The document discusses various operations that can be performed on continuous time signals, including shifting, scaling, reflection, and decomposing a signal into its even and odd parts. It provides examples of applying each operation to a sample signal and includes the corresponding Matlab code. Key operations covered are shifting a signal by adding a time delay, compressing or expanding a signal through scaling, flipping the signal vertically through reflection, and extracting the even and odd parts of a signal based on their symmetry properties.
1) The document discusses various topics related to digital communication including sampling theory, analog to digital conversion, pulse code modulation, quantization, coding, and time division multiplexing.
2) In analog to digital conversion, an analog signal is sampled, quantized by assigning it to discrete amplitude levels, and coded by mapping each level to a binary sequence.
3) The Nyquist sampling theorem states that a signal must be sampled at a rate at least twice its highest frequency to avoid aliasing when reconstructing the original signal.
Cauchy integral theorem & formula (complex variable & numerical method )Digvijaysinh Gohil
油
1) The document discusses the Cauchy Integral Theorem and Formula. It states that if a function f(z) is analytic inside and on a closed curve C, then the integral of f(z) around C is equal to 0.
2) It provides examples of evaluating integrals using the Cauchy Integral Theorem when the singularities lie outside the closed curve C.
3) The Cauchy Integral Formula is introduced, which expresses the value of an analytic function F(a) inside C as a contour integral around C. Examples are worked out applying this formula to find the value and derivatives of functions at points inside C.
This document provides an overview of signals and systems. It defines a signal as a pattern of variation that carries information over time. Signals can be continuous or discrete. Systems process input signals to produce output signals. A system can be represented as the ratio between its output and input signals. Examples of systems include electrical circuits and communication systems. Properties like linearity, time-invariance, and causality define important classes of systems. Key signal types include periodic, exponential, step, and pulse signals. Continuous systems are often modeled with differential equations while discrete systems use difference equations.
presentation on digital signal processingsandhya jois
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The document discusses digital signal processing (DSP). It defines key terms like digital, signal, and processing. It explains how analog signals are converted to digital form by sampling and quantization. It also describes common digital modulation schemes and compares DSP processors to microprocessors. Finally, it discusses digital filters and their types as well as applications of DSP in areas like audio processing, communications, and imaging.
This document provides an overview of the continuous-time Fourier transform. It introduces the Fourier integral and defines the Fourier transform pair. It discusses properties of the Fourier transform including linearity, time scaling, time reversal, time shifting, frequency shifting, and properties for real functions. Examples are provided to illustrate these concepts and properties. The document also reviews the discrete Fourier transform and Fourier series to provide context and comparison to the continuous-time Fourier transform.
Digital signal processing involves processing digital signals using digital computers and software. There are several types of signals that can be classified based on properties like being continuous or discrete in time and value, deterministic or random, and single or multichannel. Common signals include unit impulse, unit step, and periodic sinusoidal waves. Signals can also be categorized as energy signals with finite energy, power signals with finite power, and even/odd based on their symmetry. Digital signal processing is used in applications like speech processing, image processing, and more.
The document discusses various ways to classify signals. Signals can be classified based on parameters such as the independent variable (e.g. continuous time vs. discrete time signals), dependent variable (e.g. analog vs. digital signals), number of independent variables (e.g. one-dimensional, two-dimensional, multi-dimensional signals), periodicity (e.g. periodic vs. aperiodic signals), determinism (e.g. deterministic vs. random signals), causality (e.g. causal, anti-causal, non-causal signals), and energy content (e.g. energy signals, power signals). Continuous time signals have values defined over a continuum of time, while discrete time signals
The document discusses sampling theory and analog-to-digital conversion. It begins by explaining that most real-world signals are analog but must be converted to digital for processing. There are three steps: sampling, quantization, and coding. Sampling converts a continuous-time signal to a discrete-time signal by taking samples at regular intervals. The sampling theorem states that the sampling frequency must be at least twice the highest frequency of the sampled signal to avoid aliasing. Finally, it provides an example showing how to calculate the minimum sampling rate, or Nyquist rate, given the highest frequency of a signal.
The document summarizes several key properties of systems:
- Memory refers to whether a system's output depends on current and/or past input values. Causal systems are memoryless while anticausal systems require memory.
- Linearity means a system's output is a linear combination of its inputs. A linear system satisfies superposition and homogeneity.
- Time-invariance means a system's properties do not change over time such that a time shift of the input results in the same time shift of the output.
- Invertibility refers to whether the input of a system can be recovered from its output. An invertible system has a corresponding inverse system.
This document discusses the discrete Fourier transform (DFT) and fast Fourier transform (FFT). It begins by contrasting the frequency and time domains. It then defines the DFT, showing how it samples the discrete-time Fourier transform (DTFT) at discrete frequency points. It provides an example 4-point DFT calculation. It discusses the computational complexity of the direct DFT algorithm and how the FFT reduces this to O(N log N) by decomposing the DFT into smaller transforms. It explains the decimation-in-time FFT algorithm using butterfly operations across multiple stages. Finally, it notes that the inverse FFT can be computed using the FFT along with conjugation and scaling steps.
Generation of DSB-SC using Diode Ring Modulator or chopper Modulator.pptxArunChokkalingam
油
This document discusses a ring modulator method for generating a double sideband suppressed carrier (DSB-SC) signal using amplitude modulation. It has advantages like a stable output and not requiring external power. The operation involves using diodes in a ring configuration to selectively pass or block the carrier signal depending on the polarity of the modulating signal. Coherent detection can then be used to recover the message signal from the DSB-SC by synchronizing the local carrier signal.
Satellite communication involves transmitting information from one location to another using an artificial satellite orbiting Earth. A communication satellite receives signals from transmitting ground stations, amplifies and processes the signals, and transmits them back to receiving ground stations on Earth. The key components of satellite communication systems are the space segment, consisting of the satellite, and the ground segment, consisting of transmitting and receiving earth stations.
Study Material Numerical Solution of Odinary Differential EquationsMeenakshisundaram N
油
1. The document provides information about a numerical methods course for physics majors at Vivekananda College in Tiruvedakam West, including the reference textbook and details about Unit V on numerical solutions of ordinary differential equations.
2. It introduces the concept of using Taylor series approximations to find numerical solutions to differential equations, providing the general Taylor series expansion formula and explaining how to derive the terms needed to solve specific differential equations.
3. It gives examples of using the Taylor series method to solve sample ordinary differential equations, finding approximate values of y at increasing values of x to several decimal places.
This document summarizes a seminar report on discrete time systems and the Z-transform. It defines discrete time systems and different types of systems including causal/noncausal, linear/nonlinear, time-invariant/variant, static/dynamic. It then explains the Z-transform, its properties including region of convergence and time shifting. Some common Z-transform pairs are provided along with methods for the inverse Z-transform. Advantages of the Z-transform for analysis of discrete systems and signals are mentioned.
This document provides information about the ECE 4790 Electrical Communications course offered in fall 1999. It includes details about the course structure, policies, labs, and necessary background. The course consists of 3 hours of lecture and 2 hours of lab per week. Labs are done using MATLAB and are due weekly. Grades are based on tests, the final, labs, and homework. The course covers topics such as signals, channels, modulation, source coding, and spread spectrum.
The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
The gradient of a function is the collection of its partial derivatives, and is a vector field always perpendicular to the level curves of the function.
Digital signal processing involves processing digital signals using digital computers and software. There are several types of signals that can be classified based on properties like being continuous or discrete in time and value, deterministic or random, and single or multichannel. Common signals include unit impulse, unit step, and periodic sinusoidal waves. Signals can also be categorized as energy signals with finite energy, power signals with finite power, and even/odd based on their symmetry. Digital signal processing is used in applications like speech processing, image processing, and more.
The document discusses various ways to classify signals. Signals can be classified based on parameters such as the independent variable (e.g. continuous time vs. discrete time signals), dependent variable (e.g. analog vs. digital signals), number of independent variables (e.g. one-dimensional, two-dimensional, multi-dimensional signals), periodicity (e.g. periodic vs. aperiodic signals), determinism (e.g. deterministic vs. random signals), causality (e.g. causal, anti-causal, non-causal signals), and energy content (e.g. energy signals, power signals). Continuous time signals have values defined over a continuum of time, while discrete time signals
The document discusses sampling theory and analog-to-digital conversion. It begins by explaining that most real-world signals are analog but must be converted to digital for processing. There are three steps: sampling, quantization, and coding. Sampling converts a continuous-time signal to a discrete-time signal by taking samples at regular intervals. The sampling theorem states that the sampling frequency must be at least twice the highest frequency of the sampled signal to avoid aliasing. Finally, it provides an example showing how to calculate the minimum sampling rate, or Nyquist rate, given the highest frequency of a signal.
The document summarizes several key properties of systems:
- Memory refers to whether a system's output depends on current and/or past input values. Causal systems are memoryless while anticausal systems require memory.
- Linearity means a system's output is a linear combination of its inputs. A linear system satisfies superposition and homogeneity.
- Time-invariance means a system's properties do not change over time such that a time shift of the input results in the same time shift of the output.
- Invertibility refers to whether the input of a system can be recovered from its output. An invertible system has a corresponding inverse system.
This document discusses the discrete Fourier transform (DFT) and fast Fourier transform (FFT). It begins by contrasting the frequency and time domains. It then defines the DFT, showing how it samples the discrete-time Fourier transform (DTFT) at discrete frequency points. It provides an example 4-point DFT calculation. It discusses the computational complexity of the direct DFT algorithm and how the FFT reduces this to O(N log N) by decomposing the DFT into smaller transforms. It explains the decimation-in-time FFT algorithm using butterfly operations across multiple stages. Finally, it notes that the inverse FFT can be computed using the FFT along with conjugation and scaling steps.
Generation of DSB-SC using Diode Ring Modulator or chopper Modulator.pptxArunChokkalingam
油
This document discusses a ring modulator method for generating a double sideband suppressed carrier (DSB-SC) signal using amplitude modulation. It has advantages like a stable output and not requiring external power. The operation involves using diodes in a ring configuration to selectively pass or block the carrier signal depending on the polarity of the modulating signal. Coherent detection can then be used to recover the message signal from the DSB-SC by synchronizing the local carrier signal.
Satellite communication involves transmitting information from one location to another using an artificial satellite orbiting Earth. A communication satellite receives signals from transmitting ground stations, amplifies and processes the signals, and transmits them back to receiving ground stations on Earth. The key components of satellite communication systems are the space segment, consisting of the satellite, and the ground segment, consisting of transmitting and receiving earth stations.
Study Material Numerical Solution of Odinary Differential EquationsMeenakshisundaram N
油
1. The document provides information about a numerical methods course for physics majors at Vivekananda College in Tiruvedakam West, including the reference textbook and details about Unit V on numerical solutions of ordinary differential equations.
2. It introduces the concept of using Taylor series approximations to find numerical solutions to differential equations, providing the general Taylor series expansion formula and explaining how to derive the terms needed to solve specific differential equations.
3. It gives examples of using the Taylor series method to solve sample ordinary differential equations, finding approximate values of y at increasing values of x to several decimal places.
This document summarizes a seminar report on discrete time systems and the Z-transform. It defines discrete time systems and different types of systems including causal/noncausal, linear/nonlinear, time-invariant/variant, static/dynamic. It then explains the Z-transform, its properties including region of convergence and time shifting. Some common Z-transform pairs are provided along with methods for the inverse Z-transform. Advantages of the Z-transform for analysis of discrete systems and signals are mentioned.
This document provides information about the ECE 4790 Electrical Communications course offered in fall 1999. It includes details about the course structure, policies, labs, and necessary background. The course consists of 3 hours of lecture and 2 hours of lab per week. Labs are done using MATLAB and are due weekly. Grades are based on tests, the final, labs, and homework. The course covers topics such as signals, channels, modulation, source coding, and spread spectrum.
The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
The gradient of a function is the collection of its partial derivatives, and is a vector field always perpendicular to the level curves of the function.
1. EXPLICITAREA
RECURENELOR
FUNDAMENTALE
Tutorial redactat de Silviu Boga, mail: silviumath@yahoo.com
Cuprins:
Recuren釘a telescopic aditiv
Progresiile aritmetice
Recuren釘a telescopic multiplicativ
Progresiile geometrice
Recuren釘a liniar neomogen de ordin I, cu coeficien釘i variabili
Recuren釘a liniar neomogen de ordin I, cu coeficien釘i constan釘i
Recuren釘a liniar omogen de ordin II, cu coeficien釘i variabili
Recuren釘a liniar omogen de ordin II, cu coeficien釘i constan釘i
Recuren釘a liniar neomogen de ordin II, cu coeficien釘i constan釘i
Recuren釘e liniare omogene de ordin superior, cu coeficien釘i constan釘i
Recuren釘e liniare neomogene de ordin superior, cu coeficien釘i constan釘i
Recuren釘a omografic, cu coeficien釘i variabili
Recuren釘a omografic, cu coeficien釘i constan釘i
Not:
- click pe titlul din cuprins pentru hyperlink spre fiecare recuren
- click pe numrul paginii pentru a reveni la cuprins
2. EXPLICITAREA RECUREN鄭ELELOR FUNDAMENTALE
La fiecare din recuren釘ele urmtoare - fundamentale datorit prezen釘ei lor 樽n
numeroase ra釘ionamente matematice am prezentat, pe cazul general dar i pe un
exemplu, procedura optim de explicitare. Prin rezolvarea temei de aprofundare,
cititorul interesat se va putea apoi rapid acomoda cu judec釘ile expuse.
1. Recuren釘a telescopic aditiv
xn 1 xn an , ()n *
x1 termen ini釘ial dat
(a ) ir explicit dat
n n *
Explicitare
Din rela釘ia de recuren釘, cum xn1 xn an , ()n * , prin particularizare i
sumare are loc supranumita reducere telescopic i explicitarea este astfel finalizat:
x2 x1 a1
x3 x2 a2
x4 x3 a3
xn 1 xn 2 an 2
xn xn 1 an 1
____________
() n 1 n 1
xn x1 ak xn x1 ak
k 1 k 1
n 1
n aplica釘iile curente suma iterat a
k 1
k
se va constata de regul calculabil.
Se re釘in formulele de calcul pentru principalele sume iterate, ele fiind deosebit de
utile 樽n procesele de explicitare ce vor urma:
n
n(n 1)
1 k 1 2 3 ... n (I)
k 1 2
n
n(n 1)(2n 1)
2 k 2 12 22 32 ... n 2 (II)
k 1 6
n(n 1)
2
n
3 k 1 2 3 ... n
3 3 3 3 3
(III)
k 1 2
n
an 1 1
a 1 a a ... a
k 2 n
(IV)
k 0 a 1
1
3. La fel de util se va dovedi 樽n acest sens i procedura de descompunere a
frac釘iilor ra釘ionale 樽n supranumitele sume de frac釘ii simple (metoda coeficien釘ilor
n
nedetermina釘i), care va facilita calculul unor sume iterate t
k 1
k
cu termenul general,
f (k )
tk , frac釘ii av但nd f (k ) i g (k ) expresii polinomiale.
g (k )
n
Din aceast categorie de sume cel mai simplu de calculat sunt t
k 1
k
cu
1
tk . n astfel de cazuri se va observa cu uurin釘 c identificarea
(ak b)(ak a b)
1 A B
conduce la descompunerea termenului
(ak b)(ak a b) ak b ak a b
1 1 1 1
general sub forma .
(ak b)(ak a b) a ak b ak a b
De remarcat c aici descompunerea poate chiar ocoli metoda coeficien釘ilor
1 1 a
nedetermina釘i, observ但nd pur i simplu ,
ak b ak a b (ak b )(ak a b )
1 1 1 1
deci tk .
(ak b)(ak a b) a ak b ak a b
Aceast exprimare a termenului general t k , aplicat succesiv, va pune 樽n
eviden釘 cunoscuta reducere telescopic prin care de altfel se va i finaliza calculul
sumei, dup cum ilustreaz i urmtorul exemplu:
1 1 1 1
Sn ...
7 11 11 15 15 19 (4n 3) (4n 7)
1
Solu釘ie Se observ termen general tk , k 1 n , apoi
;
(4k 3)(4k 7)
1 1 4 1 1 1 1
tk din
4k 3 4k 7 (4k 3)(4k 7) (4k 3)(4k 7) 4 4k 3 4k 7
care, prin particularizare i sumare, apare reducerea telescopic ce finalizeaz
calculul,
1 1 1 1
t1
7 11 4 7 11
1 1 1 1
t2
11 15 4 11 15
..
1 1 1 1
tn ,
(4n 3) (4n 7) 4 4n 3 4n 7
2
4. ob釘in但ndu-se la final
n
1 1 1 1 1 1 1 駈 1 1 1 n
Sn t k ... 件 件
k 1 4 7 11 11 15 4n 3 4n 7 4 7 4n 7 7(4n 7)
Acestea fiind prezentate, revin la recuren釘a telescopic aditiv, cu parcurgerea
algoritmului de explicitare pe un caz concret.
x xn n(n 1), ()n *
Exemplu Explicitez irul generat de recuren釘a n 1
x1 1
Solu釘ie xn1 xn n(n 1), ()n * i astfel
x2 x1 1 2
x3 x 2 2 3
x 4 x3 3 4
xn 1 xn 2 (n 2) (n 1)
xn xn 1 (n 1) n
____________
() n 1 n 1
xn x1 k (k 1) i cum x1 1 xn 1 k (k 1) , sum care este
k 1 k 1
uor calculabil cu ajutorul formulelor sumelor remarcabile anterior prezentate,
n 1 n 1 n 1
(n 1)n(2n 1) (n 1)n
respectiv xn 1 k (k 1) 1 k 2 k 1 , etc.
k 1 k 1 k 1 6 2
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x xn (2n 1), ()n * x xn n(n 1)(2n 1), ()n *
a) n 1 b) n 1
x1 1 x1 1
1 1
xn 1 xn , ()n * xn 1 xn 2 , ()n *
c) n(n 1) d) 4n 1
x 1 x1 1
1
1 1
xn 1 xn 2 , ()n * xn 1 xn 2 , ()n *
e) n 5n 6 f) 4n 8n 3
x1 1
x1 1
3
5. 2. Progresiile aritmetice
xn 1 xn r , ()n *
x1 termen ini釘ial dat
r constant dat numit ra釘ie
Explicitare Fiind recuren釘 telescopic aditiv, prin ra釘ionamente analoge celor
descrise anterior se va ob釘ine cunoscuta formul xn x1 (n 1) r ce determin
direct termenul general al progresiei aritmetice 樽n func釘ie de primul termen i ra釘ie.
Prin intermediul acestei formule se vor deduce imediat i alte rela釘ii utile 樽n aplica釘iile
a aq
referitoare la progresii aritmetice, dintre acestea remarc但ndu-se r p i
pq
n( x1 xn )
Sn x1 x2 ... xn . n ceea ce privete explicitarea recuren釘ei, desigur
2
c 樽n astfel de situa釘ii este mai comod a se re釘ine formula i aplica direct exprimarea
termenului general al progresiei dar consider totui instructiv parcurgerea integral
a ra釘ionamentului de explicitare.
x xn 3, ()n *
Exemplu Explicitez irul generat de recuren釘a n 1
x1 2
Solu釘ie Av但nd xn1 xn 3, ()n * , din suita de egalit釘i
x2 x1 3
x3 x 2 3
x 4 x3 3
xn 1 xn 2 3
xn xn 1 3
____________
()
xn x1 3 3 3 ... 3 , deci xn 2 3(n 1) 3n 1, rezultat la care
de ( n 1) ori
se putea ajunge i pe cale direct, xn x1 (n 1) r ... 3n 1.
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x xn 2, ()n * x xn 7, ()n *
a) n 1 b) n 1
x1 2 x1 3
x xn 3, ()n * x xn 8, ()n *
c) n 1 d) n 1
x1 5 x1 9
4
6. 3. Recuren釘a telescopic multiplicativ
xn 1 xn an , ()n *
x1 termen ini釘ial dat
(a ) ir explicit dat
n n *
Explicitare Procedura este asemntoare cu cea de la recuren釘a telescopic
aditiv, de aceast dat 樽ns eliminrile ce conduc la aflarea expresiei termenului
general al irului apar la efectuarea produsului iterat corespunztor exprimrilor
x
particulare, respectiv din xn1 xn an , ()n * n 1 an , ()n * i astfel din
xn
() n 1
x2 x x x
a1, 3 a2 , 4 a3 , ... , n an 1 xn x1 ak , produs care 樽n aplica釘iile
x1 x2 x3 xn 1 k 1
propuse se va restr但nge, uneori prin simplificri telescopice, alteori prin exprimri
combinatorice adecvate.
n2
xn 1 xn , ()n *
Exemplu Explicitez irul generat de recuren釘a (n 1)(n 2)
x 1
1
xn 1 n2
Solu釘ie Cum , ()n * , prin particularizare se ob釘ine
xn (n 1)(n 2)
x2 12 x 22 x4 32 x (n 1)2
, 3 , ,..., n i observ但nd simplificarea
x1 2 3 x2 3 4 x3 4 5 xn 1 n (n 1)
x x x x 12 22 32 (n 1)2
telescopic 2 3 4 ... n ... , cu ajutorul exprimrii
x1 x2 x3 xn 1 2 3 3 4 4 5 n (n 1)
2 (n 1)!
2
x 2
factoriale, n , rezult 樽n final xn 2 .
x1 n ! (n 1)! n (n 1)
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
n n(n 1)
xn 1 xn , ()n * xn 1 xn , ()n *
a) n 1 b) (n 2)2
x1 1
x 1
1
n 2 3n 2 1
x xn 2 , ()n * xn 1 xn 1 , ()n *
c) n 1 n 4n 3 d) 2n
x 1 x 1
1 1
5
7. 4. Progresiile geometrice
xn 1 xn q, ()n *
x1 termen ini釘ial dat
q constant dat numit ra釘ie
Explicitare Acestea fiind generate tot de recuren釘a telescopic multiplicativ, prin
xn 1 x x x x
ra釘ionament analog q, ()n * 2 3 4 ... n q q q ... q , din
xn x1 x2 x3 xn 1 de ( n 1) ori
care se deduce imediat cunoscuta formul xn x1 q n1 .
x 2xn , ()n *
Exemplu Explicitez irul generat de recuren釘a n 1
x1 3
x x x x x
n acest caz n 1 2, ()n * , 2 3 4 ... n 2 2 2 ... 2 xn 3 2n 1 .
xn x1 x2 x3 xn 1 de ( n 1) ori
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x 3 xn , ()n * x 10 xn , ()n *
a) n 1 b) n 1
x1 2 x1 7
1
xn 1 xn , ()n * x 2xn , ()n *
c) 2 d) n 1
x1 3 x1 5
5. Recuren釘a liniar neomogen de ordin I, cu coeficien釘i variabili
xn 1 an xn bn , ()n *
x1 termen ini釘ial dat
(a ) ,(b )
n n * n n * iruri explicit date
Explicitare Explicitarea acestei recuren釘e se va baza pe transformarea ei 樽ntr-o
y
recuren釘 telescopic aditiv. ntr-adevr, introduc但nd substitu釘ia an n , y1 1,
y n 1
y
rela釘ia de recuren釘 devine xn 1 n xn bn , deci xn1 y n1 xn y n bn y n 1 .
y n 1
Astfel, xn1 y n1 xn y n bn y n1, ()n * i particulariz但nd
x2 y 2 x1 y1 b1 y 2
x3 y 3 x2 y 2 b2 y 3
xn y n xn 1 y n 1 bn 1 y n
() n 1
xn y n x1 y1 bk y k 1 ,
k 1
6
8. pentru finalizarea explicitrii mai fiind necesar doar determinarea irului ( y n )n *
y y y y
introdus de substitu釘ia efectuat. Cum 樽ns 1 a1, 2 a2 , 3 a3 , ... , n an , i
y2 y3 y4 y n 1
b
, xn ak x1 k k , ()n 2 . Evident
n 1 n 1
1
y1 1, se ob釘ine imediat y n 1 n
k 1
ak
k 1
k 1
ai
i 1
c 樽n aplica釘ii este de preferat parcurgerea integral a ra釘ionamentului expus.
x n xn n !, ()n *
Exemplu Explicitez irul dat de recuren釘a n 1
x1 1
y y
Solu釘ie Not但nd n n , y1 1 xn 1 n xn n ! xn1 y n1 xn y n n ! y n 1 i
y n 1 y n 1
y
astfel xn1 y n1 xn y n n ! y n1, ()n * . Dar din nota釘ia aplicat, n n , cum
y n 1
y1 y y 1
1 2 2, ..., n n y n 1 , deci
, xn1 y n1 xn y n 1, care conduce
y2 y3 y n 1 n!
imediat la xn y n x1 y1 (n 1) i 樽n final xn n !
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x n xn (n 1)!, ()n * x n xn (n 2)!, ( )n *
a) n 1 b) n 1
x1 1 x1 1
1 1
xn 1 n 2 xn (n !)2, ()n * x xn , ()n *
c) d) n 1 n n!
x1 1 x1 1
6. Recuren釘a liniar neomogen de ordin I, cu coeficien釘i constan釘i
xn 1 a xn b, ()n *
x1 termen ini釘ial dat
a, b constante date
Explicitare Fiind la fel cu recuren釘a anterioar, i se poate aplica pentru explicitare
acelai ra釘ionament, ob釘in但nd la final pentru xn o expresie exponen釘ial care admite
restr但ngere 樽n forma xn A an B . Aceast observa釘ie permite scurtarea cii de
explicitare a acestor recuren釘e, coeficien釘ii A i B put但nd fi rapid determina釘i din
sistemul primilor doi termeni ai irului.
x 2xn 3, ()n *
Exemplu Explicitez irul dat de recuren釘a n 1
x1 1
7
9. Solu釘ie Av但nd xn A 2n B , cum x1 1 i x2 2x1 3 5 , din sistemul
2 A B 1
se deduce imediat A 2 i B 3 , deci xn 2n1 3
4 A B 5
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x 3 xn 5, ()n * x 5 xn 2, ()n *
a) n 1 b) n 1
x1 2 x1 3
x 2xn 3, ()n * x 5 xn 3, ()n *
c) n 1 d) n 1
x1 7 x1 2
7. Recuren釘a liniar omogen de ordin II, cu coeficien釘i variabili
xn 2 an xn 1 bn xn , ()n *
x1 , x2 termeni ini釘ial da釘i
(a ) ,(b )
n n * n n * iruri explicit date
Explicitare Voi prezenta doar un rezultat par釘ial legat de explicitarea acestui tip
de recuren釘. Acesta este con釘inut de afirma釘ia: dac ecua釘ia t 2 an t bn 0
admite o rdcin care nu depinde de n * atunci recuren釘a devine
explicitabil. ntr-adevr, dac supranumita ecua釘ie caracteristic a recuren釘ei are
rdcinile t1 i t2 n atunci n an i n bn . n acest caz vom
ob釘ine xn2 ( n ) xn 1 ¥n xn xn2 xn1 n ( xn 1 xn ) , recuren釘
telescopic multiplicativ care va permite determinarea xn 1 xn y n , ob釘in但nd
n 1
y1 x2 x1 , y n ( x2 x1 ) k , ()n 2 . Dar recuren釘a xn 1 xn y n a fost i
k 1
ea tratat anterior i particularizat pe aceast situa釘ie conduce 樽n final la forma
i
k 1
x n 1
explicit xn n 1 2 ( x2 x1 ) i 1 k , ()n 3 .
k 2
nx 2(2n 1)xn 1 4(n 1)xn , n *
Exemplu: Explicitez irul dat de recuren釘a n 2
x1 1 x2 3
,
2(n 1)
Ecua釘ia caracteristic nt 2 2(2n 1)t 4(n 1) 0 are rdcinile t1 2 , t 2 ,
n
deci suntem 樽n condi釘ii favorabile explicitrii. Folosind cunoscutele rela釘ii dintre
rdcinile i coeficien釘ii ecua釘iei de gradul doi, rela釘ia de recuren釘 se va scrie 樽n
2(n 1) 4(n 1) x 2xn 1 2(n 1)
forma xn 2 2 xn 1 xn din care n 2 .
n n xn 1 2xn n
8
10. De aici se va repeta ra釘ionamentul 樽nt但lnit la recuren釘a telescopic multiplicativ,
xn 1 2xn n 2n 1
ob釘in但nd xn1 2xn n 2 . Dar recuren釘a
n 1
este de tip cunoscut,
x1 1
de aceast dat procedura de explicitare finaliz但nd cu xn (n 2 n 4) 2n3, ()n 3
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
nx 3(2n 1)xn 1 9(n 1)xn , n *
a) n 2
x1 1 x2 5
,
n 2 xn 2 2(2n 2 2n 1)xn 1 4(n 1)2 xn , n *
b)
x1 1 x2 3
,
n 3 xn 2 2(2n 3 3n 2 3n 1)xn 1 4(n 1)3 xn , n *
c)
x1 2, x2 5
n 2 xn 2 2(2n 2 3n 2)xn 1 4(n 2 3n 2)xn , n *
d)
x1 1 x2 5
,
8. Recuren釘a liniar omogen de ordin II, cu coeficien釘i constan釘i
xn 2 a xn 1 b xn , ()n *
x1 , x2 termeni ini釘ial da釘i
a, b constante date
Explicitare La fel ca i cea de ordinul I cu coeficien釘i constan釘i, i aceast
recuren釘 va permite explicitare imediat, considerente de la recuren釘a anterioar
pun但nd 樽n eviden釘 urmtoarele dou situa釘ii posibile:
I) Ecua釘ia caracteristic t 2 a t b 0 are rdcini egale t1 t2 .
n acest caz termenul general este de forma xn (nA B) n
II) Ecua釘ia caracteristic t 2 a t b 0 are rdcini distincte t1 , t2 .
n acest caz termenul general este de forma xn A n B n
n ambele situa釘ii coeficien釘ii A i B se determin din sistemul celor doi termeni
ini釘ial da釘i.
x 6 xn 1 9 xn , n *
Exemplu (cazul t1 t2 ) Explicitez irul dat de recuren釘a n 2
x1 2, x2 7
Ecua釘ia caracteristic conduce la t1 t2 3 , deci xn (nA B) 3n i din sistemul
( A B ) 3 2 1 5
termenilor ini釘iali, , ob釘in A , B , xn (n 5) 3n2
(2 A B ) 3 7
2
9 9
9
11. x 5 xn 1 6 xn , n *
Exemplu (cazul t1 t2 ) Explicitez irul dat de recuren釘a n 2
x1 4, x2 5
De aceast dat ecua釘ia caracteristic are rdcinile t1 2, t2 3 , deci
2 A 3B 4 7
xn A 2n B 3n cu , rezult但nd A , B 1, xn 7 2n 1 3n .
4 A 9B 5 2
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
I) Cazul t1 t2
x 4 xn 1 4 xn , n * x 10 xn 1 25 xn , n *
a) n 2 b) n 2
x1 1 x2 2
, x1 1 x2 3
,
4 x 4 xn 1 xn , n * 9 x 6 xn 1 xn , n *
c) n 2 d) n 2
x1 1 x2 3
, x1 1 x2 2
,
II) Cazul t1 t2
x 7 xn 1 12xn , n * x 7 xn 1 10 xn , n *
a) n 2 b) n 2
x1 1 x2 3
, x1 2, x2 3
2x 5 xn 1 2xn , n * 2x 7 xn 1 3 xn , n *
c) n 2 d) n 2
x1 1 x2 2
, x1 2, x2 5
9. Recuren釘a liniar neomogen de ordin II, cu coeficien釘i constan釘i
xn 2 a xn 1 b xn c, ()n *
x1 , x2 termeni ini釘ial da釘i
a, b, c constante date
Explicitare Aceast recuren釘 se reduce imediat la tipul anterior, observ但nd
xn 2 a xn 1 b xn c
( xn3 xn2 ) a ( xn2 xn1 ) b ( xn1 xn ) , care este
xn 3 a xn 2 b xn 1 c
de forma y n2 a y n 1 b y n cu y n xn 1 xn . Se ob釘ine astfel xn1 xn y n ,
recuren釘 telescopic aditiv ce va permite finalizarea explicitrii. Analiz但nd forma
explicit final vom constata c i aceast recuren釘 are termenul general de un tip
bine determinat, tot 樽n func釘ie de rdcinile ecua釘iei caracteristice, aceasta fiind i de
aceast dat tot t 2 a t b 0 . Astfel, vom deosebi situa釘iile:
I) Ecua釘ia caracteristic t 2 a t b 0 are rdcini egale t1 t2 .
n acest caz termenul general este de forma xn (nA B) n C
10
12. II) Ecua釘ia caracteristic t 2 a t b 0 are rdcini distincte t1 , t2 .
n acest caz termenul general este de forma xn A n B n C
Coeficien釘ii A , B i C sunt imediat determinabili din sistemul celor doi termeni ini釘ial
da釘i i al celui de al treilea, ob釘inut din recuren釘.
x 4 xn 1 4 xn 3, n *
Exemplu(cazul t1 t2 ) Explicitez irul dat de recuren釘a n 2
x1 1 x2 2
,
n acest caz ecua釘ia caracteristic t 2 4t 4 0 conduce la t1 t2 2 , deci
xn (nA B) 2n C i din sistemul termenilor ini釘iali, inclusiv x3 4x2 4x1 3 7 ,
3 7
se ob釘in A , B , C 3 , xn (3n 7) 2n2 3 .
4 4
x 5 xn 1 6 xn 3, n *
Exemplu (cazul t1 t2 ) Explicitez irul dat de recuren釘a n 2
x1 1 x2 2
,
De aceast dat ecua釘ia caracteristic are rdcinile t1 2, t2 3 , deci
xn A 2n B 3n C . Av但nd x3 5x2 6x1 3 7 , din sistemul celor trei termeni
1 3 3n 2n 1 3
cunoscu釘i ob釘in A 1 B , C , xn
, .
2 2 2
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
I) Cazul t1 t2
x 4 xn 1 4 xn 5, n * x 10 xn 1 25 xn 3, n *
a) n 2 b) n 2
x1 1 x2 2
, x1 1 x2 3
,
4 x 4 xn 1 xn 7, n * 9 x 6 xn 1 xn 1 n
, *
c) n 2 d) n 2
x1 1 x2 3
, x1 1 x2 2
,
II) Cazul t1 t2
x 7 xn 1 12xn 2, n * x 7 xn 1 10 xn 1 n
, *
a) n 2 b) n 2
x1 1 x2 3
, x1 2, x2 3
2x 5 xn 1 2xn , n * 2x 7 xn 1 3 xn 4, n *
c) n 2 d) n 2
x1 1 x2 2
, x1 2, x2 5
10. Recuren釘e liniare omogene de ordin superior, cu coeficien釘i constan釘i
xn p a1 xn p 1 a2 xn p 2 ... ap 1 xn 1 ap xn , ()n *, p 3
x1 , x2 , ..., x p termeni ini釘ial da釘i
a1 , a2 , ..., ap constante date
11
13. Explicitare Determinarea explicit a unor astfel de iruri se va face la fel ca i la
suratele lor mai mici prezentate anterior, paii de parcurs fiind urmtorii:
- se rezolv ecua釘ia caracteristic t p a1 t p1 a2 t p2 ... ap1 t ap ;
- se clasific rdcinile distincte dup ordinul de multiplicitate;
- dac t1 este rdcin simpl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma A t1n ;
- dac t2 este rdcin dubl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma (nB C ) t2 ; n
- dac t3 este rdcin tripl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma (n 2D nE F ) t3 , etc.
n
Coeficien釘ii A, B, C, etc. se ob釘in din sistemul termenilor ini釘iali ai recuren釘ei.
x 7 xn 2 16 xn 1 12xn , n *
Exemplu Explicitez irul dat de recuren釘a n 3
x1 1 x2 2, x3 5
,
n acest caz ecua釘ia caracteristic este t 3 7t 2 16t 12 0 , cu t1 3 rdcin
simpl i t2 t3 2 rdcin dubl, deci termenul general al irului va avea forma
xn A 3n (nB C ) 2n . Din sistemul termenilor ini釘iali se determin coeficien釘ii,
1 1 1
A ,B ,C i se ob釘ine xn 3n1 (1 n) 2n 2 .
3 4 4
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x 8 xn 2 21xn 1 18 xn , n * x xn 2 16 xn 1 20 xn , n *
a) n 3 b) n 3
x1 1 x2 5, x3 6
, x1 4, x2 5, x3 3
11. Recuren釘e liniare neomogene de ordin superior, cu coeficien釘i constan釘i
xn p a1 xn p 1 a2 xn p 2 ... ap 1 xn 1 ap xn b, ()n *, p 3
x1 , x2 , ..., x p termeni ini釘ial da釘i
a1 , a2 , ..., ap , b constante date
Explicitare La fel ca la recuren釘a neomogen de ordin doi cu coeficien釘i constan釘i:
- se rezolv ecua釘ia caracteristic t p a1 t p1 a2 t p2 ... ap1 t ap ;
- se clasific rdcinile distincte dup ordinul de multiplicitate;
- dac t1 este rdcin simpl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma A t1n ;
- dac t2 este rdcin dubl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma (nB C ) t2 ;
n
- dac t3 este rdcin tripl, ea se va prezenta 樽n exprimarea termenului
general xn aditiv sub forma (n 2D nE F ) t3 , etc.
n
- se introduce coeficientul termen liber G .
Coeficien釘ii A, B, C,...,G se ob釘in din sistemul termenilor ini釘iali ai recuren釘ei.
12
14. x 7 xn 2 16 xn 1 12xn 1 n *
,
Exemplu Explicitez irul dat de recuren釘a n 3
x1 x2 x3 1
Ecua釘ia caracteristic este t 3 7t 2 16t 12 0 , cu t1 3 rdcin simpl i
t2 t3 2 rdcin dubl, deci termenul general al irului va avea forma
xn A 3n (nB C ) 2n D . Din sistemul termenilor ini釘iali i x4 ... 2 se ob釘ine
1 1 1 1 3n 1 (1 n ) 2n 1 1
A , B , C , D , xn .
6 4 4 2 2
Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
x 8 xn 2 21xn 1 18 xn 10, n * x xn 2 16 xn 1 20 xn 15, n *
a) n 3 b) n 3
x1 1 x2 1 x3 2
, , x1 1 x2 0, x3 0
,
12. Recuren釘a omografic cu coeficien釘i variabili
a x bn
xn 1 n n , ()n *
cn xn d n
x1 termen ini釘ial dat
(a ) ,(b ) ,(c ) ,(d ) iruri explicit date
n n * n n * n n * n n *
Explicitare La aceste recuren釘e vom analiza urmtoarele dou cazuri:
I) Cazul bn 0
Dup cum uor se va observa, aceast particularitate permite totdeauna
an xn 1 d 1 c
finalizarea explicitrii. ntr-adevr xn 1 n n not但nd
cn xn d n xn 1 an xn an
1
y n recuren釘a ia forma y n1 An y n Bn care este explicitabil.
xn
II) Cazul bn 0
Un rezultat par釘ial 樽n astfel de situa釘ii este urmtorul:
- introduc substitu釘ia cn xn dn y n recuren釘a ia forma y n1 y n An y n Bn
z
- introduc substitu釘ia y n n 1 , z1 1 recuren釘a ia forma zn2 An zn1 Bn zn
zn
cu z1 1 i z2 ... c1 x1 d1 dac ecua釘ia caracteristic t 2 An t Bn 0
are o rdcin nedependent de n * atunci zn devine determinabil prin
y dn zn 1 dn zn
procedur descris anterior i astfel xn n .
cn cn zn
13
15. xn
xn 1
Exemplu ( bn 0 ) Explicitez irul dat de recuren釘a n ! xn n
x 1
1
1
Cu y n recuren釘a devine y n1 ny n n ! , y1 1 (explicitat anterior) y n n !
xn
1
i astfel xn .
n!
an xn bn
xn 1
Exemplu ( bn 0 ) Explicitez irul dat de recuren釘a cn xn d n cu coeficien釘ii
x 1
1
an n !(n 2 3n 2) , bn n3 3n2 2n 4 , cn (n !)2 n (n 1) , dn n ! n 2 (n 1)
Dei aceast exprimare apare de-a dreptul descurajant, parcurg但nd drumul indicat
se va ajunge la aceeai recuren釘 zn 1 nzn n ! , z1 1 din care se va ob釘ine zn n ! ,
1
y n n 1 i 樽n final xn
n!
Tem de aprofundare La aceast sec釘iune, ca exerci釘iu de virtuozitate, propun
cititorului s-i construiasc singur o aplica釘ie care s permit explicitare i
bine樽n釘eles, s o i rezolve !
13. Recuren釘a omografic cu coeficien釘i constan釘i
a xn b
xn 1 c x d , ()n *
n
x1 termen ini釘ial dat
a, b, c, d constante date
Explicitare Fiind particularizare a celei anterioare, se vor parcurge ra釘ionamente
analoge, conform cu fiecare din situa釘iile:
I) Cazul b 0
a xn 1 d 1 c 1
Av但nd xn 1 not但nd y n recuren釘a ia forma
c xn d xn 1 a xn a xn
y n 1 A y n B care este explicitabil. n aceast situa釘ie termenul general se va
an
ob釘ine de forma xn , cu coeficien釘ii i determinabili din
d n an
sistemul primilor doi termeni, observa釘ie care poate scurta sensibil explicitarea.
14
16. II) Cazul b 0
n aceast situa釘ie:
- introduc substitu釘ia cxn d y n recuren釘a ia forma y n1 y n A y n B
z
- introduc substitu釘ia y n n 1 , z1 1 recuren釘a ia forma zn2 A zn1 B zn
zn
z
cu z1 1 i z2 ... c x1 d determin zn determin y n n 1 i finalizez,
zn
y d zn 1 d zn
ob釘in但nd xn n . De aceast dat forma termenului general
c c zn
va fi decis de ordinul de multiplicitate a rdcinilor ecua釘iei t 2 A t B 0 ,
n t1n t2n
respectiv xn c但nd t1 t2 i xn n c但nd t1 t2 , coeficien釘ii
n t1 t2n
, , fiind determinabili din sistemul primilor trei termeni.
xn
xn 1 ,()n *
Exemplu ( b 0 ) Explicitez irul dat de recuren釘a 3 xn 5
x 1
1
1 1 1
Ob釘in 5 3 xn , etc.
xn 1 xn A 3n B
5 xn 1
xn 1
Exemplu ( b 0, t1 t2 ) Explicitez irul dat de recuren釘a 4 xn 1
x 1
1
z
Aplicarea substitu釘iilor cxn d y n , y n n 1 , z1 1, va conduce la recuren釘
zn
omogen de ordin doi. Se ob釘in rdcini ale ecua釘iei caracteristice t1 t2 3 , etc.,
n2
cu finalizarea xn .
2n 1
7 xn 4
xn 1
Exemplu ( b 0, t1 t2 ) Explicitez irul dat de recuren釘a 5 xn 2
x 2
1
z
Aplicarea substitu釘iilor cxn d y n , y n n 1 , z1 1, va pune 樽n eviden釘 recuren釘a
zn
omogen de ordin doi. Se vor ob釘ine rdcini ale ecua釘iei caracteristice
3n 2n
t1 2, t2 3 , etc., cu finalizarea xn n .
3 5 2n 2
15
17. Tem de aprofundare Proced但nd analog, explicita釘i urmtoarele recuren釘e:
I) Cazul b 0
xn 2 xn
xn 1 ,()n * xn 1 ,()n *
a) 5 xn 2 b) 3 xn 7
x 1 x 2
1 1
II) Cazul b 0, t1 t2
5 xn 3 3 xn 1
xn 1 ,()n * xn 1 ,()n *
a) 3 xn 1 b) xn 1
x 3 x 2
1 1
III) Cazul b 0, t1 t2
4 xn 1 5 xn 2
xn 1 ,()n * xn 1 ,()n *
a) 2 xn 1 b) xn 2
x 2 x 3
1 1
16