MATLAB 2012 DOUMENTATION CODE

Simulation stepper to simulate and rewind your simulation step by step.

Panning and zooming using the mouse wheel.

Reorganized menus to better fit Model-Based Design workflow.

There are so many new features in Simulink R2012b that I think we will need the next 6 months to cover them all.ДЇor this first post, I made a short video where I tried to use as many new features as possible in a minute: You will notice that the interfaces for the MATLAB desktop and Simulink editor have been completely redesigned. Here we discuss the three different toolboxes in MatLab with examples and outputs.We are happy to announce that MATLAB R2012b is released today! The toolboxes in MATLAB are a collection of numerous functions MATLAB provides various toolboxes to perform functionalities like data analytics, image processing, curve fitting, etc. Basic editing can also be performed using the above options.ДЄs we can see in the output, we have obtained an image of the moon that can be processed using the icons in the ribbon.

We can use the above options in the image processing toolbox to get detailed information about our image or do pre-processing. Options Provided by Image Processing Toolbox

MATLAB 2012 DOUMENTATION CODE

Once we execute the above code in вЂCommand WindowвЂ™, we will get the вЂmoonImageвЂ™ in our вЂWORKSPACEвЂ™. We will upload this dataset to вЂImage processing ToolboxвЂ™ and will explore the possible options. In this example, we will use one of the inbuilt images provided by MATLAB, вЂmoon.tiff. Image Processing ToolboxД«elow we will learn about image processing toolbox: Example Let us now understand the use of the Image processing toolbox using an example. Now as per our requirement, we can train this data and get a response plot, residual plot, min MSE plot using the options available. We can immediately see a response plot created by Regression Learner Toolbox. Step 8: Click on вЂStart SessionвЂ™, to start analyzing the data Step 7: Now we can select the predictor variables as per our requirement Step 6: This will load all the predictor variables under the section вЂPredictorsвЂ™ Step 5: From the вЂData Set VariableвЂ™ dropdown, select the вЂnewTableвЂ™ table created by us Step 4: Click on New Session in the left which will open a new window prompt Step 2: Select вЂRegression Learner ToolboxвЂ™ Once we execute the above code in вЂCommand WindowвЂ™, we will get the вЂnewTableвЂ™ created in our вЂWORKSPACEвЂ™. NewTable = table (Cylinders, Acceleration, Displacement. Create a table using this dataset to load it into вЂRegression Learner ToolboxвЂ™.We will upload this dataset to the вЂRegression Learner ToolboxвЂ™ and will explore the possible options. In this example, we will use an inbuilt dataset provided by MATLAB, вЂcarbigвЂ™. Let us now understand the use of the Regression Learner toolbox using an example. It can also be used to compare different options amongst linearВ regression,В support vector machines, regressionВ trees & visualize the results.It is used to train a model automatically.Regression Learner toolbox is used to perform regression.Next, let us learn how Regression Learner Toolbox works in MATLAB Regression Learner Toolbox We can use a custom equation using the dropdown on the top of the curve.ДЄs we can see in the output, we have obtained a curve, fitting the input variables вЂxвЂ™, вЂyвЂ™, and вЂzвЂ™, which is the same as expected by us. The equation for this curve can be seen in the Result section. We can immediately see that a curve will be created by Curve Fitting Toolbox. Step 4: Now set the вЂX DataвЂ™, вЂY DataвЂ™, вЂZ DataвЂ™ in this pop-up window to our inputs, вЂxвЂ™, вЂyвЂ™, вЂzвЂ™ respectively. Step 3: A pop-up window will open like below: Once we execute the above code in вЂCommand WindowвЂ™, we will get the 3 variables created in our вЂWORKSPACEвЂ™. Set the вЂX DataвЂ™, вЂY DataвЂ™, вЂZ DataвЂ™ in Curve fitting tool to our inputs, вЂxвЂ™, вЂyвЂ™, вЂzвЂ™ respectively.Create the 3 matrices using rand function.In this example, we will use 3 metrics вЂxвЂ™, вЂyвЂ™, вЂzвЂ™ and will fit a curve in them using the Curve fitting toolbox. Let us now understand the use of the Curve fitting toolbox using an example. ThisВ toolbox is very helpful in data analytics as it helps in performing EDA (exploratory data analysis), data processing and removing outliers.This Toolbox provides us with functions and an application to fit curvesВ to our data.Curve fitting toolbox is used to fit the surfaces and curves to input data while using interpolation, regression, and smoothing.Let us now understand the use of a couple of toolboxes in MATLAB: Curve Fitting Toolbox

These toolboxes can be accessed using the вЂAPPSвЂ™ icon in MATLAB ribbon. Hadoop, Data Science, Statistics & others

Matlab 2012 doumentation