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16.1:

Trigonometric Fourier series

JoVE Core
Electrical Engineering
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JoVE Core Electrical Engineering
Trigonometric Fourier series

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A Fourier series is a mathematical technique that breaks down periodic functions into an infinite series of sinusoidal harmonics. The trigonometric Fourier series represents a periodic function with a specific period using sine and cosine functions. The frequency of these functions is inversely proportional to the period of the original function. The constant terms and the individual contribution of each sine and cosine function to the original function are understood using Fourier coefficients. The calculation of these coefficients involves integration over one period. For a Fourier series to depict a periodic function, the Dirichlet conditions must be satisfied. The first condition demands that the function have a finite integral over one period. The second condition states that the function's variation should be limited within any given range. Lastly, the function must have a finite number of discontinuities within any specific range, none of which can be infinite. Fourier series can be used to approximate functions – it cannot fully recover functions that do not meet Dirichlet's conditions.

16.1:

Trigonometric Fourier series

Fourier series is a foundational mathematical technique that decomposes periodic functions into an infinite series of sinusoidal harmonics. This method enables the representation of complex periodic signals as sums of simple sine and cosine functions, facilitating their analysis and interpretation in various fields, including signal processing, acoustics, and electrical engineering.

The trigonometric Fourier series specifically expresses a periodic function with a defined period T using sine and cosine functions. The general form of the trigonometric Fourier series for a function x(t) is:

Equation1

Here, a0 represents the average value of the function over one period, while

an and bare the Fourier coefficients that quantify the contribution of each cosine and sine function, respectively. These coefficients are determined through integration over one period T:

Equation2

Equation3

Equation4

These integrals are essential for calculating the exact coefficients that reconstruct the original function from its sinusoidal components.

To accurately depict a periodic function using a Fourier series, the Dirichlet conditions must be met. The first condition stipulates that the function should have a finite integral over one period, ensuring the overall function is bounded. The second condition requires the function to have a limited number of maxima and minima within any given range, ensuring the function does not exhibit excessive oscillations. The third condition mandates that the function should possess a finite number of discontinuities, none of which are infinite. These conditions ensure the Fourier series converges appropriately to the original function.

In practical applications, even if these conditions are not strictly satisfied, Fourier series representations can often still be constructed. Such representations, while potentially less accurate, can provide useful approximations for analyzing and synthesizing periodic functions. This flexibility underscores the robustness and utility of the Fourier series in various mathematical and engineering applications.