A Fourier series is an expansion of a periodic function in terms of an infinite sum of sines and cosines.
Fourier series make use of the orthogonality
relationships of the sine and cosine functions. The computation and study of Fourier series
is known as harmonic analysis
and is extremely useful as a way to break up an arbitrary periodic function
into a set of simple terms that can be plugged in, solved individually, and then
recombined to obtain the solution to the original problem or an approximation to
it to whatever accuracy is desired or practical. Examples of successive approximations
to common functions using Fourier series are illustrated above.
In particular, since the superposition principle holds for solutions of a linear homogeneous ordinary differential equation, if such an equation can be
solved in the case of a single sinusoid, the solution for an arbitrary function is
immediately available by expressing the original function as a Fourier series and
then plugging in the solution for each sinusoidal component. In some special cases
where the Fourier series can be summed in closed form, this technique can even yield
analytic solutions.
Any set of functions that form a complete orthogonal system have a corresponding generalized Fourier series analogous to the Fourier series.
For example, using orthogonality of the roots of a Bessel function of the first kind gives a so-called Fourier-Bessel series.
The computation of the (usual) Fourier series is based on the integral identities
for , where is the
Kronecker delta.
Using the method for a generalized Fourier series, the usual Fourier series involving sines and cosines is obtained
by taking and . Since
these functions form a complete
orthogonal system over , the Fourier
series of a function is given by
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where
and , 2, 3, .... Note that the coefficient
of the constant term has been written in a special form
compared to the general form for a generalized
Fourier series in order to preserve symmetry with the definitions of and .
The Fourier cosine coefficient and sine coefficient
are implemented in Mathematica as FourierCosCoefficient[expr, t, n]
and FourierSinCoefficient[expr, t, n],
respectively.
A Fourier series converges to the function (equal to the
original function at points of continuity or to the average of the two limits at
points of discontinuity)
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if the function satisfies so-called Dirichlet conditions. Dini's test gives
a condition for the convergence of Fourier series.
As a result, near points of discontinuity, a "ringing" known as the Gibbs phenomenon, illustrated
above, can occur.
For a function periodic on an interval instead of
, a simple change of variables can be used
to transform the interval of integration from to . Let
Solving for gives , and plugging
this in gives
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Therefore,
Similarly, the function is instead defined on the interval , the above
equations simply become
In fact, for periodic with period , any interval
can be used, with the choice being one
of convenience or personal preference (Arfken 1985, p. 769).
The coefficients for Fourier series expansions of a few common functions are given in Beyer (1987, pp. 411-412)
and Byerly (1959, p. 51). One of the most common functions usually analyzed
by this technique is the square
wave. The Fourier series for a few common functions are summarized in the table
below.
If a function is even so that , then is odd. (This follows since is odd and an even
function times an odd function
is an odd function.) Therefore,
for all . Similarly, if
a function is odd so that , then is odd. (This follows since is even and an even
function times an odd function
is an odd function.) Therefore,
for all .
The notion of a Fourier series can also be extended to complex coefficients.
Consider a real-valued function . Write
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Now examine
so
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The coefficients can be expressed
in terms of those in the Fourier series
For a function periodic in , these
become
These equations are the basis for the extremely important Fourier transform, which is obtained by transforming from a discrete
variable to a continuous one as the length .
The complex Fourier coefficient is implemented in Mathematica as FourierCoefficient[expr, t, n].
Arfken, G. "Fourier Series." Ch. 14 in Mathematical Methods for Physicists, 3rd ed. Orlando, FL:
Academic Press, pp. 760-793, 1985.
Askey, R. and Haimo, D. T. "Similarities between Fourier and Power Series."
Amer. Math. Monthly 103, 297-304, 1996.
Beyer, W. H. (Ed.). CRC Standard Mathematical Tables, 28th ed. Boca Raton,
FL: CRC Press, 1987.
Brown, J. W. and Churchill, R. V. Fourier Series and Boundary Value Problems, 5th ed. New
York: McGraw-Hill, 1993.
Byerly, W. E. An Elementary Treatise on Fourier's Series, and Spherical, Cylindrical,
and Ellipsoidal Harmonics, with Applications to Problems in Mathematical Physics.
New York: Dover, 1959.
Carslaw, H. S. Introduction to the Theory of Fourier's Series and Integrals, 3rd
ed., rev. and enl. New York: Dover, 1950.
Davis, H. F. Fourier Series and Orthogonal Functions. New York: Dover,
1963.
Dym, H. and McKean, H. P. Fourier Series and Integrals. New York: Academic Press,
1972.
Folland, G. B. Fourier Analysis and Its Applications. Pacific Grove, CA:
Brooks/Cole, 1992.
Groemer, H. Geometric Applications of Fourier Series and Spherical Harmonics.
New York: Cambridge University Press, 1996.
Körner, T. W. Fourier Analysis. Cambridge, England: Cambridge University
Press, 1988.
Körner, T. W. Exercises for Fourier Analysis. New York: Cambridge University
Press, 1993.
Krantz, S. G. "Fourier Series." §15.1 in Handbook of Complex Variables. Boston, MA: Birkhäuser,
pp. 195-202, 1999.
Lighthill, M. J. Introduction to Fourier Analysis and Generalised Functions.
Cambridge, England: Cambridge University Press, 1958.
Morrison, N. Introduction to Fourier Analysis. New York: Wiley, 1994.
Sansone, G. "Expansions in Fourier Series." Ch. 2 in Orthogonal Functions, rev. English ed. New York: Dover,
pp. 39-168, 1991.
Weisstein, E. W. "Books about Fourier Transforms." http://www.ericweisstein.com/encyclopedias/books/FourierTransforms.html.
Whittaker, E. T. and Robinson, G. "Practical Fourier Analysis." Ch. 10 in The Calculus of Observations: A Treatise on Numerical Mathematics,
4th ed. New York: Dover, pp. 260-284, 1967.
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