The document describes the bisection method, a root-finding algorithm that uses binary search to find roots (values that make a function equal to zero) of a continuous function. It works by repeatedly bisecting an interval known to contain a root and narrowing in on the root. The key steps are: (1) Start with an interval [a,b] where the function changes sign, ensuring a root exists in the interval. (2) Calculate the midpoint m of the interval. (3) Determine which subinterval [a,m] or [m,b] contains the root based on the sign change and update the interval accordingly. (4) Repeat until the interval size is small enough to approximate the root.
This lecture contains Newton Raphson Method working rule, Graphical representation, Example, Pros and cons of this method and a Matlab Code.
Explanation is available here: https://www.youtube.com/watch?v=NmwwcfyvHVg&lc=UgwqFcZZrXScgYBZPcV4AaABAg
This document discusses approximation and round-off error in engineering. It defines approximation as using an inexact value when the exact value is unknown or difficult to obtain. Approximations introduce errors from measurements in the real world. There are two main types of errors - truncation error from dropping digits during approximations, and rounding error from representing numbers with a fixed number of significant figures. The absolute error is the difference between the true and approximate values, while relative error is the percentage difference between the absolute error and true value.
The document discusses numerical methods and their applications. Numerical methods provide approximate solutions to mathematical problems using arithmetic operations. They are used when analytical solutions cannot be found or are too complex. Numerical methods involve formulating a mathematical model, developing a numerical solution technique, implementing the technique, obtaining a solution, and validating the results. Engineering and science applications of numerical methods include modeling, scientific computing, modeling airflow over airplanes, estimating ocean currents, solving electromagnetics problems, and simulating shuttle tank separation.
The document discusses the secant method for finding the roots of non-linear equations. It introduces the secant method which uses successive secant lines through points on the graph of a function to better approximate roots. The methodology section explains that a secant line is defined by two initial points and the next point is where the secant line crosses the x-axis. The algorithm involves calculating the next estimate from the two initial guesses and checking if the error is below a tolerance level. Applications include using the secant method for earthquake engineering analysis and limitations include potential division by zero errors or root jumping.
The document discusses the Newton Raphson method for finding roots of equations. It describes how Isaac Newton and Joseph Raphson discovered the method in the 17th century. The method works by taking the derivative of the function and using it to calculate successive approximations that converge on a root. The document provides an example of using the method to find the root of a function and discusses advantages like fast convergence and requiring only an initial guess, as well as potential drawbacks such as failure to converge or slow convergence for roots with multiplicity greater than one.
The document describes the bisection method, a root-finding algorithm that uses binary search to find roots (values that make a function equal to zero) of a continuous function. It works by repeatedly bisecting an interval known to contain a root and narrowing in on the root. The key steps are: (1) Start with an interval [a,b] where the function changes sign, ensuring a root exists in the interval. (2) Calculate the midpoint m of the interval. (3) Determine which subinterval [a,m] or [m,b] contains the root based on the sign change and update the interval accordingly. (4) Repeat until the interval size is small enough to approximate the root.
This lecture contains Newton Raphson Method working rule, Graphical representation, Example, Pros and cons of this method and a Matlab Code.
Explanation is available here: https://www.youtube.com/watch?v=NmwwcfyvHVg&lc=UgwqFcZZrXScgYBZPcV4AaABAg
This document discusses approximation and round-off error in engineering. It defines approximation as using an inexact value when the exact value is unknown or difficult to obtain. Approximations introduce errors from measurements in the real world. There are two main types of errors - truncation error from dropping digits during approximations, and rounding error from representing numbers with a fixed number of significant figures. The absolute error is the difference between the true and approximate values, while relative error is the percentage difference between the absolute error and true value.
The document discusses numerical methods and their applications. Numerical methods provide approximate solutions to mathematical problems using arithmetic operations. They are used when analytical solutions cannot be found or are too complex. Numerical methods involve formulating a mathematical model, developing a numerical solution technique, implementing the technique, obtaining a solution, and validating the results. Engineering and science applications of numerical methods include modeling, scientific computing, modeling airflow over airplanes, estimating ocean currents, solving electromagnetics problems, and simulating shuttle tank separation.
The document discusses the secant method for finding the roots of non-linear equations. It introduces the secant method which uses successive secant lines through points on the graph of a function to better approximate roots. The methodology section explains that a secant line is defined by two initial points and the next point is where the secant line crosses the x-axis. The algorithm involves calculating the next estimate from the two initial guesses and checking if the error is below a tolerance level. Applications include using the secant method for earthquake engineering analysis and limitations include potential division by zero errors or root jumping.
The document discusses the Newton Raphson method for finding roots of equations. It describes how Isaac Newton and Joseph Raphson discovered the method in the 17th century. The method works by taking the derivative of the function and using it to calculate successive approximations that converge on a root. The document provides an example of using the method to find the root of a function and discusses advantages like fast convergence and requiring only an initial guess, as well as potential drawbacks such as failure to converge or slow convergence for roots with multiplicity greater than one.
Restoring and Non-Restoring division algo for CSEARoy10
Aishwarya Roy presented on division algorithms, specifically restoring and non-restoring techniques. The presentation covered:
- Division algorithms provide a quotient and remainder when dividing two numbers. There are slow and fast algorithms, including restoring and non-restoring.
- Non-restoring division shifts bits in registers and performs ALU operations without restoring bits to the accumulator. Restoring division restores previous accumulator bits when the MSB is 1.
- An example demonstrated non-restoring division with 27 as the dividend and 4 as the divisor. Hardware implementations of non-restoring and restoring division were also described.
- While non-restoring avoids a test subtraction, it requires an extra
Matematika 2 - Slide week 3 - integral substitusi trigonometrikBeny Nugraha
Dokumen ini memberikan penjelasan tentang integral tak tentu, integral tentu, integrasi parsial, dan integral substitusi trigonometri. Integral adalah proses kebalikan dari diferensiasi untuk menemukan anti-derivatif dari suatu fungsi. Terdapat beberapa aturan dalam menghitung integral seperti aturan perkalian konstan dan penjumlahan. Integrasi parsial digunakan untuk menghitung integral yang berisi dua atau lebih fungsi. Substitusi trigonometri diterapkan untuk menghilang
This document discusses sequences and series. It provides definitions of key terms like sequence, finite sequence, infinite sequence, convergent sequence, divergent sequence, monotonic sequence, and geometric progression. It then goes on to solve 4 example problems:
1) It shows that the sequence 2n^2+n/n^2+1 is convergent by taking the limit as n approaches infinity.
2) It uses the ratio test to show that the sequence n!/n^n is convergent.
3) It proves that the sequence 1/1! + 1/2! +...+ 1/n! is convergent by showing it is increasing and bounded.
4) It shows that the sequence
Numerical method errors analysis examines the difference between true and approximate values. Absolute error is the difference between true and approximate values, while relative error is the ratio of absolute error to true value. Percentage error is calculated by taking the absolute difference between true and approximate values, dividing by the absolute true value, and multiplying by 100. Examples are provided to demonstrate calculating absolute, relative, and percentage errors.
Eigen values and eigen vectors engineeringshubham211
mathematics...for engineering mathematics.....learn maths...............................The individual items in a matrix are called its elements or entries.[4] Provided that they are the same size (have the same number of rows and the same number of columns), two matrices can be added or subtracted element by element. The rule for matrix multiplication, however, is that two matrices can be multiplied only when the number of columns in the first equals the number of rows in the second. Any matrix can be multiplied element-wise by a scalar from its associated field. A major application of matrices is to represent linear transformations, that is, generalizations of linear functions such as f(x) = 4x. For example, the rotation of vectors in three dimensional space is a linear transformation which can be represented by a rotation matrix R: if v is a column vector (a matrix with only one column) describing the position of a point in space, the product Rv is a column vector describing the position of that point after a rotation. The product of two transformation matrices is a matrix that represents the composition of two linear transformations. Another application of matrices is in the solution of systems of linear equations. If the matrix is square, it is possible to deduce some of its properties by computing its determinant. For example, a square matrix has an inverse if and only if its determinant is not zero. Insight into the geometry of a linear transformation is obtainable (along with other information) from the matrix's eigenvalues and eigenvectors.
Applications of matrices are found in most scientific fields. In every branch of physics, including classical mechanics, optics, electromagnetism, quantum mechanics, and quantum electrodynamics, they are used to study physical phenomena, such as the motion of rigid bodies. In computer graphics, they are used to project a 3-dimensional image onto a 2-dimensional screen. In probability theory and statistics, stochastic matrices are used to describe sets of probabilities; for instance, they are used within the PageRank algorithm that ranks the pages in a Google search.[5] Matrix calculus generalizes classical analytical notions such as derivatives and exponentials to higher dimensions.
A major branch of numerical analysis is devoted to the development of efficient algorithms for matrix computations, a subject that is centuries old and is today an expanding area of research. Matrix decomposition methods simplify computations, both theoretically and practically. Algorithms that are tailored to particular matrix structures, such as sparse matrices and near-diagonal matrices, expedite computations in finite element method and other computations. Infinite matrices occur in planetary theory and in atomic theory. A simple example of an infinite matrix is the matrix representing the derivative operator, which acts on the Taylor series of a function
...
Secant method is mathematical Root finding method. Most of techniques like this method but it is useful and time managing strategy.
So, refer this method its is useful for root finding.
Laplace Transformation & Its ApplicationChandra Kundu
This document presents an overview of the Laplace transform and its applications. It begins with an introduction to Laplace transforms as a mathematical tool to convert differential equations into algebraic expressions. It then provides definitions and properties of both the Laplace transform and its inverse. Examples are given of how Laplace transforms can be used to solve ordinary and partial differential equations, as well as applications in electrical circuits and other fields. The document concludes by noting some limitations of the Laplace transform method and references additional resources.
This word file contains history, applications, pros and cons of numerical integration methods, to be precies (Open Newton cotes and Closed Newton Cotes Methods) along with a flowchart and algorithm explaning the structure and flow of a MATLAB program working on Numerical Integration Methods.
The refernces are also linked in the end.
Presentation on Numerical Method (Trapezoidal Method)Syed Ahmed Zaki
The document discusses the trapezoidal method, which is a technique for approximating definite integrals. It provides the general formula for the trapezoidal rule, explains how it works by approximating the area under a function as a trapezoid, and discusses its history, advantages of being easy to use and having powerful convergence properties. An example application of the trapezoidal rule is shown, along with pseudocode and a C code implementation. The document concludes the trapezoidal rule can accurately integrate non-periodic and periodic functions.
This document provides an overview of the topics covered in the Numerical Methods course CISE-301. It discusses:
- Numerical methods as algorithms used to obtain numerical solutions to mathematical problems when analytical solutions do not exist or are difficult to obtain.
- Specific topics that will be covered, including solution of nonlinear equations, linear equations, curve fitting, interpolation, numerical integration, differentiation, and ordinary and partial differential equations.
- An introduction to Taylor series and how they can be used to approximate functions, along with examples of Maclaurin series expansions.
- How numerical representations of real numbers like floating point can lead to rounding errors, and the concepts of accuracy and precision in numerical calculations.
This document provides information on numerical integration techniques, specifically the Trapezoidal rule and Simpson's rule. It begins with an overview of integration and the basis of the Trapezoidal rule in approximating the integrand as a linear polynomial. It then discusses the derivation and application of the Trapezoidal rule, as well as extending it to multiple segments to improve accuracy. Simpson's rule is introduced as approximating the integrand as a quadratic polynomial. The document gives examples applying single- and multi-segment Trapezoidal and Simpson's rules. It concludes with an introduction to the Romberg rule as an extrapolation method to further increase the accuracy of numerical integration estimates.
A short presentation on the topic Numerical Integration for Civil Engineering students.
This presentation consist of small introduction about Simpson's Rule, Trapezoidal Rule, Gaussian Quadrature and some basic Civil Engineering problems based of above methods of Numerical Integration.
The document discusses numerical methods and their applications. It provides definitions of numerical methods as procedures for solving problems with computable error estimates. Some common numerical methods are listed, including bisection, Newton-Raphson, iteration, and interpolation methods. Applications mentioned include root finding, profit/loss calculation, multidimensional root finding, and simulations. An example is given of using numerical methods for image deblurring. The document also discusses computational modeling, algorithm development and implementation, and limitations of computers in solving mathematical problems.
This document discusses the history and applications of integration. It provides an overview of how integration was developed over time by mathematicians like Archimedes, Gauss, Leibniz, and Newton. It also outlines real-world uses of integration in engineering projects like designing the PETRONAS Towers and Sydney Opera House. The document then explains numerical integration methods like the Trapezoidal Rule, Simpson's Rule, and their variations. It provides formulas and examples of how to apply these rules to approximate definite integrals.
The document discusses sequences and their properties. A sequence is a function whose domain is the positive integers. Sequences are commonly represented using subscript notation rather than standard function notation. The nth term of a sequence is denoted an. [/SUMMARY]
Integration is used in physics to determine rates of change and distances given velocities. Numerical integration is required when the antiderivative is unknown. It involves approximating the definite integral of a function as the area under its curve between bounds. The Trapezoidal Rule approximates this area using straight lines between points, while Simpson's Rule uses quadratic or cubic functions, achieving greater accuracy with fewer points. Both methods involve dividing the area into strips and summing their widths multiplied by the function values at strip points.
There are three main sources of errors in numerical computation: rounding, data uncertainty, and truncation. Rounding errors, also called arithmetic errors, are an unavoidable consequence of working in finite precision arithmetic.
Introduction to Teaching Math to Adult Students in Basic EducationRachel Gamarra
Teaching math to adults is different from teaching math to children. Volunteers in adult education programs will learn how to manage the challenges, implement new ideas, and find resources for their math challenged students. NOTE: Original formatting may have been altered during the upload process.
Slide bài tập huấn phần mềm Geogebra. Phần 7.Bùi Việt Hà
Đây là Slide bài tập huấn GV sử dụng Geogebra, Phần 7.
Đây là bài cuối cùng trong dãy các bài học Geogebra 5.0 dành cho GV các nhà trường PT.
Nội dung: làm quen với cửa sổ các lệnh đại số CAS. Các hàm số học và đại số cơ bản trong Geogebra như khai triển đa thức, phép nhân, chia đa thức, giải phương trình, hệ phương trình, tính tích phân và đạo hàm. Kết nối với các cửa sổ khác trong Geogebra.
Restoring and Non-Restoring division algo for CSEARoy10
Aishwarya Roy presented on division algorithms, specifically restoring and non-restoring techniques. The presentation covered:
- Division algorithms provide a quotient and remainder when dividing two numbers. There are slow and fast algorithms, including restoring and non-restoring.
- Non-restoring division shifts bits in registers and performs ALU operations without restoring bits to the accumulator. Restoring division restores previous accumulator bits when the MSB is 1.
- An example demonstrated non-restoring division with 27 as the dividend and 4 as the divisor. Hardware implementations of non-restoring and restoring division were also described.
- While non-restoring avoids a test subtraction, it requires an extra
Matematika 2 - Slide week 3 - integral substitusi trigonometrikBeny Nugraha
Dokumen ini memberikan penjelasan tentang integral tak tentu, integral tentu, integrasi parsial, dan integral substitusi trigonometri. Integral adalah proses kebalikan dari diferensiasi untuk menemukan anti-derivatif dari suatu fungsi. Terdapat beberapa aturan dalam menghitung integral seperti aturan perkalian konstan dan penjumlahan. Integrasi parsial digunakan untuk menghitung integral yang berisi dua atau lebih fungsi. Substitusi trigonometri diterapkan untuk menghilang
This document discusses sequences and series. It provides definitions of key terms like sequence, finite sequence, infinite sequence, convergent sequence, divergent sequence, monotonic sequence, and geometric progression. It then goes on to solve 4 example problems:
1) It shows that the sequence 2n^2+n/n^2+1 is convergent by taking the limit as n approaches infinity.
2) It uses the ratio test to show that the sequence n!/n^n is convergent.
3) It proves that the sequence 1/1! + 1/2! +...+ 1/n! is convergent by showing it is increasing and bounded.
4) It shows that the sequence
Numerical method errors analysis examines the difference between true and approximate values. Absolute error is the difference between true and approximate values, while relative error is the ratio of absolute error to true value. Percentage error is calculated by taking the absolute difference between true and approximate values, dividing by the absolute true value, and multiplying by 100. Examples are provided to demonstrate calculating absolute, relative, and percentage errors.
Eigen values and eigen vectors engineeringshubham211
mathematics...for engineering mathematics.....learn maths...............................The individual items in a matrix are called its elements or entries.[4] Provided that they are the same size (have the same number of rows and the same number of columns), two matrices can be added or subtracted element by element. The rule for matrix multiplication, however, is that two matrices can be multiplied only when the number of columns in the first equals the number of rows in the second. Any matrix can be multiplied element-wise by a scalar from its associated field. A major application of matrices is to represent linear transformations, that is, generalizations of linear functions such as f(x) = 4x. For example, the rotation of vectors in three dimensional space is a linear transformation which can be represented by a rotation matrix R: if v is a column vector (a matrix with only one column) describing the position of a point in space, the product Rv is a column vector describing the position of that point after a rotation. The product of two transformation matrices is a matrix that represents the composition of two linear transformations. Another application of matrices is in the solution of systems of linear equations. If the matrix is square, it is possible to deduce some of its properties by computing its determinant. For example, a square matrix has an inverse if and only if its determinant is not zero. Insight into the geometry of a linear transformation is obtainable (along with other information) from the matrix's eigenvalues and eigenvectors.
Applications of matrices are found in most scientific fields. In every branch of physics, including classical mechanics, optics, electromagnetism, quantum mechanics, and quantum electrodynamics, they are used to study physical phenomena, such as the motion of rigid bodies. In computer graphics, they are used to project a 3-dimensional image onto a 2-dimensional screen. In probability theory and statistics, stochastic matrices are used to describe sets of probabilities; for instance, they are used within the PageRank algorithm that ranks the pages in a Google search.[5] Matrix calculus generalizes classical analytical notions such as derivatives and exponentials to higher dimensions.
A major branch of numerical analysis is devoted to the development of efficient algorithms for matrix computations, a subject that is centuries old and is today an expanding area of research. Matrix decomposition methods simplify computations, both theoretically and practically. Algorithms that are tailored to particular matrix structures, such as sparse matrices and near-diagonal matrices, expedite computations in finite element method and other computations. Infinite matrices occur in planetary theory and in atomic theory. A simple example of an infinite matrix is the matrix representing the derivative operator, which acts on the Taylor series of a function
...
Secant method is mathematical Root finding method. Most of techniques like this method but it is useful and time managing strategy.
So, refer this method its is useful for root finding.
Laplace Transformation & Its ApplicationChandra Kundu
This document presents an overview of the Laplace transform and its applications. It begins with an introduction to Laplace transforms as a mathematical tool to convert differential equations into algebraic expressions. It then provides definitions and properties of both the Laplace transform and its inverse. Examples are given of how Laplace transforms can be used to solve ordinary and partial differential equations, as well as applications in electrical circuits and other fields. The document concludes by noting some limitations of the Laplace transform method and references additional resources.
This word file contains history, applications, pros and cons of numerical integration methods, to be precies (Open Newton cotes and Closed Newton Cotes Methods) along with a flowchart and algorithm explaning the structure and flow of a MATLAB program working on Numerical Integration Methods.
The refernces are also linked in the end.
Presentation on Numerical Method (Trapezoidal Method)Syed Ahmed Zaki
The document discusses the trapezoidal method, which is a technique for approximating definite integrals. It provides the general formula for the trapezoidal rule, explains how it works by approximating the area under a function as a trapezoid, and discusses its history, advantages of being easy to use and having powerful convergence properties. An example application of the trapezoidal rule is shown, along with pseudocode and a C code implementation. The document concludes the trapezoidal rule can accurately integrate non-periodic and periodic functions.
This document provides an overview of the topics covered in the Numerical Methods course CISE-301. It discusses:
- Numerical methods as algorithms used to obtain numerical solutions to mathematical problems when analytical solutions do not exist or are difficult to obtain.
- Specific topics that will be covered, including solution of nonlinear equations, linear equations, curve fitting, interpolation, numerical integration, differentiation, and ordinary and partial differential equations.
- An introduction to Taylor series and how they can be used to approximate functions, along with examples of Maclaurin series expansions.
- How numerical representations of real numbers like floating point can lead to rounding errors, and the concepts of accuracy and precision in numerical calculations.
This document provides information on numerical integration techniques, specifically the Trapezoidal rule and Simpson's rule. It begins with an overview of integration and the basis of the Trapezoidal rule in approximating the integrand as a linear polynomial. It then discusses the derivation and application of the Trapezoidal rule, as well as extending it to multiple segments to improve accuracy. Simpson's rule is introduced as approximating the integrand as a quadratic polynomial. The document gives examples applying single- and multi-segment Trapezoidal and Simpson's rules. It concludes with an introduction to the Romberg rule as an extrapolation method to further increase the accuracy of numerical integration estimates.
A short presentation on the topic Numerical Integration for Civil Engineering students.
This presentation consist of small introduction about Simpson's Rule, Trapezoidal Rule, Gaussian Quadrature and some basic Civil Engineering problems based of above methods of Numerical Integration.
The document discusses numerical methods and their applications. It provides definitions of numerical methods as procedures for solving problems with computable error estimates. Some common numerical methods are listed, including bisection, Newton-Raphson, iteration, and interpolation methods. Applications mentioned include root finding, profit/loss calculation, multidimensional root finding, and simulations. An example is given of using numerical methods for image deblurring. The document also discusses computational modeling, algorithm development and implementation, and limitations of computers in solving mathematical problems.
This document discusses the history and applications of integration. It provides an overview of how integration was developed over time by mathematicians like Archimedes, Gauss, Leibniz, and Newton. It also outlines real-world uses of integration in engineering projects like designing the PETRONAS Towers and Sydney Opera House. The document then explains numerical integration methods like the Trapezoidal Rule, Simpson's Rule, and their variations. It provides formulas and examples of how to apply these rules to approximate definite integrals.
The document discusses sequences and their properties. A sequence is a function whose domain is the positive integers. Sequences are commonly represented using subscript notation rather than standard function notation. The nth term of a sequence is denoted an. [/SUMMARY]
Integration is used in physics to determine rates of change and distances given velocities. Numerical integration is required when the antiderivative is unknown. It involves approximating the definite integral of a function as the area under its curve between bounds. The Trapezoidal Rule approximates this area using straight lines between points, while Simpson's Rule uses quadratic or cubic functions, achieving greater accuracy with fewer points. Both methods involve dividing the area into strips and summing their widths multiplied by the function values at strip points.
There are three main sources of errors in numerical computation: rounding, data uncertainty, and truncation. Rounding errors, also called arithmetic errors, are an unavoidable consequence of working in finite precision arithmetic.
Introduction to Teaching Math to Adult Students in Basic EducationRachel Gamarra
Teaching math to adults is different from teaching math to children. Volunteers in adult education programs will learn how to manage the challenges, implement new ideas, and find resources for their math challenged students. NOTE: Original formatting may have been altered during the upload process.
Slide bài tập huấn phần mềm Geogebra. Phần 7.Bùi Việt Hà
Đây là Slide bài tập huấn GV sử dụng Geogebra, Phần 7.
Đây là bài cuối cùng trong dãy các bài học Geogebra 5.0 dành cho GV các nhà trường PT.
Nội dung: làm quen với cửa sổ các lệnh đại số CAS. Các hàm số học và đại số cơ bản trong Geogebra như khai triển đa thức, phép nhân, chia đa thức, giải phương trình, hệ phương trình, tính tích phân và đạo hàm. Kết nối với các cửa sổ khác trong Geogebra.
Ito Lemması olarak bilinen stokastik analiz ve modelleme tekniği, stokastik modellerin çözümünde olağanüstü katkı sağlayan bir yaklaşım olarak yaygın bir şekilde uygulanıyor.
1. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.,2007 1
8. SAYISAL TÜREV ve İNTEGRAL
=Değişimin matematiği
Mühendisler değişen sistemler ve süreçlerle sürekli
olarak uğraşmak zorunda oldukları için türev ve
integral kavramları mesleğimizin temel araçları
arasındadır.
Bağımlı değişkenin / bağımsız değişken
x∆
t∆
X∆ t∆
2. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.2
x
xfxxf
x
f ii
∆
−∆+
=
∆
∆ )()(
Türev Tanımı: (matematikte),
fark (difference) yaklaşımı idi
x
xfxxf
x
xf
xf ii
x ∆
−∆+
=
∂
∂
=
→∆
)()()(
)(' lim0
• Diferansiyel, farkları belirlemek, ayırmak anlamına gelir
3. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.3
Mühendislikte türev
VL=L , ic=C
•Mühendislikte bir çok yasa ve genelleştirme,
fiziksel dünyada karşılıkları olan değişimlerin
tahmin edilmesi esasına dayanmaktadır.
•Newton’un ikinci yasası temel bir örnek olup, bir cismin konumuyla
değil, konumunun zamana göre değişimiyle ilgilenmektedir
v= dX/dt
•Isı geçişleri, sıcaklık farkına bağlı olarak, akım yasası potansiyel farkına
bağlı olarak ifade edilir.
• Benzer şekilde, L,C elemanlarının uç denklemleri;
4. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.4
İntegral Tanımı
Yüksek matematikte diferansiyelin
ters işlemi; integraldir
Sum [ f(x)dx dilimleri ]
Birleştirme, biraraya getirme, toplama(sum)
f(xi)dx
…
…
…
…
f(x)
f(xi)dx ∫
200
0
dx)x(f
S
5. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.5
Mühendislikte integral: (fonksiyonun-
eğrinin altında kalan alan)
(a) (b) (c)
6. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.6
8.1) Sayısal Türev
8.1.1. İki noktalı basit türev yaklaşımları
a) Geri Fark Yaklaşımı
(8.4)
Geri Fark Formülü
Şekil.8.2. Geri Fark Yaklaşımı
7. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.7
8.1.1. İki noktalı basit türev yaklaşımları
a) İleri Fark Yaklaşımı
(8.5)
İleri Fark Formülü
Şekil.8.3. İleri Fark Yaklaşımı
b) Merkez Fark Yaklaşımı
(8.6)
Merkez Fark Formülü
Şekil.8.4. Merkez Fark Yaklaşımı
8. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.8
Örnek: y=x2
işlevinin x=2’deki türevini h=0.1 kullanarak
her üç yöntemle yaklaşık olarak bulunuz.
a) İleri fark yöntemiyle
b) Geri fark yöntemiyle
c) Merkez fark yöntemiyle
9. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.9
8.1.2. Taylor Serisi yardımıyla
çok noktalı türev yaklaşımları
İki noktalı türev yaklaşımları
!
)(
.................................
!2
)(''
!1
)('
)()(
21
n
xfhxfhxfh
xfhxf i
nn
ii
ii +++=+
!2
)(''
!1
)('
)()(
21
ii
ii
xfhxfh
xfhxf ++=+
( ) ( )
!2
)(''2
!1
)('2
)()2(
21
xfhxfh
xfhxf ii ++=+
-4
+
10. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.10
2
)(''
4)('4)(4)(4
2
i
iii
xfh
xhfxfhxf −−−=+−
2
)(''4
)('2)()2(
2
xfh
xhfxfhxf iii ++=+
=
veya kısaca
=
+
İki noktalı türev yaklaşımları : Taylor serisi için ileri fark yöntemi
Taylor serisi için ileri fark formülü
11. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.11
b) Aynı işlemler, geriye (xi-1 noktasına ) doğru yapılırsa
fi
xi+1xi
h
fi+1
fi-1
h
xi-1 xi+2xi-2
fi-2
fi+2
Şekil.8.5. Taylor Serisi yardımıyla iki noktalı türev yaklaşımları
12. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.12
( ) ( )
!2
)x(''fh
!1
)x('fh
)x(f)hx(f i
2
i
1
ii
−
+
−
+=−
( ) ( )
!2
)(''2
!1
)('2
)()2(
21
xfhxfh
xfhxf ii
−
+
−
+=−
=
İki noktalı türev yaklaşımları : Taylor serisi için geri fark yöntemi
Taylor serisi için geri fark formülü
13. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.13
Üç noktalı türev yaklaşımları
Taylor serileri 3. dereceden kuvvetlerine kadar açılarak ve yine taraf tarafa yok etme işlemleri
kullanılarak 1. 2. ve 3. dereceden türevleri yaklaşık olarak bulunabilir. Buradan
= (8.15)
= (8.16)
= (8.17)
Ödev: Taylor serisine açarak bu denklemleri ispatlayın
14. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.14
Örnek: f(x)=ex-2
işlevinin x=2 noktasındaki
yaklaşık türevini gördüğümüz yöntemlerle
bulunuz. ( h=0,1 Analitik çözüm: )1e)2('f 22
== −
Çözüm:
• İki noktalı ileri farkla çözüm
,
= olduğundan, = =0.9964
• Basit ileri farkla çözüm;
= 1,0517
15. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.15
• İki noktalı geri farkla çözüm
,
= olduğundan, = =0.99705
• Basit geri farkla çözüm;
=
Merkez farkla çözüm;
= 1,001
Örnek (devam)
16. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.16
8.2) Sayısal İntegral
x= t
f(x)=T
Şekil.8.6. Bir sisteme ait 1’er dakika aralıklarla alınmış ayrık sıcaklık verileri
17. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.17
Örnek:
x f(x)
0.25 2.599
0.75 2.414
1.25 1.945
1.75 1.993
( ) dxe
x
x x5.0
2
0
2/3
sin5.01
1cos2
∫ +
++
x
f(x)
0 0.25 0.75 1.25 1.75
18. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.18
8.2.1. Basit İntegral Yaklaşımları
Alt Değer Yaklaşımı
xi+h
f(xi)
f (xi+h)
xi
f (x)
x
Şekil.8.8. Alt Değer Yaklaşımı
( ) ( )hxfIdxxf iA
hx
x
i
i
=≡∫
+
19. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.19
Üst Değer Yaklaşımı
xi+h
f(xi)
f (xi+h)
xi
f (x)
x
( ) ( )hhxfIdxxf iÜ
hx
x
i
i
+=≡∫
+
xi +h
f(xi )
f(xi +h)
xi xi+h/2
f(x)
x
f(xi+h/2)
Orta Nokta Yaklaşımı
h
h
xfI iÜ
+=
2
20. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.20
8.2.2. Newton-Cotes Formülleri
=ao+a1x+........anxn
( ) ( )dxxfdxxfI
b
a
n
b
a
∫∫ ≅=
= f(a)+
8.2.2.1. Trapez (Yamuk) Kuralı
f1(x)
b,f(b)
a, f(a) doğrusal interpolasyon
I= ∫
b
a
[f(a)+ )(
)()(
ax
ab
afbf
−
−
−
]dx
I=(b-a)*
2
)()( afbf +
21. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.21
b
f(a)
f (b)
a Taban
f (x)
x
Trapez (Yamuk) Kuralı
I=Taban * ortalama yükseklik
I=(b-a)*
2
)()( afbf +
22. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.22
Trapez kuralı’nın tekli uygulaması
Örnek:
f(x) = 0.2+25x-200x2
+675x3
-900x4
+400x5
işlevinin x=0’dan 0.8’e kadar trapez kuralı ile
integralini alın.
(İntegralin analitik çözümü:1.640533)
Çözüm: İşlevin verilen noktalardaki değerleri;
f(0)=0.2, f(0.8)=0.232 bulunur . Eşitlikte yerine koyulursa
I=(b-a)* bulunur.
Hata
Et=1.640533-0.1728=1.467733
Sonuç %89.5 bağıl hatayla bulunmuştur.
f(x)
-
-
-
2.0-
-
-
-
0
Hata
İntegral Tahmini .
0.8 x
-Şekil.8.12. Aralığın büyük seçilmesi sonucu
integral hatası(Chapra S.,Canale,R., 2003)
23. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.23
Trapez kuralı’nın çoklu uygulaması
f2
xn-1x2.......
h
fn-1
f1
I2
x1 xnx0
f0
fn
I1
I1= , I2=
Şekil.8.13. Çoklu uygulamalarda trapez kuralı
I=I1+I2+................In Burada
I=
I=
Trapez kuralının çoklu uygulaması için genelleştirilmiş formül
1980’lerde Türkçemize giren deyim; “toplanıp Voltranı
oluşturmak”
24. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.24
Kalbin pompaladığı kan debisini ölçmek için kullanılan standart teknik, Hamilton tarafından geliştirilen
indikatör seyrelmesidir. Küçük bir sondanın bir ucu radyal bir atardamara sokulur ve diğer ucu kan içindeki
boyanın (indikatör) derişikliğini otomatik olarak kaydedebilen bir yoğunluk ölçere bağlanır. Bilinen miktarda
boya (5.6 mg) hızlı bir şekilde enjekte edilir ve Tablo’daki veriler alınır.
Boya seyrelmesinde elde edilen bu sonuçların grafiği Şekil’de görülmektedir. Derişim 15 sn civarında en yüksek
değere ulaşmakta, daha sonra düşmektedir ve bu düşüşü yeniden dolaşan boya nedeniyle bir artış izlemektedir.
Yeniden dolaşımın etkisini gözardı etmek için
analistler derişim eğrisini düz bir doğru şeklinde
uzatırlar. Bu durumda derişim ( fD(t) ): t=23.
saniyede 1.1, t=25. saniyede 0.9, t=27. saniyede
0.45 ve t=29. saniyede 0 olmaktadır. Daha
sonra kalp çıktısı (cardiac output) şöyle
hesaplanabilmektedir;
C= , Burada C kalp debisi [L/dakika],
M=enjekte edilen boya miktarı (mg),
60=dakikayı saniyeye çeviren katsayı (s/dakika)
ve A= eğrinin (Analistler tarafından düzeltilmiş
haliyle!) altında kalan alandır ((mg/L)*s).
t1=5. ile t13=29. saniyeler arasında, 2s adım
büyüklüğüyle, trapez kuralınının çoklu uygulamasını kullanarak bu hastanın kalp debisini hesaplayın.
(Trapez formülü : I= )
Örnek:
25. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.25
Çözüm:adım büyüklüğü h=2 sn I= idi.
f1= f(5)=0, f2= f(7)=0.1, f3= f(9)=0.11, f4= f(11)=0.4,
f5= f(13)=4, f6=f(15)=9, f7=f(17)=7.9, f8=f(19)=4.1,
f9=f(21)=2.2, f10=f(23)=1.1, f11=f(25)=0.9,
f12=f(27)=0.45, fn= f13=f(29)=0
)f2ff(
2
h 1n
2k
kn1 ∑
−
=
++
)f2ff(
2
2
IAdt)t(f
12
2k
k131
29
5
D ∑∫ =
++===
L/dk5.55188dakika/s60*
s*)L/mg(52.60
mg6.5
60*
A
M
C:Debi ===
= 0+0+2*(0.1+0.11+0.4+4+9+7.9+4.1+2.2+1.1+0.9+0.45) =60.52 mg/L
26. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.26
Soru:a) Aynı veriler ve yöntemi kullanarak kalp debisini hesaplayacak bir
bilgisayar algoritması oluşturun.
b) ve programını yazın
a)
İlk Değerleri Ata
M, n,h, Toplam
H
k=k+1
k=2
?)1n(k −≤E
Yoğunlukölçerden alınıp düzeltilen
tüm verileri gir f1……..fn
∑
−
=
)1n(
2k
kf = Toplam
++= ∑
−
=
)1n(
2k
kn1 f*2ff
2
h
A
Toplam=Toplam+f(k)
C=(M/A)*60
b)
27. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.27
8.2.2.2.Simpson Kuralları
f(x)
x
Şekil.8.14. 2. dereceden polinom
f(x)
x
Şekil.8.15. 3. dereceden polinom
28. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.28
Simpson’un 1/3 Kuralı
( ) ( )dxxfdxxfI
b
a
2
b
a
∫∫ ≅=
( )( )
( )( )
( )( )
( )( )
( )( )
( )( )
dxxf
xxxx
xxxx
xf
xxxx
xxxx
xf
xxxx
xxxx
I
x
xo
∫
−−
−−
+
−−
−−
+
−−
−−
=
2
)()()( 2
1202
10
1
2101
20
0
2010
21
( )xf2
x1, f(x1)
2. Dereceden Lagrange İnterpolasyon Polinomu
x2, f(x2)
x3, f(x3)
( )[ ])()(4
3
210 xfxfxf
h
I ++≅
Simpson’un 1/3 Kuralı (İkinci Newton Cotes İntegral Formülü)
h= 2
ab − ( )[ ]
yükseklikOrtalama
Taban
xfxfxf
abI
6
)()(4
)( 210 ++
−≅
a=x0, b=x2’dir. x1 ise a ve b’nin ortasındaki nokta
29. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.29
Simpson’un 1/3 Kuralının Tekli Uygulaması:
Örnek: f(x)=0.2+25x-200x2
+675x3
-900x4
+400x5
işlevini a=0’dan b=0.8’e kadar
Simpson’un 1/3 kuralıyla sayısal olarak integre edin. (İntegralin tam
değeri:1.640533 idi)
Çözüm: f(0)=0.2, f(0.4)=2.456, f(0.8)=0.232 ‘dir. Integral değeri
Bu değer yamuk yöntemiyle çözüme göre daha doğru bir sonuç bulmuştur.
Et=1.640533-1.367467=0.2730667, yüzde bağıl hatası %16.6’dır.
30. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.30
Simpson’un 1/3 Kuralının Çoklu Uygulaması:
I=I1+I2+................In
I=
h=
n
ab −
I=
( )[ ]
yükseklikOrtalama
Taban
xfxfxf
abI
6
)()(4
)( 210 ++
−≅
31. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.31
Örnek: : f(x) = 0.2+25x-200x2
+675x3
-900x4
+400x5
işlevinin a=0’dan b=0.8’e kadar
Simpson’un 1/3 kuralını kullanarak n=4 aralık için integre edin. (İntegralin tam
değeri:1.640533 idi)
Çözüm: n=4, h=(0.8-0)/4=0.2 x0=0, x1=0.2, x2=0.4, x3=0.6, x4=0.8
f(0)=0.2 f(0.2)=1.288
f(0.4)=2.456 f(0.6)=3.464
f(0.8)=0.232
Et=1.640533-1.623467=0.017067. Bağıl yüzde hatası %1.04 bulunur.
32. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.32
İlk Değerleri Ata
n, b, a, h, ToplamTekler=0,
ToplamÇiftler=0, f0
H
i=i+1
i=1
?ni ≤E
h=0.2 aralıklarla tüm noktalarda sırayla
fonksiyonun aldığı değerler bulunur
f1, f2.........fn= f(0.2)........f(0.8)
H
i=i+1
i=1
?2/ni ≤ETek x sayıları için fonksiyonların aldığı
değerlerin toplamını bul
ToplamTekler=ToplamTekler+f(i)
H
i=i+1
i=1
?2/)2( −≤ niEÇift x sayıları için fonksiyonların aldığı
değerlerin toplamını bul
ToplamCiftler=ToplamCiftler+f(i)
∑=
−
2/
1
12
n
i
if =Toplam Tek Sayılar
∑
−
=
2/)2(
1
2
n
i
if = Toplam Çift Sayılar
( )
+++
−=
∑ ∑=
−
=
−
n
ffff
abI
n
i
n
i
iin
3
24
2/
1
2/)2(
1
2120
Program Algoritması
Simpson’un 1/3 kuralının
çoklu uygulaması için örnek algoritma
34. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.34
Simpson’un 3/8 Kuralı
Diğer iki yöntemin türetilmesine benzer şekilde, üçüncü dereceden bir Lagrange polinomu
dört noktadan geçirilebilir ve integrali alınacak f(x) işlevi yerine kullanılabilir.
Üçüncü dereceden Lagrange polinomunun integrali;
veya
Simpson’un 3/8 kuralı (3. Newton Cotes integral formülü):
35. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.35
Sayısal Türev ve İntegralin Elektrik-Elektronik Mühendisliğinde Uygulamaları
Bir periyot boyunca salınan bir elektrik akımının ortalama değeri sıfır olabilir. Örneğin
akımın basit bir sinüsle tanımlandığını varsayalım: i(t)=sin(2 /T). Burada T periyottur. Bu
işlevin ortalama değeri aşağıdaki eşitlikle hesaplanabilir.
i= =
Burada net sonucun sıfır olması gerçeğine karşın, bu akım bir iş yapabilir ve ısı üretebilir.
Ortalama değeri sıfır olsa da bu tür etkilerinden dolayı etkili veya etkin akım değeri olarak
adlandırılır. Bu nedenle elektik mühendisleri bu tür bir akımı genellikle aşağıdaki eşitlikle
tanımlarlar. (RMS: Roots of mean square:karesel ortalamanın karekökü) :
IRMS=
Burada i(t): t anındaki anlık akımdır.
36. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.36
Ödev: T=1sn için şekilde görülen dalganın etkin akımını trapez ve Simpson 1/3 kurallarıyla 4
aralık için bulun. Bağıl yüzde hatayı bulun. (Gerçek değer 15.41261, % )
Şekil.8.18. Yarım periyot için sinüzoidal akım işareti
37. Serhat YILMAZ, Elektronik ve Hab,Kocaeli Ün.37
Şekilde değişimi verilen akımın etkin değerini Simpson’un 1/3 integral formülünü kullanarak h= adımı ile
hesaplayınız. Burada akım; şekilden de görüldüğü gibi ve katlarında periyodik olarak başlayan (iletime
geçen), genliği 1.45A, periyodu olan sinüzoidal bir işarettir. Dolayısıyla taralı bölgeler simetrik ve
alanları eşittir.
ietkin= , A=(b-a) ,h= , Radyan= )
Soruyu çözecek a) algoritmayı oluşturun b) programı yazın.
Ödev.2.
Kaynaklar
• Müh. İçin Say. Yöntemler, CAPRA,S
ve diğ., Literatür Yayınları
• Sayısal Çözümleme,Aktaş Z., ODTÜ
Yayınları
• Applied Num. Analysis, Gerald,C.F. ve
diğ. Addison Wesley Pub.
• Sayısal Çözümleme Ders Notları,
Bilgin, M.Z., Kocaeli Ün., Elektrik
Müh. Bölümü