Name: continuous and discrete system models
Uploaded: 2024-09-26T13:16:54.000Z
Duration: 1 min 31 s
Channel: JoVE

Sürekli zamanlı sistemler, sürekli olarak ölçülen zaman ile sürekli giriş ve çıkış sinyallerine sahiptir ve genellikle diferansiyel veya cebirsel denklemlerle tanımlanır.
Örneğin, bir RC devresinde, giriş ve çıkış voltajı arasındaki ilişki, Ohm yasası ve kapasitörün voltaj-akım ilişkisi kullanılarak türetilen bir diferansiyel denklem aracılığıyla ifade edilir.
Ayrık zamanlı sistemler, giriş ve çıkış sinyallerini belirli aralıklarla işler ve davranışları, farklı zaman örneklerinde fark denklemleri ile tanımlanır. Böyle bir sisteme bir örnek, bir banka hesabındaki bakiye için aydan aya basit bir modeldir.
Başka bir sistem türü zamanla değişen ve zamanla değişmezdir.
Zamanla değişen bir sistem, zamanla değişen parametrelere sahiptir. Sürekli zamanlı, zamanla değişen sistemler, zamana bağlı katsayılara sahip diferansiyel denklemlerle tanımlanırken, ayrık zamanlı, zamanla değişen sistemler, zamana bağlı parametrelere sahip fark denklemleri ile tanımlanır.
Zamanla değişmeyen sistemler, giriş sinyali zaman kaydırmalı olduğunda, sistemin özelliklerini değiştirmeden aynı şekilde kayan bir çıkış üretir.
Bir RC devresi, direnç ve kapasitans değerleri sabit kalırsa zamanla değişmezdir. Bunlar dalgalanırsa, deney sonuçları deneyin ne zaman yapıldığına bağlı olarak değişeceğinden sistem zaman değişkeni haline gelir.

sistemlerin sınıflandırılması-ii

Sürekli zamanlı sistemler, sürekli giriş ve çıkış sinyallerine sahiptir ve bu sinyallerin zamanları sürekli olarak ölçülür. Bu sistemler genellikle diferansiyel veya cebirsel denklemlerle tanımlanır. Örneğin, bir RC devresinde giriş ve çıkış gerilimi arasındaki ilişki, Ohm yasasından ve kapasitör ilişkisinden türetilen bir diferansiyel denklemle ifade edilir:
<img alt="Equation1" src="/files/ftp_upload/16046/16046_PT%20EQUATION_1.svg" style="height: 30px;">
Ayrık zamanlı sistemler, diferansiyel denklemleriyle belirli anlarda tanımlanan belirli aralıklarla giriş ve çıkış sinyallerine sahiptir. Örneğin aydan aya bir banka hesabındaki bakiye basit bir modeldir ve şu şekilde gösterilir, 
<img alt="Equation2" src="/files/ftp_upload/16046/16046_PT%20EQUATION_2.svg" style="height: 14px;">
burada B[n], n ayındaki bakiyedir, D[n], mevduattır ve W[n], çekimdir.
Sistemler ayrıca zamanla değişen veya zamanla değişmeyen olarak da kategorize edilebilir. Zamanla değişen bir sistemin zamanla değişen parametreleri vardır. Hem sürekli zamanlı ve zamanla değişen sistemler hem de ayrık zamanlı ve zamanla değişen sistemler zamanla değişen diferansiyel denklemlerle tanımlanır.
Zamanla değişmeyen bir sistemde giriş sinyali zamana kaymasına uğradığında çıkış sinyalleri de aynı zaman kaymalarını gösterir. Matematiksel olarak, x(t) girdisinin çıktısı y(t) ise zamanla değişmeyen bir sistem için girdi x(t−t_0) için çıktı y(t−t_0) olur. Örneğin, bir RC devresinde direnç R ve kapasitans C değerleri sabitse devre zamanla değişmezdir. Bu değerler dalgalanırsa, sistem zamanla değişken hale gelir, yani sonuçlar deneyin ne zaman gerçekleştirildiğine bağlı olarak değişecektir. 
Sürekli zamanlı ve ayrık zamanlı sistemlerdeki, ayrıca zamanla değişen ve zamanla değişmeyen sistemlerdeki ayrımları anlamak, çeşitli mühendislik sistemlerini analiz etmek ve tasarlamak için çok önemlidir.

Sistemlerin Sınıflandırılması-II

Sürekli zaman sistemleri, sürekli olarak ölçülen zaman ile sürekli giriş ve çıkış sinyallerine sahiptir ve genellikle diferansiyel veya cebirsel olarak tanımlanır.

Education

JoVE Core

Electrical Engineering

Elektrik Mühendisliği

Unit 1

Ünite 1

Introduction to Signals and Systems

Sinyaller ve Sistemlere Giriş

KEY TERMS AND CONCEPTS

TEMEL TERİMLER VE KAVRAMLAR

Classification of Systems-II

continuous and discrete system models

Continuous-time and discrete-time systems are two common ways to model signals and their outputs. In continuous-time systems, both input and output signals change smoothly as time moves continuously. These systems are often written with differential or algebraic equations.

An RC circuit is one example of a continuous-time system. Its input and output voltage can be related by a differential equation based on Ohm’s law and the capacitor relation. Discrete-time systems work differently. Their inputs and outputs are defined at specific time steps, and they are described by difference equations.

A bank account balance from month to month is a simple discrete-time model. In that example, B[n] is the balance at month n, D[n] is the deposit, and W[n] is the withdrawal. Systems are also grouped as time-varying or time-invariant. A time-varying system has parameters that change with time, while a time-invariant system keeps the same shift in output when the input is shifted.

For a time-invariant system, if y(t) is the output for input x(t), then y(t − t0) is the output for x(t − t0). An RC circuit is time-invariant when the resistance R and capacitance C stay constant. If those values change, the system becomes time-variant. Then the result depends on when the experiment is done. Knowing these categories helps in analyzing and designing many engineering systems.

Continuous and Discrete System Models

signal and system

Signals and Systems in Everyday Data

Signals and systems help describe how data moves through the world. A signal x(t) is a set of data or a time function that represents a variable of interest. It can carry information about atmospheric temperature, humidity, human voice, television images, a dog’s bark, or birdsongs. A signal can also depend on more than one independent variable. An image, for example, depends on horizontal and vertical position, so it can be treated as a two-dimensional signal. In this course, the focus is on...

classification of signals

Signal Types by Time and Amplitude

Signals in signal processing are grouped by time behavior, amplitude, and whether they start before or after time zero. These signal classes help students describe and compare signals before they are analyzed or processed. A continuous-time signal has a value at every instant in time. A discrete-time signal has values only at specific moments, often written as x(n), where n is an integer. Discrete-time signals often come from systems with naturally discrete values, such as digital audio...

energy and power signals

Power and Energy in Electrical Signals

Power and energy in electrical signals are measured from voltage and current across a resistor. These signals help describe how much energy a signal carries and how much power it delivers over time. For a continuous-time signal, total energy is found by integrating the square of the signal's magnitude over a chosen time interval. Time-averaged power is then the total energy divided by the length of that interval. For a discrete-time signal, total energy is found by summing the squares of the...

even and odd signals

Signal Symmetry and Decomposition

Signal symmetry helps classify continuous-time and discrete-time signals as even or odd. An even signal matches its time-reversed version. It mirrors around the vertical axis, so the values at negative time match the values at positive time. An odd signal does not match its time-reversed counterpart. It has antisymmetry about the vertical axis. These symmetry rules are used in both continuous-time and discrete-time signal analysis. Any continuous-time signal can be split into even and odd...

basic continuous time signals

Step, Impulse, and Ramp Signals

Step, impulse, and ramp signals are the main basic continuous-time signals. They are also called singularity functions. These signals are known for discontinuities or for derivatives that are discontinuous. The unit step function, written as u(t), is zero for negative time values and one for positive time values. It has a jump at t = 0. In many cases, it can model an abrupt change, such as the step voltage that appears when a car ignition key is turned. The derivative of the unit step...

rectangular and triangular pulse function

rectangular-and-triangular-pulse-function

Modeling Pulses with Rectangles and Triangles

Rectangular and triangular pulse functions are basic signal shapes used to model waveforms in signal processing. The rectangular pulse has a flat top and is centered at the origin with a height of one unit. It is described by two parameters: T, which gives the center location on the time axis, and τ, which sets the pulse duration. A rectangular pulse can also represent a real signal with a chosen amplitude and length. For example, a pulse with a 5 V amplitude, a 3-second duration, and a center...

exponential and sinusoidal signals

Complex Exponentials in Signal Behavior

Exponential and sinusoidal signals are used to describe how waveforms change over time. The continuous-time exponential function uses constants α and A. When both are real, it shows exponential growth or decay. Graphs of these signals make the rise or drop easy to see. When α is purely imaginary, the signal becomes a complex exponential. In this form, j is the imaginary unit and ω0 is the angular frequency. A complex exponential is periodic when its magnitude is unity. That periodic behavior...

basic discrete time signals

Discrete-Time Signal Basics and Shapes

Discrete-time signals include several basic sequences used in signal processing. The unit step sequence is 1 for zero and positive values of the integer n. A graph of eight sample points shows it as a step that begins at n = 0 and stays constant after that. The unit impulse, also called the sample sequence, is 0 for every n except at n = 0, where it equals 1. This sequence is written as δ(n). It is the first difference of the unit step sequence, and the unit step sequence u(n) is the...

basic operations on signals

Signal Transformations in Time and Amplitude

Signal transformations in time and amplitude are key tools in signal processing. They include time reversal, time scaling, time shifting, and amplitude changes. These operations help students describe how a continuous-time signal changes when it is mirrored, stretched, moved, or resized. Time reversal flips a continuous-time signal about the vertical axis at t = 0. It is done by replacing t with -t, so x(t) becomes x(-t). A graph of the signal shows this mirrored shape clearly. Time scaling...

classification of systems-i

System Properties: Linearity, Causality, Memory

System properties such as linearity, causality, and memory help describe how a system responds to input. These ideas are useful for studying and designing systems in engineering. A system can be checked by looking at how its output changes with different inputs. Linearity means the input-output relationship follows homogeneity and additivity. Homogeneity says that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Additivity means the response...

classification of systems-ii

Continuous-time and discrete-time systems are two common ways to model signals and their outputs. In continuous-time systems, both input and output signals change smoothly as time moves continuously. These systems are often written with differential or algebraic equations. An RC circuit is one example of a continuous-time system. Its input and output voltage can be related by a differential equation based on Ohm’s law and the capacitor relation. Discrete-time systems work differently. Their...

key terms and concepts

Learn how continuous-time and discrete-time systems differ, plus how time-varying and time-invariant behavior is identified using RC circuits and bank account examples.

Continuous and Discrete System Models | JoVE K12

JoVE Core Elektrik Mühendisliği, devre analizi teknikleriyle birlikte elektrik devrelerinin temel kavramlarını kapsayan, anlaşılması kolay animasyonlu videolar sunar. Operasyonel amplifikatörler ve devre dönüşümü kavramlarını kullanarak birinci dereceden ve ikinci dereceden devreleri içerir. Pratik tasarım örneklerinin ve gerçek dünya uygulamalarının bilim insanlarınca hazırlanan videolar aracılığıyla dahil edilmesi, karmaşık kavramları erişilebilir hale getirerek önemli bir eğitim değeri katmaktadır.

electrical engineering

Continuous and Discrete System Models

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