworks.com,2005:FileInfo/484722014-11-16T14:04:07Z2014-11-16T14:04:07ZTraining areas for imageImage Training areas<p>Image training areas are selected with the help of ROI (region of interest) and then converted to .CSV file. .CSV (comma seprated value) file is used to store training areas and the corresponding class.</p>Dr.Varsha Turkarhttp://www.mathworks.com/matlabcentral/fileexchange/authors/515016MATLAB 8.0 (R2012b)Neural Network Toolboxfalsetag:www.mathworks.com,2005:FileInfo/483902014-11-08T13:06:59Z2014-11-08T13:06:59ZSpeed estimation in rotating reference frameAn angular velocity neuroestimator synthesized in the rotating reference frame is demonstrated.<p>This model illustrates the possibility to use a feedforward neural network (static neural network) to estimate (more precisely to approximate) mechanical speed of the asynchronous motor. The main challenge in such a task is to construct an effective approximation base. In <a href="http://www.mathworks.com/matlabcentral/fileexchange/48012-speed-sensorless-induction-motor-drive">http://www.mathworks.com/matlabcentral/fileexchange/48012-speed-sensorless-induction-motor-drive</a> 6 heuristic signals have been proposed. Here the very basic 4 signals, i.e. stator voltages and currents, span the approximation space. These 4 signals are observed in the flux-oriented rotating reference frame and thus one of the key objectives to keep them constant at a steady state is fulfilled.</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Neural Network ToolboxSimulinkMATLABfalsetag:www.mathworks.com,2005:FileInfo/483192014-11-01T17:35:16Z2014-11-01T17:35:16ZPhotovoltaic module explicit neural modelA feedforward neural network is used to model a photovoltaic module.<p>The phenomenological relation between current and voltage is described by the implicit function. In the case of HIL (hardware in the loop) simulation it could be more convenient to use a neuroemulator instead of solving in real time the phenomenological model of a solar panel (e.g. using Newton's method). This submission contains both approaches. The very basic MPPT (maximum power point tracking) algorithm is also included. There is nothing insightful about this model -- it is supposed to serve educational purposes. Developed back in 2003.</p>Bartlomiej Ufnalskihttp://www.mathworks.com/matlabcentral/fileexchange/authors/501396MATLAB 8.1 (R2013a)Neural Network Toolboxfalse