tag:www.mathworks.com,2005:/matlabcentral/fileexchange/feedMATLAB Central File Exchangeicon.pnglogo.pngMATLAB Central - File ExchangeUser-contributed code library2014-12-28T09:12:41-05:00226571100tag:www.mathworks.com,2005:FileInfo/488842014-12-28T11:31:38Z2014-12-28T12:44:39ZSensitivity Analysis - Morris method (advanced)Application of the Morris method with a reduced risk of factors underestimation<p>Sensitivity analysis is used to estimate the influence of uncertainty factors on the output of a function. The Morris method is sometimes referenced to as a qualitative method : it gives rough estimations with a limited number of calculations. The Morris method can be used to simplify a function, as a first step. It can identify the factors with a low influence which can be fixed. For further information :
<br />Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity Analysis in Practice - A Guide to Assessing Scientific Models. Wiley.</p>
<p>This algorithm reduces the risk to underestimate and fix non-negligible factors. It is presented in:
<br />Henri Sohier, Helene Piet-Lahanier, Jean-Loup Farges, Analysis and optimization of an air-launch-to-orbit separation, Acta Astronautica, Volume 108, March–April 2015, Pages 18-29, ISSN 0094-5765, <a href="http://dx.doi.org/10.1016/j.actaastro.2014.11.043">http://dx.doi.org/10.1016/j.actaastro.2014.11.043</a>.</p>
<p>For the classic Morris method : <a href="http://fr.mathworks.com/matlabcentral/fileexchange/48883-sensitivity-analysis-morris-method--simple-">http://fr.mathworks.com/matlabcentral/fileexchange/48883-sensitivity-analysis-morris-method--simple-</a></p>
<p>IMPORTANT : The function analyzed must be a function with one multidimensional input x. x must represent the values of the uncertainty factors in the quantiles hyperspace (= the values of the cumulative distribution function). To adapt your function, first apply the inverse of the cumulative distribution function to x before any other calculation; Matlab includes such inverses: mathworks.com/help/stats/icdf.html ).</p>Mrhttp://www.mathworks.com/matlabcentral/fileexchange/authors/295146MATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/488832014-12-28T10:48:15Z2014-12-28T12:40:24ZSensitivity Analysis - Morris method (simple)Application of the Morris method to any Matlab function<p>Sensitivity analysis is used to estimate the influence of uncertainty factors on the output of a function. The Morris method is sometimes referenced to as a qualitative method : it gives rough estimations with a limited number of calculations. The Morris method can be used to simplify a function, as a first step. It can identify the factors with a low influence which can be fixed. For further information :
<br />Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity Analysis in Practice - A Guide to Assessing Scientific Models. Wiley.</p>
<p>This function respects the recommendations from :
<br />Sohier, H., Farges, J. L., & Piet-Lahanier, H. (2014, August). Improvement of the Representativity of the Morris Method for Air-Launch-to-Orbit Separation. In The 19th IFAC World Congress.
<br />(the elementary effects are calculated by applying large variations to radial points sampled in a Latin hypercube)</p>
<p>To reduce the risk to underestimate and fix non-negligible factors : <a href="http://fr.mathworks.com/matlabcentral/fileexchange/48884-sensitivity-analysis-morris-method--advanced-">http://fr.mathworks.com/matlabcentral/fileexchange/48884-sensitivity-analysis-morris-method--advanced-</a></p>
<p>IMPORTANT : The function analyzed must be a function with one multidimensional input x. x must represent the values of the uncertainty factors in the quantiles hyperspace (= the values of the cumulative distribution function). To adapt your function, first apply the inverse of the cumulative distribution function to x before any other calculation; Matlab includes such inverses: mathworks.com/help/stats/icdf.html ).</p>Mrhttp://www.mathworks.com/matlabcentral/fileexchange/authors/295146MATLAB 8.4 (R2014b)falsetag:www.mathworks.com,2005:FileInfo/488822014-12-28T10:39:47Z2014-12-28T10:39:47Zsingle phase grid connected photovoltaic systemboost-buck cascaded converter is used to improve efficiency of system<p>harmonic content can be improve by some modification</p>ld collegehttp://www.mathworks.com/matlabcentral/fileexchange/authors/376841MATLAB 8.1 (R2013a)SimPowerSystemsSimulinkfalsetag:www.mathworks.com,2005:FileInfo/479552014-09-29T17:23:50Z2014-12-27T19:25:40ZfdtFrequency distribution table.<p>A frequency distribution table shows us a summarized grouping of data orderly arranged, divided into mutually exclusive classes (no data value can fall into two different classes), inclusive or exhaustive (all data values must be included) and the number of occurrences in a class. It is a way of showing unorganized data. Some of the graphs that can be used with frequency distributions are histograms, line charts, bar charts and pie charts.
<br />Frequency distributions are used for both qualitative and quantitative data. Here, we are presenting it for quantitative data (measuring observations).
<br />An essential requirement for a frequency distribution is to decide about the number of classes. Theory recomends it should be between 5 and 20 classes. However, some times it it required more classes. Too many classes or too few classes might not reveal the basic shape of the data set, also it will be difficult to interpret such frequency distribution. The maximum number of classes may be determined by a formula. Generally the class interval or class width is the same for all classes.</p>
<p>There are several mathematical procedures which can help calculate the number of classes, all of them have their pros and cons, and can be found in many statistical texts. Here, we include a menu to choose one from:</p>
<p>-- Square root rule
<br />-- 2 to the k rule
<br />-- Rice rule
<br />-- Sturges rule
<br />-- Doane formula
<br />-- Freedman-Diaconis rule
<br />-- Scott rule
<br />-- Shimazaki-Shinomoto method*</p>
<p>In other case you must give the number of classes you need.</p>
<p>*For the last option, it is necessary to download the sshist m-function (Histogram Binwidth Optimization). It returns the optimal number of bins in a histogram used for density estimation. Optimization principle is to minimize expected L2 loss function between the histogram and an unknown underlying density function. An assumption made is merely that samples are drawn from the density independently each other. It can found at</p>
<p><a href="http://www.mathworks.com/matlabcentral/fileexchange/24913-histogram-binwidth-optimization">http://www.mathworks.com/matlabcentral/fileexchange/24913-histogram-binwidth-optimization</a></p>
<p>Why one should organize data in a frequency distribution table?
<br />-- To organize data in a meaningful, intelligible way.
<br />-- To enable the reader to determine the nature or shape of the
<br /> distribution (can make patterns within the data more evident).
<br />-- To facilitate computational procedures for measuring the center,
<br /> variation, distribution shape, outlier(s), and time.
<br />-- To enable the researcher to draw charts and graphs for the
<br /> presentation of data.
<br />-- To enable the reader to make comparison among different data sets. </p>
<p>This m-function also offer a data graph dispaly menu you can select one
<br />option:</p>
<p>-- Histogram
<br />-- Frequency polygon
<br />-- Absolut ogive
<br />-- Relative ogive (here with the observed and the predicted cdf)
<br />-- All
<br />
<br />Syntax: function [y] = fdt(x)
<br />
<br />Input:
<br />x - data vector (from a menu can choose the number of classes
<br /> procedure)
<br />Output:
<br />- frequency (distribution) table and a data graph display optionally from a menu)
<br />[y] - frequency (distribution) table, a data graph display (optionally from a menu), and absolut frequencies and class marks matrix (optionally). This last matrix can be further used for some grouped statistics procedure you can find in my Matlab FEX author page.</p>Antonio Trujillo-Ortizhttp://www.mathworks.com/matlabcentral/fileexchange/authors/7089MATLAB 7.10 (R2010a)Statistics Toolboxfalsetag:www.mathworks.com,2005:FileInfo/429972013-08-09T00:29:02Z2014-12-27T18:12:38ZDICOM to NIfTI converterdicm2nii.m converts dicom files into nifti files.<p>dicm2nii.m converts dicom files in a zip file or in a folder (including sub-folders) into nifti files. It can also convert Philips PAR/REC files and AFNI HEAD/BRIK files into nifti files.
<br />Features:
<br />1. Support nii(.gz) and hdr/img(.gz) output.
<br />2. Create bval and bvec files if there is DTI series.
<br />3. Store B0 unwarping parameters in nifti header, such as delta TE for fieldmap, and effective echo spacing, TE and phase encoding direction for EPI data.
<br />4. Store slice timing related information in nifti header (except for Philips).
<br />5. Unique and descriptive result file names.
<br />6. Several useful dicom functions, like reading header or image, sorting dicom files, renaming dicom files, are included.
<br />'doc dicm2nii' or 'help dicm2nii' for details and usage.
<br />Run dicm2nii without argument will evoke graphic user interface.
<br />Bug reports, comments and suggestions are welcomed.</p>Xiangrui Lihttp://www.mathworks.com/matlabcentral/fileexchange/authors/219482MATLAB 8.1 (R2013a)dicm2nii.m requires NIfTI toolbox. For easy distribution, the required NIfTI code is included. Thanks Jimmy Shen for the excellent toolbox.falsetag:www.mathworks.com,2005:FileInfo/488812014-12-27T17:50:58Z2014-12-27T17:50:58ZShape file - DissolveDissolve shape file<p>I have worked on this code with a friend. It is a common function in many commercial GIS packages but I need it in Matlab. This code works, but it is very very slow when there there are 50,000 plus polygons. Any ideas to improve?</p>Markhttp://www.mathworks.com/matlabcentral/fileexchange/authors/35781MATLAB 8.3 (R2014a)falsetag:www.mathworks.com,2005:FileInfo/476962014-08-29T01:45:54Z2014-12-27T07:00:20ZChromatography ToolboxOpen-source code for processing chromatography data in the MATLAB programming environment.<p>Chromatography Toolbox takes an object-oriented approach to chromatography data processing using the MATLAB programming environment. Current features include:
<br />
<br />1) File Conversion
<br />2) Baseline Correction
<br />3) Peak Detection
<br />4) Peak Integration
<br />5) Visualization
<br />
<br />Supported file extensions include:
<br /> * Agilent (.D)
<br /> * Agilent (.MS)
<br /> * netCDF (.CDF)
<br />
<br />Visit <a href="https://github.com/chemplexity/chromatography">https://github.com/chemplexity/chromatography</a> for more information about getting started.</p>James Dillonhttp://www.mathworks.com/matlabcentral/fileexchange/authors/482605MATLAB 8.3 (R2014a)MATLABfalsetag:www.mathworks.com,2005:FileInfo/467712014-05-27T18:34:10Z2014-12-26T23:22:13ZAlgorithm for Global Optimization Inspired by Collective Animal BehaviorAlgorithm for Global Optimization Inspired by Collective Animal Behavior<p>A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
<br />The algorithm was published in:
<br /><a href="http://www.hindawi.com/journals/ddns/2012/638275/">http://www.hindawi.com/journals/ddns/2012/638275/</a>
<br />The files contain a main program CAB.m and two auxiliary functions.</p>Erikhttp://www.mathworks.com/matlabcentral/fileexchange/authors/150569MATLAB 8.0 (R2012b)falsetag:www.mathworks.com,2005:FileInfo/486522014-12-04T16:33:49Z2014-12-26T19:31:08ZA Comparison of Evolutionary Computation Techniques for IIR Model IdentificationEvolutionary Computation Techniques are compared considering some IIR identification problems<p>System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.
<br />More information can be found in
<br />Erik Cuevas, Jorge Gálvez, Salvador Hinojosa, Omar Avalos, Daniel Zaldívar, and Marco Pérez-Cisneros, “A Comparison of Evolutionary Computation Techniques for IIR Model Identification,” Journal of Applied Mathematics Volume 2014 (2014), Article ID 827206, 8 pages.
<br /><a href="http://www.hindawi.com/journals/jam/2014/827206/">http://www.hindawi.com/journals/jam/2014/827206/</a></p>Erikhttp://www.mathworks.com/matlabcentral/fileexchange/authors/150569MATLAB 8.0 (R2012b)Statistics ToolboxMATLABfalsetag:www.mathworks.com,2005:FileInfo/488792014-12-26T19:30:15Z2014-12-26T19:30:15Zmmoerdijk/plotterA small matlab program to easily load and plot your data<p>See: <a href="https://github.com/mmoerdijk/plotter">https://github.com/mmoerdijk/plotter</a>
<br />Plotter
<br />I made this program for Forze(<a href="http://www.formulazero.tudelft.nl/">http://www.formulazero.tudelft.nl/</a>), the Hydrogen Racing Team of the TU Delft in the Netherlands. We needed program to analyze our log files from the fuel cell controller. As with any other program is it never done and I'm adding new functions when I need them.</p>
<p>What can it do?</p>
<p>It all in the name, really. You can load files from the ./data/ directory or subdirectories and plot the data from those files using the various plot functions. It allows you to export the imported data as csv, apply a low pass filter and basic calculations with 1 or 2 signals.</p>
<p>Does it also load my files?</p>
<p>For now it does load .csv files, CodeSys .trace files and GPUZ log files. There is one requirement for the .csv files, there should be a column named "time" containing timestamps in seconds. Plotter is build to be easily adaptable, this can be done by adding/modifying parsefunctions and/or plotfunctions. These are located in the parsefunctions and plotfunctions directories. New or modified parse and plot functions are automatically loaded when the main function is started.</p>
<p>How to use</p>
<p>Simply start the program by typing in the matlab commandline</p>
<p>>> main_plotter
<br />And follow the instructions on the screen. Or</p>
<p>>> main_plotter(true)
<br />If you want plotter to periodically scan the root of each drive for compatible files. If found these files are then copied into the data directory</p>Marthttp://www.mathworks.com/matlabcentral/fileexchange/authors/474778MATLAB 8.4 (R2014b)false