The document provides an introduction to MATLAB. It discusses that MATLAB is a numerical computing environment and programming language. It can be used for matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages. The document then covers various MATLAB basics like the MATLAB environment, matrix operations, data types, mathematical and logical operators, and plotting functions. It provides examples of creating and manipulating matrices and vectors in MATLAB.
This document discusses using MATLAB to generate a sine wave with minimal computational complexity. It shows that a sine wave can be generated with just three lines of code by plotting x values from 0.1 to 10 against sine of x. This demonstrates MATLAB's simplicity and power as an engineering tool.
This document discusses matrices and arrays in MATLAB. It defines matrices and vectors, and notes that MATLAB treats all variables as matrices. It explains how to enter matrices in MATLAB by listing elements separated by commas and semicolons. It also discusses built-in functions to generate matrices filled with zeros, ones, random values, or an identity matrix. The document covers operations on matrices like addition, subtraction, and multiplication. It explains how to extract sub-matrices and elements using indexing and introduces the colon operator.
This document provides an introduction to MATLAB, covering topics such as its command-oriented interface, variable names, matrices, plotting, logical and relational operators, and toolboxes. MATLAB was originally designed for solving linear algebra problems using matrices and treats all variables as matrices. It allows importing/exporting data and contains toolboxes for tasks like signal processing, control systems, statistics and more.
This document introduces basic commands and concepts in Matlab, including how to define matrices and their elements, perform operations on matrices, and save workspaces. It provides examples of defining row vectors, column vectors, and multi-dimensional matrices. It also demonstrates using semicolons to define matrices that span multiple lines, line continuation with ellipses, and accessing individual matrix elements. Finally, it lists 10 practice problems for the reader to define matrices in Matlab and check their results.
This document provides an introduction and overview of MATLAB. It discusses MATLAB basics like the command window and variables. It also covers topics like working with matrices, vectors, plotting, scripts and functions. Specific MATLAB commands covered include plot, mesh, surf, contour and more. Functions like length, dot, cross and special matrices like ones, zeros and eye are also explained.
This document provides an overview of mathematical functions and plotting in MATLAB. It discusses:
- The many predefined mathematical functions available in MATLAB, such as sin, cos, and exp.
- How to generate vectors and matrices for input.
- How to perform basic plotting of data and functions using commands like plot and linspace.
- How to customize plots by adding titles, labels, and changing colors.
- Matrix operations in MATLAB like transposes, inverses, concatenation and arithmetic operations.
This document provides an introduction to MATLAB. It discusses what MATLAB is, how to perform basic matrix operations and use script files and M-files. It also covers some common MATLAB commands and functions. MATLAB can be used for applications like plotting, image processing, robotics and GUI design. Key topics covered include matrices, vectors, scalars, matrix operations, logical and relational operators, selection and repetition structures, and reading/writing data files. Plotting functions allow creating graphs and 3D surface plots. Image processing, robotics and GUI design are listed as potential application areas.
The document provides an overview of MATLAB, including what it is used for, its graphical user interface, help features, toolboxes, and how to connect to other programs. MATLAB is a numerical computing environment and programming language. It was originally designed for matrix manipulations but has been expanded to include tools for data analysis, signal processing, optimization, and more. Key aspects of MATLAB covered in the document include its command-line interface, workspace, command history, help system, built-in functions, matrices, plotting capabilities, and toolboxes for specialized tasks.
Here are the key points about scalar-matrix addition in MATLAB:
- a is a scalar (single value)
- b is a matrix (2D array)
- To add a scalar to a matrix, MATLAB adds the scalar to each element of the matrix
- c = b + a performs element-wise addition, adding the value of a (which is 3) to each element of b
- The result c is the matrix b with 3 added to each element
So c would be:
c =
4 5 6
7 8 9
Scalar-matrix operations in MATLAB perform the operation on each element of the matrix.
This document discusses MATLAB and provides examples of generating common discrete time signals such as unit impulse, unit step, ramp, exponential and sawtooth signals. MATLAB is an interpreted language well-suited for matrix manipulation and contains built-in functions. Typical uses include math, modelling, data analysis and visualization. Scripts allow executing a series of commands and signals can be plotted versus time or index.
This document provides an overview of MATLAB, including what it is, its features, toolboxes, applications, and how to perform various tasks. MATLAB is a numerical computing environment and programming language used for algorithm development, data analysis, and visualization. It allows matrix operations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs in other languages. The document describes MATLAB's various components, data types, commands, and how to work with matrices, arrays, plots, and other mathematical functions. It also outlines uses of MATLAB in domains like signal processing, control systems, image processing, and more.
This document provides an introduction to MATLAB. It begins with an overview of the MATLAB environment and display windows. It then discusses getting help in MATLAB, variables, vectors, matrices, linear algebra, plotting, built-in functions, selection programming using if/else statements, M-files, user-defined functions, and specific topics. Key points covered include the MATLAB interface, basic programming constructs like variables and arrays, and tools for computation, visualization, and programming in MATLAB.
This document provides an overview of basic concepts in MATLAB including:
- The MATLAB environment and how it is used interactively and for programming.
- Creating and manipulating arrays, matrices, and vectors through built-in functions.
- Saving, loading, selecting, changing, and deleting array elements.
- Performing arithmetic operations like multiplication and addition on arrays.
- Writing user-defined functions and scripts to extend MATLAB's capabilities.
- An example function to calculate the Euclidean distance between a point and multiple other points.
This document discusses the importance and advantages of MATLAB. It notes that MATLAB has matrices as its basic data element, supports vectorized operations, and has built-in graphical and statistical functions. Toolboxes can further expand MATLAB's functionality. While it uses more memory and CPU time than other languages, MATLAB allows both command line and programming capabilities. The document provides examples of how to create matrices, perform operations on matrices using functions like sum(), transpose(), and indexing. It also discusses matrix multiplication and how operations depend on matrix dimensions.
Matrices are arrays of numbers arranged in rows and columns. The order of a matrix specifies the number of rows and columns. Entries are the individual numbers within the matrix. Basic matrix operations include addition, subtraction, scalar multiplication, transpose, and multiplication. Matrix multiplication is only defined when the number of columns of the first matrix equals the number of rows of the second matrix. The determinant provides a scalar value for a square matrix and is used to determine properties like invertibility. Excel can be used to perform matrix operations through functions like MMULT and control-shift-enter.
This document provides an overview of key concepts and functions for MATLAB programming help. It discusses regular expressions, symbolic expressions, logical functions and operators, and relational operators. It also outlines operations for working with matrices like concatenating, accessing elements, deleting rows and columns. Major functions covered include pseudo inverse, inverse, eigen values and vectors, Cholesky decomposition, QR decomposition, and matrix transposition. Contact details are provided at the end for additional MATLAB programming assistance.
The document discusses entering matrices in MATLAB. It explains that a matrix is entered using square brackets with elements separated by spaces or commas within each row and semicolons between rows. Once entered, the matrix is stored in the workspace. It also describes how to find specific elements of a matrix, extract submatrices, determine matrix dimensions, transpose a matrix, concatenate matrices, and perform basic arithmetic operations on matrices.
The document discusses MATLAB, an numerical computing environment and programming language. It provides an introduction to MATLAB, describing its origins and uses. It also outlines some key MATLAB elements like variables, matrices, loading and saving data, and the MATLAB programming language. The document concludes by discussing some MATLAB functions and advantages/disadvantages of the software.
The colon operator (:) in MATLAB is used to create vectors, subscript arrays, and specify iterations. It can be used to generate row or column vectors from a starting number to an ending number, with an optional increment value. The colon operator is also used to select rows, columns, or elements of matrices and multidimensional arrays.
This document provides an introduction to using MATLAB. It begins with instructions on starting and exiting MATLAB. It then discusses MATLAB's basic functionality for matrix calculations and operations. Subsequent sections cover topics like repeating commands, subscripting matrices, the edit-test-edit cycle for developing code, writing functions and scripts, and input/output in MATLAB including loading/saving data and printing output. Exercises are provided throughout to help readers practice key concepts.
This document provides an introduction to MATLAB. It discusses what MATLAB is, how to perform basic matrix operations and use script files and M-files. It also covers some common MATLAB commands and functions. MATLAB can be used for applications like plotting, image processing, robotics and GUI design. Key topics covered include matrices, vectors, scalars, matrix operations, logical and relational operators, selection and repetition structures, and reading/writing data files. Plotting functions allow creating graphs and 3D surface plots. Image processing, robotics and GUI design are listed as potential application areas.
The document provides an overview of MATLAB, including what it is used for, its graphical user interface, help features, toolboxes, and how to connect to other programs. MATLAB is a numerical computing environment and programming language. It was originally designed for matrix manipulations but has been expanded to include tools for data analysis, signal processing, optimization, and more. Key aspects of MATLAB covered in the document include its command-line interface, workspace, command history, help system, built-in functions, matrices, plotting capabilities, and toolboxes for specialized tasks.
Here are the key points about scalar-matrix addition in MATLAB:
- a is a scalar (single value)
- b is a matrix (2D array)
- To add a scalar to a matrix, MATLAB adds the scalar to each element of the matrix
- c = b + a performs element-wise addition, adding the value of a (which is 3) to each element of b
- The result c is the matrix b with 3 added to each element
So c would be:
c =
4 5 6
7 8 9
Scalar-matrix operations in MATLAB perform the operation on each element of the matrix.
This document discusses MATLAB and provides examples of generating common discrete time signals such as unit impulse, unit step, ramp, exponential and sawtooth signals. MATLAB is an interpreted language well-suited for matrix manipulation and contains built-in functions. Typical uses include math, modelling, data analysis and visualization. Scripts allow executing a series of commands and signals can be plotted versus time or index.
This document provides an overview of MATLAB, including what it is, its features, toolboxes, applications, and how to perform various tasks. MATLAB is a numerical computing environment and programming language used for algorithm development, data analysis, and visualization. It allows matrix operations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs in other languages. The document describes MATLAB's various components, data types, commands, and how to work with matrices, arrays, plots, and other mathematical functions. It also outlines uses of MATLAB in domains like signal processing, control systems, image processing, and more.
This document provides an introduction to MATLAB. It begins with an overview of the MATLAB environment and display windows. It then discusses getting help in MATLAB, variables, vectors, matrices, linear algebra, plotting, built-in functions, selection programming using if/else statements, M-files, user-defined functions, and specific topics. Key points covered include the MATLAB interface, basic programming constructs like variables and arrays, and tools for computation, visualization, and programming in MATLAB.
This document provides an overview of basic concepts in MATLAB including:
- The MATLAB environment and how it is used interactively and for programming.
- Creating and manipulating arrays, matrices, and vectors through built-in functions.
- Saving, loading, selecting, changing, and deleting array elements.
- Performing arithmetic operations like multiplication and addition on arrays.
- Writing user-defined functions and scripts to extend MATLAB's capabilities.
- An example function to calculate the Euclidean distance between a point and multiple other points.
This document discusses the importance and advantages of MATLAB. It notes that MATLAB has matrices as its basic data element, supports vectorized operations, and has built-in graphical and statistical functions. Toolboxes can further expand MATLAB's functionality. While it uses more memory and CPU time than other languages, MATLAB allows both command line and programming capabilities. The document provides examples of how to create matrices, perform operations on matrices using functions like sum(), transpose(), and indexing. It also discusses matrix multiplication and how operations depend on matrix dimensions.
Matrices are arrays of numbers arranged in rows and columns. The order of a matrix specifies the number of rows and columns. Entries are the individual numbers within the matrix. Basic matrix operations include addition, subtraction, scalar multiplication, transpose, and multiplication. Matrix multiplication is only defined when the number of columns of the first matrix equals the number of rows of the second matrix. The determinant provides a scalar value for a square matrix and is used to determine properties like invertibility. Excel can be used to perform matrix operations through functions like MMULT and control-shift-enter.
This document provides an overview of key concepts and functions for MATLAB programming help. It discusses regular expressions, symbolic expressions, logical functions and operators, and relational operators. It also outlines operations for working with matrices like concatenating, accessing elements, deleting rows and columns. Major functions covered include pseudo inverse, inverse, eigen values and vectors, Cholesky decomposition, QR decomposition, and matrix transposition. Contact details are provided at the end for additional MATLAB programming assistance.
The document discusses entering matrices in MATLAB. It explains that a matrix is entered using square brackets with elements separated by spaces or commas within each row and semicolons between rows. Once entered, the matrix is stored in the workspace. It also describes how to find specific elements of a matrix, extract submatrices, determine matrix dimensions, transpose a matrix, concatenate matrices, and perform basic arithmetic operations on matrices.
The document discusses MATLAB, an numerical computing environment and programming language. It provides an introduction to MATLAB, describing its origins and uses. It also outlines some key MATLAB elements like variables, matrices, loading and saving data, and the MATLAB programming language. The document concludes by discussing some MATLAB functions and advantages/disadvantages of the software.
The colon operator (:) in MATLAB is used to create vectors, subscript arrays, and specify iterations. It can be used to generate row or column vectors from a starting number to an ending number, with an optional increment value. The colon operator is also used to select rows, columns, or elements of matrices and multidimensional arrays.
This document provides an introduction to using MATLAB. It begins with instructions on starting and exiting MATLAB. It then discusses MATLAB's basic functionality for matrix calculations and operations. Subsequent sections cover topics like repeating commands, subscripting matrices, the edit-test-edit cycle for developing code, writing functions and scripts, and input/output in MATLAB including loading/saving data and printing output. Exercises are provided throughout to help readers practice key concepts.
Jacob Murphy Australia - Excels In Optimizing Software ApplicationsJacob Murphy Australia
In the world of technology, Jacob Murphy Australia stands out as a Junior Software Engineer with a passion for innovation. Holding a Bachelor of Science in Computer Science from Columbia University, Jacob's forte lies in software engineering and object-oriented programming. As a Freelance Software Engineer, he excels in optimizing software applications to deliver exceptional user experiences and operational efficiency. Jacob thrives in collaborative environments, actively engaging in design and code reviews to ensure top-notch solutions. With a diverse skill set encompassing Java, C++, Python, and Agile methodologies, Jacob is poised to be a valuable asset to any software development team.
Construction Materials (Paints) in Civil EngineeringLavish Kashyap
This file will provide you information about various types of Paints in Civil Engineering field under Construction Materials.
It will be very useful for all Civil Engineering students who wants to search about various Construction Materials used in Civil Engineering field.
Paint is a vital construction material used for protecting surfaces and enhancing the aesthetic appeal of buildings and structures. It consists of several components, including pigments (for color), binders (to hold the pigment together), solvents or thinners (to adjust viscosity), and additives (to improve properties like durability and drying time).
Paint is one of the material used in Civil Engineering field. It is especially used in final stages of construction project.
Paint plays a dual role in construction: it protects building materials and contributes to the overall appearance and ambiance of a space.
Several studies have established that strength development in concrete is not only determined by the water/binder ratio, but it is also affected by the presence of other ingredients. With the increase in the number of concrete ingredients from the conventional four materials by addition of various types of admixtures (agricultural wastes, chemical, mineral and biological) to achieve a desired property, modelling its behavior has become more complex and challenging. Presented in this work is the possibility of adopting the Gene Expression Programming (GEP) algorithm to predict the compressive strength of concrete admixed with Ground Granulated Blast Furnace Slag (GGBFS) as Supplementary Cementitious Materials (SCMs). A set of data with satisfactory experimental results were obtained from literatures for the study. Result from the GEP algorithm was compared with that from stepwise regression analysis in order to appreciate the accuracy of GEP algorithm as compared to other data analysis program. With R-Square value and MSE of -0.94 and 5.15 respectively, The GEP algorithm proves to be more accurate in the modelling of concrete compressive strength.
This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.
The main purpose of the current study was to formulate an empirical expression for predicting the axial compression capacity and axial strain of concrete-filled plastic tubular specimens (CFPT) using the artificial neural network (ANN). A total of seventy-two experimental test data of CFPT and unconfined concrete were used for training, testing, and validating the ANN models. The ANN axial strength and strain predictions were compared with the experimental data and predictions from several existing strength models for fiber-reinforced polymer (FRP)-confined concrete. Five statistical indices were used to determine the performance of all models considered in the present study. The statistical evaluation showed that the ANN model was more effective and precise than the other models in predicting the compressive strength, with 2.8% AA error, and strain at peak stress, with 6.58% AA error, of concrete-filled plastic tube tested under axial compression load. Similar lower values were obtained for the NRMSE index.
3. Matlab introduction
Matlab is a program for doing
numerical computation. It was
originally designed for solving linear
algebra type problems using
matrices. It’s name is derived from
MATrix LABoratory.
Matlab is also a programming
language that currently is widely
used as a platform for
developing tools for Machine
Learning
4. Matlab Matrices
Matlab treats all variables as matrices.
For our purposes a matrix can be thought
of as an array, in fact, that is how it is
stored.
Vectors are special forms of matrices
and contain only one row OR one column.
Scalars are matrices with only one row
AND one column
5. Matlab Matrices
A matrix with only one row is called a
row vector. A row vector can be created in
Matlab as follows (note the commas):
» rowvec = [12 , 14 , 63]
rowvec =
12 14 63
6. Matlab Matrices
A matrix with only one column is
called a column vector. A column
vector can be created in MATLAB as
follows (note the semicolons):
» colvec = [13 ; 45 ; -2]
colvec =
13
45
-2
7. Matlab Matrices
A matrix can be created in Matlab
as follows (note the commas AND
semicolons):
» matrix =
[1 , 2 , 3 ; 4 , 5 ,6 ; 7 , 8 , 9]
matrix =
1 2 3
4 5 6
7 8 9
8. Extracting a Sub-Matrix
A portion of a matrix can be extracted and
stored in a smaller matrix by specifying the
names of both matrices and the rows and
columns to extract.
The syntax is:
sub_matrix = matrix ( r1 : r2 , c1 : c2 ) ;
where r1 and r2 specify the beginning and
ending rows and c1 and c2 specify the
beginning and ending columns to be
extracted to make the new matrix.
9. Matlab Matrices
A column vector can be extracted from a matrix. As an
example we create a matrix below:
» matrix=[1,2,3;4,5,6;7,8,9]
matrix =
1 2 3
4 5 6
7 8 9
Here we extract column 2 of the matrix and
make a column vector:
» col_two=matrix( : , 2)
col_two =
2
5
8
10. Matlab Matrices
A row vector can be extracted from a matrix. As an example
we create a matrix below:
» matrix=[1,2,3;4,5,6;7,8,9]
matrix =
1 2 3
4 5 6
7 8 9
Here we extract row 2 of the matrix and make a row vector.
Note that the 2:2 specifies the second row and the 1:3
specifies which columns of the row.
» rowvec=matrix(2 : 2 , 1 : 3)
rowvec =
4 5 6
11. Colon Operator
j:k is the same as [j,j+1,...,k] is empty if j > k
j:i:k is the same as [j,j+i,j+2i, ..,k] is empty if i > 0 and j > k or if
i < 0 and j < k
A(:,j) is the j-th column of A
A(i,:) is the i-th row of A
A(:,:) is the equivalent two-dimensional array. For matrices this
is the same as A.
A(j:k) is A(j), A(j+1),...,A(k)
A(:,j:k) is A(:,j), A(:,j+1),...,A(:,k)
A(:,:,k) is the k-th page of three-dimensional array A.
12. Matlab Matrices
Accessing Single Elements of a Matrix
A(i,j)
Accessing Multiple Elements of a Matrix
A(1,4) + A(2,4) + A(3,4) + A(4,4)
sum(A(1:4,4)) or sum(A(:,end))
The keyword end refers to the last row or
column.
Deleting Rows and Columns
to delete the second column of X, use
X(:,2) = []
Concatenating Matrices A and B
C=[A;B]
13. Some matrix functions in Matlab
X = ones(r,c) % Creates matrix full with
ones
X = zeros(r,c) % Creates matrix full with
zeros
X = rand(r,c) % Creates matrix with
With random numbers between (0, 1]
[r,c] = size(A) % Return dimensions of
matrix A
+ - * / % Standard operations
.+ .- .* ./ % Wise addition, substraction,…
v = sum(A) % Vector with sum of columns
14. Commands
clc Clears Command window.
clear Removes variables from memory.
exist Checks for existence of file or variable.
help Searches for a help topic.
lookfor Searches help entries for a keyword.
quit Stops MATLAB.
who Lists current variables.
whos Lists current variables (long display).
ans Most recent answer.
find Finds indices of nonzero elements.
length Computers number of elements.
max Returns largest element.
min Returns smallest element.
size Computes array size.
sort Sorts each column.
sum Sums each column.