1. Introduction
One of the differential equations that converts nonlinear equations into linear equations is the Bernoulli differential equation. A Bernoulli differential equation is an equation of the form
where
m is any real number,
and
are continuous functions on the interval. If
or
, the above equation is linear, and if not, the equation is nonlinear. The Bernoulli differential equation can be reduced to a linear differential equation with substitution
. Then, for
u we obtain a linear equation
. This Bernoulli differential equation has many applications to problems modeled by nonlinear differential equations, equations about the population expressed in logistic equations or Verhulst equations, physics, etc.
If
in (1), then the Bernoulli differential equation has the solution which is the generating function of the Bernoulli polynomials. The equation is as follows.
where
is the Bernoulli polynomials, see [
1,
2].
For
, the Bernoulli numbers and polynomials can be expressed as
Based on the concept above, we can consider the
q-Bernoulli differential equation of the first order
in
q-calculus. When
in (1), the
q-Bernoulli polynomials are a solution of the following
q-differential equation of the first order.
where
is the derivative in
q-calculus and
is the
q-Bernoulli polynomials, see [
3,
4]. The
q-Bernoulli numbers and polynomials can be expressed as
We note that (3) becomes (2) when
.
Many mathematicians discovered
q-differential equations by using special polynomials as a solution and studying their properties and identities, see [
5,
6]. For example, in [
5], Hermoso, Huertas, Lastra, and Soria-Lorente studied the
q-differential equation based on
q-Hermit polynomials. In [
6],
q-differential equations and properties related to Euler polynomials and Genocchi polynomials were studied.
Based on the above paper, our purpose is to find various
q-differential equations of higher order that contain
q-Bernoulli polynomials as a solution of the
q-differential equation of higher order. In
Section 3, we find
q-differential equations of higher order that have
q-Bernoulli polynomials as the solution by expanding the equation in (3) and check its associated symmetric properties. In
Section 4, we find the values of
q-Bernoulli numbers, show the approximate roots of
q-Bernoulli polynomials, and organize several conjectures for
q-Bernoulli numbers and polynomials. Using
q-Bernoulli polynomials, we construct a Mandelbrot set and Julia set and find the results of various figures and phenomena in
Section 5.
2. Preliminaries
To reach the goal of this paper, we will summarize the definitions and theorems and make arrangements as follows.
The
q-number, that plays an important role in
q-calculus, is first introduced by Jackson, see [
7,
8]. From the discovery of the
q-number, various useful results are studied in
q-series,
q-special functions, quantum algebras,
q-discrete distribution,
q-differential equations,
q-calculus, etc., see [
3,
4,
6,
7,
9,
10,
11,
12,
13,
14,
15]. Here, we briefly review several concepts of
q-calculus which we need for this paper.
Let
with
. The number
is called
q-number, see [
11]. We note that
. In particular, for
,
is called
q-integer.
The
q-Gaussian binomial coefficients are defined by
where
m and
r are nonnegative integers, see [
9,
11,
16]. For
, the value is 1 since the numerator and the denominator are both empty products. One notes
and
.
Definition 1. Let z be any complex numbers with . Two forms of q-exponential functions can be expressed as We note that
, see [
10,
11].
Theorem 1. From Definition 1, we note that
- (i)
- (ii)
- (iii)
From the result of using the two concepts of
q-exponential functions, new types of Bernoulli, Euler, and Genocchi polynomials appear and many mathematicians have studied their properties and identities. This topic is studied in various studies via computer, see [
1,
2,
3,
4,
6,
12,
13,
17,
18]. The generating functions of
q-Euler polynomials and
q-Genocchi polynomials used in this paper can be confirmed in Definitions 2 and 3, see [
1,
2,
3,
4,
13].
Definition 2. The generating function for the q-Euler numbers and polynomials are Let
in Definition 2. Then, we can find the Euler numbers and polynomials as
Definition 3. The generating function for the q-Genocchi numbers and polynomials are Setting
in Definition 3, we can find the Genocchi numbers and polynomials as
Definition 4. The q-derivative of a function f with respect to x is defined byand .
We can prove that
f is differentiable at zero and it is clear that
, see [
7,
10,
14,
15]. From Definition 4, we can see some formulae for
q-derivative.
Theorem 2. From Definition 4, we note that
- (i)
- (ii)
- (iii)
Definition 5. We define the iterated maps of functions as follows: denote the iterated map of function f as such that Definition 6. The orbit of the point under the action of the function of f is said to be bounded if there exists such that for all . If not, it is said to be unbounded.
Let be a complex function, with X as a subset of . Point z is said to be a fixed point of f if . Point z is said to be a periodic point of the period n of f if n is the smallest natural number such that . We also say that is a fixed point or a 1 is a periodic point.
Definition 7 ([
19]).
Let be given. Then, the Mandelbrot sequence is We know the Mandelbrot set expected for the divergent sequence in the complex plane, i.e., , where .
Definition 8 ([
19]).
Let be the fixed point. The Julia sequence is then We also know the Julia set for the complex plane, i.e., , where .
4. The Observation of -Bernoulli Numbers and Scattering Zeros of the -Bernoulli Polynomials
In this section, we try to find approximate values of q-Bernoulli numbers and approximate roots of q-Bernoulli polynomials which appear with changes in q. We use MATHEMATICA to pile up the structure and make some conjectures based on this.
Based on the generating function of
q-Bernoulli numbers, several
q-Bernoulli numbers
are found as follows:
From
q-Bernoulli numbers,
Table 1 shows the approximate values of
which appear with changes in
q. In
Table 1, we can see that if
q decreases, there are approximate values of
q-Bernoulli numbers near the absolute value of zero.
From
Table 1, we can see the location of
shown by varying
q and
n, as shown in
Figure 1. In
Figure 1, nonnegative integers of the
x-axis represent the value of
n.
For example, it means that 0 is
, 1 is
, ⋯, and 15 is
. The blue dots, the yellow squares, the green rhombuses, and the red triangles in
Figure 1 are the approximate values of
q-Bernoulli numbers when
, respectively. The lines represent variations of the approximate values for
q-Bernoulli numbers. Here, from
Table 1 and
Figure 1, we can observe the following.
Conjecture 1. If the value of n increases, then the value of q-Bernoulli numbers approaches zero when.
Next, several
q-Bernoulli polynomials
are shown in the following:
Let us fix
. Then, we can find the structure of the approximate roots of
q-Bernoulli polynomials shown by varying
q as in
Figure 2. The condition of the left figure (a) is
, the condition of the middle figure (b) is
, and the condition of the right figure (c) is
.
From
Figure 2, it can be inferred that the structures of approximate roots for
q-Bernoulli polynomials change from elliptical to circular form as
q becomes smaller and
n increases. In addition, it can be seen that the numbers of real roots among the approximate roots decrease as
q becomes smaller in
Figure 2.
Table 2 shows the approximate real zeros of
and
with changes in
n. In
Table 2, we can see that if
, one of the approximate real root values is 1 in
. Additionally, if
, one of the approximate real root values is 1 in
. Therefore, we can organize the following.
Conjecture 2. If n increases, then one of the approximate real root values forandis 1.
To find the exact results of the experiment,
Figure 3 shows the results of the changed value of
q. Let us fix
. The left figure (a) in
Figure 3 shows the location for approximate roots of
q-Bernoulli polynomials under the condition of
, the condition of the middle figure (b) is
, and the condition of the right figure (c) is
. Panels (a), (b), and (c) in
Figure 3 contain the approximate real roots and the approximate imaginary roots for
q-Bernoulli polynomials. Here, we expect the approximate root location for
q-Bernoulli polynomials to approach any approximate diagram. Hence, we remove the approximate real roots of
of a changed value of
q because these polynomials have some approximate real roots.
Let us consider
for
q-Bernoulli polynomials. In (d), (e), and (f) of
Figure 3, the red dots are the approximate imaginary roots for
, the blue dots are the circle’s centers, and the blue lines represent the circles closest to the approximate imaginary roots. The lower left figure (d) in
Figure 3 is when
, the lower middle figure (e) is when
, and the lower right figure (f) is when
.
Figure 3 shows the location of the approximate roots of
q-Bernoulli polynomials approximated in circular form.
Table 3 shows the information in detail regarding (d), (e), and (f) in
Figure 3. The position of the blue dot in (d) of
Figure 3 is
and the radius of the circle of (d) is
. The error range between the blue line and the red dots in (d) of
Figure 3 is
. In addition, we can see that the position of the blue dot in (f) of
Figure 3 is
, the radius of the circle of (f) is
, and the error range of the blue line and the red dots in (f) of
Figure 3 is
.
Conjecture 3. Let us fix. If, then the location of approximate roots forq-Bernoulli polynomials can be found on the circle.
5. Visualization of the Mandelbrot Set and the Julia Set for q-Bernoulli Polynomials
In this section, we introduce the Mandelbrot set and the Julia set for q-Bernoulli polynomials and show their special properties according to the change in q. Figures of this section are investigated using the C program.
Let us consider
. Then, the Mandelbrot set is
. By using the escape time algorithm, the Mandelbrot set iterated 64 times can be seen in the left picture in
Figure 4. As shown in (a) in
Figure 4, the range of convergence is the absolute value of 2. Panel (a) in
Figure 4 shows that the top and bottom are symmetrical. In
Figure 4, the image center of the figure in (b) is
. Panel (b) is an enlarged part of (a) because the boundary of the Mandelbrot set shows more intricate detail as one looks closer or magnifies the image. The line in (c) in
Figure 4 represents periodic critical orbit with period 4. The point
in the Mandelbrot set is a periodic point with period 4 if its critical orbit is periodic with period 4.
Figure 5 shows the properties of the Mandelbrot set created by
. Panel (a) of
Figure 5 shows the information about the accumulated points. As the point colors go from red to blue, we can see that there are more accumulated points.
We show some fixed points which are periodic points with periods equal to one in (b) in
Figure 5. In this figure, the green dots represent fixed points, and we can see that it consists of four dots. Additionally, it can be seen that there is a portion in which the fixed points are clustered, since the sizes of the points are different. In (c) in
Figure 5, a point
is connected to one of the attracting fixed points of the function.
Figure 6 and
Figure 7 show the visualization of the Julia set’s various shapes for
by setting the number of iterations to 128 or 64. Additionally, we consider the convergence range as 2. Panel (a) of
Figure 6 shows the range of the real axis from
to
and the range of the imaginary axis from
to
. The polynomial here is
, the offset is shown if
, and the center of the image is
. To show this figure, we use HSV image, and light blue means low iterations and yellow means the 128th iteration. If we look closely in this figure, we can see that the 128th appearing area is indicated by a dot.
Panel (b) of
Figure 6 shows the range of the real axis from
to
and the range of the imaginary axis from
to
, the offset is shown in
, and the convergence range of figure is 2. This figure shows a continent which is one of the representative shapes of the Julia set. The blue color shows low iterations and the green color shows the 128th iteration. In (c) of
Figure 6, the range of the real axis and the range of the imaginary axis are the same condition as (b), the offset is shown in
, and the image center is
. Here, we use the gray image color and as we get closer to the black color, this means 128 iteration was complete.
Panel (a) of
Figure 7 shows the range of the real axis from
to
and the range of the imaginary axis from
to
when the offset is
. Here, the image center of (a) in
Figure 7 is
. We use the gray image color and as we get closer to the black color, this means the 128th iteration was complete. To find the property of self-similarity in this figure, we change the scale from
to
.
Panel (b) in
Figure 7 shows the range of the real axis from
to
and the range of the imaginary axis from
to
, the offset is shown in
, the maximum iteration is 64, and the convergence range of figure is 2. The red color shows low iterations and the blue color shows the 64th iteration.
In panel (b) of
Figure 7, it can be seen that various periodic points appear. Panel (c) of
Figure 7 shows the range of the real axis from
to
and the range of the imaginary axis from
to
, the offset is shown if
, and the center of the image is
. To show this figure, we used HSV image. In addition, the red color means few iterations were carried out and the blue color means 128 iterations were carried out. In (c), one can find that the 128th appearing area is indicated by blue dots.