International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(1): 11-19
Published online March 25, 2023
https://doi.org/10.5391/IJFIS.2023.23.1.11
© The Korean Institute of Intelligent Systems
Hamzeh Zureigat^{1}, Abd Ulazeez Alkouri^{2}, Areen Al-khateeb^{1}, Eman Abuteen^{3}, and Sana Abu-Ghurra^{2}
^{1}Department of Mathematics, Faculty of Science and Technology, Jadara University, Irbid, Jordan
^{2}Department of Mathematics, Faculty of Science, Ajloun National university, Ajloun, Jordan
^{3}Department of Physics Basic Science, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
Correspondence to :
Hamzeh Husin Zureigat (hamzeh.zu@jadara.edu.jo)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Recently, complex fuzzy sets have become powerful tools for generalizing the range of fuzzy sets to wider ranges that lie on a unit disk in the complex plane. In this study, complex fuzzy numbers are discussed and applied for the first time to solve a complex fuzzy partial differential equation involving a complex fuzzy heat equation under Hukuhara differentiability. Subsequently, an explicit finite difference scheme, referred to as the forward time-center space (FTCS), was implemented to solve the complex fuzzy heat equations. The imprecision of the issue is evident in the initial and boundary conditions, as well as in the amplitude and phase terms’ coefficients, where the convex normalized triangular fuzzy numbers are extended to the unit disk in the complex plane. The proposed numerical methods utilized the properties and benefits of the complex fuzzy set theory. Furthermore, a new proof of consistency, stability, and convergence was established under this theory. A numerical example was provided to illustrate the reliability and feasibility of the proposed approach. The results obtained using the proposed approach are in adequate agreement with the exact solution and related theoretical aspects.
Keywords: Complex fuzzy sets, Finite difference methods, Fuzzy heat equations, Complex fuzzy numbers
The theory of fuzzy sets introduced by Zadeh handles uncertainty and vagueness in mathematical models to obtain a better understanding of real-life phenomena. Numerous real-world problems can be formulated as mathematical models involving differential equations. Classical (crisp) quantities in the differential equations that are uncertain and imprecise can be replaced by fuzzy quantities to reflect uncertainty. This leads to the following fuzzy differential equations. The interest in the analysis and applications of fuzzy differential equations has recently increased because of their considerable applicability in numerous fields, such as mathematical physics [1], engineering [2], and medicine [3–6].
A fuzzy partial differential equation has been used to describe the behavior of many time-dependent phenomena, including fuzzy heat conduction and fuzzy particle diffusion, in which uncertainty or vagueness exists. The fuzzy heat equation is considered one of the most significant fuzzy parabolic partial differential equations used to describe how a fuzzy quantity, such as heat, diffuses through a given region [7–19]. In general, the exact analytical solution for the fuzzy heat equations is difficult to obtain. Therefore, numerical techniques are required to obtain the solution. In recent decades, several studies have been conducted to solve the fuzzy heat equation. Allahviranloo [20] developed and used finite difference methods to solve fuzzy heat and wave equations. Fuzzy numerical solutions were obtained using the schemes proposed under Seikkala derivative with complete error analysis. Numerical examples are presented to investigate the efficiency of the proposed schemes. An extension of the differential transformation method (DTM) was considered by Barkhordari and Kiani [21] to solve fuzzy partial differential equations under a strongly generalized differentiability. The DTM is an iterative method for obtaining an analytical-numerical series solution for fuzzy partial differential equations. The proposed method is investigated through several numerical experiments. The DTM was found to be a simple and effective method for obtaining analytical-numerical solutions of fuzzy partial differential equations. Wang and Qiu [22] proposed a fuzzy numerical technique based on the finite difference method to solve heat conduction problems that involve uncertainties in both the initial/boundary conditions and physical parameters. The fuzzy difference discrete equations are equivalently transformed into groups of interval equations based on Zadeh extension principle, including basic fuzzification and defuzzification concepts. A stability analysis of this method was also conducted. Two numerical experiments are conducted to demonstrate the feasibility of the proposed method. Recently, Bayrak and Can [23] considered the concept of generalized differentiability to solve fuzzy parabolic partial differential equations using the finite difference method. Here, fuzziness appeared in the initial and boundary conditions as well as the coefficients. The results obtained were compared with the exact solutions at different fuzzy levels. The finite difference method was found to be simple and efficient for solving parabolic partial differential equations.
The above studies solved the governing equations under the concept of fuzzy set theory, which has a range of values in [0, 1]. Recently, certain medical applications have been represented by fuzzy sets to solve complex biological systems and create reasonable algorithmic solutions [24]. Fuzzy sets were developed into complex fuzzy sets (CFSs) by expanding the range of the membership function from [0, 1] to a unit disk in the complex plane. This expansion enables us to represent information in the human brain with greater detail without losing its full meaning. Normally, humans obtain meaningful information from large amounts of data and yield reasonable solutions. Human minds are affected by different phases and factors that lead to different changes in thinking and decisions. Therefore, our hypothesis was to use CFS to provide a suitable basis for the ability to summarize and extract large amounts of data related to the human brain affected by different phases/factors that are related to the performance of the task at hand.
In 1987, Buckley and his colleague [24–27] introduced the concept of fuzzy complex numbers (FCNs). Buckley’s definition incorporates complex numbers into the support of a fuzzy set to create FCNs. The fuzzy set represents the FCN as an ordinary fuzzy set with a range of membership functions within [0, 1]. However, the concept of generalizing the image of the membership function of fuzzy sets from [0, 1]to a unit disk in the complex plane crystallized a novel notion called CFSs in 2002 [28]. CFSs differ from FCNs, a novel fuzzy set with complex-valued grades of membership functions. The concept of CFSs [28] has been extensively adopted, applied, and studied by numerous scholars [29–39]. The innovation of the CFS appeared in further dimension membership, with the image function lying on the unit disk. The present idea of obtaining a wider range of CFS lies in its ability to denote both uncertainty and periodicity semantics simultaneously without losing its full meaning. The degree phase was developed to classify similar data measured at different phases or levels. Polar and Cartesian forms with two fuzzy components were used to represent complex fuzzy membership grades [21]. The uncertainty and periodicity semantics are denoted by the amplitude
The fuzzifications of the fuzzy heat equation are represented by values in (real numbers) [0, 1]. This study aims to fuzzify the complex fuzzy heat equation, represented by two values (amplitude and phase terms) in the unit disk in the complex plane, to provide generalization and increased accuracy in its solution by considering new periodicity semantics present in complex fuzzy information [28, 36].
Consider the general form of the one-dimensional complex fuzzy heat equation [37]
where
In
Here
Fuzzification of
A complex membership function was defined using the fuzzy extension principle (reference based on a complex).
Substituting
This section adapts and uses a forward difference approximation for the first-order time derivative and a central difference approximation for the second-order space derivative to solve the complex fuzzy heat equation.
The partial time derivative
Furthermore, the second partial derivatives
Subsequently, substitute
By assuming that
For each spatial grid point,
The forward time-centered space (FTCS) scheme in
Although the proof is for a crisp heat equation is standard, we describe it as a fuzzy complex heat equation for completeness.
Let
Let
Let
The fuzzy absolute error is established by the following form:
The fuzzy error equations for
Suppose that
Substituting
Divide
Since
By simplifying
For the Fourier method, a scheme is stable if and only if |
The fuzzy heat equation in [39] is generalized by adding the phase term, as discussed in the previous sections, as a complex heat equation as follows:
The boundary conditions are
Here,
and
The exact solution of
In Figures 1
In this study, a FCN was applied to solve the complex fuzzy heat equation based on the FTCS scheme. The fuzziness of the problem appears in the initial and boundary conditions, as well as the coefficients in both the amplitude and phase terms, simultaneously. The obtained results using the FTCS scheme satisfy the complex fuzzy number properties by assuming the triangular fuzzy number shape for both the real part and imaginary part and have an accuracy of order
No potential conflict of interest relevant to this article was reported.
Table 1. Numerical solution of Eq. (33) by FTCS at
Lower solution | Upper solution | ||||
---|---|---|---|---|---|
0 | −0.09091 − 0.279796 | 0.0030 + 0.0095 | 0.09091 − 0.279796 | 0.0030 + 0.0095 | |
0.2 | −0.07273 − 0.22383 | 0.0025 + 0.0076 | 0.07273 − 0.22383 | 0.0025 + 0.0076 | |
0.4 | −0.05455 − 0.16787 | 0.0019 + 0.0057 | 0.05455 − 0.16787 | 0.0019 + 0.0057 | |
0.6 | −0.03636 − 0.11191 | 0.0012 + 0.0038 | 0.03636 − 0.11191 | 0.0012 + 0.0038 | |
0.8 | −0.01818 − 0.05596 | 0.00061 + 0.0019 | 0.01818 − 0.05596 | 0.00061 + 0.0019 | |
1 | 0 | 0 | 0 | 0 | |
0 | 0.23800 − 0.17292 | 0.0081 + 0.0059 | −0.23800 − 0.17292 | 0.0081 + 0.0059 | |
0.2 | 0.19040 − 0.13833 | 0.0065 + 0.0047 | −0.19040 − 0.13833 | 0.0065 + 0.0047 | |
0.4 | 0.14280 − 0.10375 | 0.0049 + 0.0035 | −0.14280 − 0.10375 | 0.0049 + 0.0035 | |
0.6 | 0.09520 − 0.06917 | 0.0032 + 0.0023 | −0.09520 − 0.06917 | 0.0032 + 0.0023 | |
0.8 | 0.04760 − 0.03458 | 0.0016 + 0.0012 | −0.04760 − 0.03458 | 0.0016 + 0.0012 | |
1 | 0 | 0 | 0 | 0 |
Table 2. Numerical solution of Eq. (33) by FTCS at
Lower solution | Upper solution | ||||
---|---|---|---|---|---|
0 | 0.23800 + 0.17292 | 0.0081 + 0.0059 | −0.23800 + 0.17292 | 0.0081 + 0.0059 | |
0.2 | 0.19040 + 0.13833 | 0.0065 + 0.0047 | −0.19040 + 0.13833 | 0.0065 + 0.0047 | |
0.4 | 0.14280 + 0.10375 | 0.0049 + 0.0035 | −0.14280 + 0.10375 | 0.0049 + 0.0035 | |
0.6 | 0.09520 + 0.06917 | 0.0032 + 0.0023 | −0.09520 + 0.06917 | 0.0032 + 0.0023 | |
0.8 | 0.04760 + 0.03458 | 0.0016 + 0.0012 | −0.04760 + 0.03458 | 0.0016 + 0.0012 | |
1 | 0 | 0 | 0 | 0 | |
0 | −0.09091 + 0.279796 | 0.0030 + 0.0095 | 0.09091 + 0.279796 | 0.0030 + 0.0095 | |
0.2 | −0.07273 + 0.22383 | 0.0025 + 0.0076 | 0.07273 + 0.22383 | 0.0025 + 0.0076 | |
0.4 | −0.05455 + 0.16787 | 0.0019 + 0.0057 | 0.05455 + 0.16787 | 0.0019 + 0.0057 | |
0.6 | −0.03636 + 0.11191 | 0.0012 + 0.0038 | 0.03636 + 0.11191 | 0.0012 + 0.0038 | |
0.8 | −0.01818 + 0.05596 | 0.00061 + 0.0019 | 0.01818 + 0.05596 | 0.00061 + 0.0019 | |
1 | 0 | 0 | 0 | 0 |
E-mail: hamzeh.zu@jadara.edu.jo
E-mail: alkouriabdulazeez@gmail.com
E-mail: Areen.k@jadara.edu.jo
E-mail: Dr.eman.abuteen@bau.edu.jo
E-mail: sana3j@yahoo.com
International Journal of Fuzzy Logic and Intelligent Systems 2023; 23(1): 11-19
Published online March 25, 2023 https://doi.org/10.5391/IJFIS.2023.23.1.11
Copyright © The Korean Institute of Intelligent Systems.
Hamzeh Zureigat^{1}, Abd Ulazeez Alkouri^{2}, Areen Al-khateeb^{1}, Eman Abuteen^{3}, and Sana Abu-Ghurra^{2}
^{1}Department of Mathematics, Faculty of Science and Technology, Jadara University, Irbid, Jordan
^{2}Department of Mathematics, Faculty of Science, Ajloun National university, Ajloun, Jordan
^{3}Department of Physics Basic Science, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan
Correspondence to:Hamzeh Husin Zureigat (hamzeh.zu@jadara.edu.jo)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Recently, complex fuzzy sets have become powerful tools for generalizing the range of fuzzy sets to wider ranges that lie on a unit disk in the complex plane. In this study, complex fuzzy numbers are discussed and applied for the first time to solve a complex fuzzy partial differential equation involving a complex fuzzy heat equation under Hukuhara differentiability. Subsequently, an explicit finite difference scheme, referred to as the forward time-center space (FTCS), was implemented to solve the complex fuzzy heat equations. The imprecision of the issue is evident in the initial and boundary conditions, as well as in the amplitude and phase terms’ coefficients, where the convex normalized triangular fuzzy numbers are extended to the unit disk in the complex plane. The proposed numerical methods utilized the properties and benefits of the complex fuzzy set theory. Furthermore, a new proof of consistency, stability, and convergence was established under this theory. A numerical example was provided to illustrate the reliability and feasibility of the proposed approach. The results obtained using the proposed approach are in adequate agreement with the exact solution and related theoretical aspects.
Keywords: Complex fuzzy sets, Finite difference methods, Fuzzy heat equations, Complex fuzzy numbers
The theory of fuzzy sets introduced by Zadeh handles uncertainty and vagueness in mathematical models to obtain a better understanding of real-life phenomena. Numerous real-world problems can be formulated as mathematical models involving differential equations. Classical (crisp) quantities in the differential equations that are uncertain and imprecise can be replaced by fuzzy quantities to reflect uncertainty. This leads to the following fuzzy differential equations. The interest in the analysis and applications of fuzzy differential equations has recently increased because of their considerable applicability in numerous fields, such as mathematical physics [1], engineering [2], and medicine [3–6].
A fuzzy partial differential equation has been used to describe the behavior of many time-dependent phenomena, including fuzzy heat conduction and fuzzy particle diffusion, in which uncertainty or vagueness exists. The fuzzy heat equation is considered one of the most significant fuzzy parabolic partial differential equations used to describe how a fuzzy quantity, such as heat, diffuses through a given region [7–19]. In general, the exact analytical solution for the fuzzy heat equations is difficult to obtain. Therefore, numerical techniques are required to obtain the solution. In recent decades, several studies have been conducted to solve the fuzzy heat equation. Allahviranloo [20] developed and used finite difference methods to solve fuzzy heat and wave equations. Fuzzy numerical solutions were obtained using the schemes proposed under Seikkala derivative with complete error analysis. Numerical examples are presented to investigate the efficiency of the proposed schemes. An extension of the differential transformation method (DTM) was considered by Barkhordari and Kiani [21] to solve fuzzy partial differential equations under a strongly generalized differentiability. The DTM is an iterative method for obtaining an analytical-numerical series solution for fuzzy partial differential equations. The proposed method is investigated through several numerical experiments. The DTM was found to be a simple and effective method for obtaining analytical-numerical solutions of fuzzy partial differential equations. Wang and Qiu [22] proposed a fuzzy numerical technique based on the finite difference method to solve heat conduction problems that involve uncertainties in both the initial/boundary conditions and physical parameters. The fuzzy difference discrete equations are equivalently transformed into groups of interval equations based on Zadeh extension principle, including basic fuzzification and defuzzification concepts. A stability analysis of this method was also conducted. Two numerical experiments are conducted to demonstrate the feasibility of the proposed method. Recently, Bayrak and Can [23] considered the concept of generalized differentiability to solve fuzzy parabolic partial differential equations using the finite difference method. Here, fuzziness appeared in the initial and boundary conditions as well as the coefficients. The results obtained were compared with the exact solutions at different fuzzy levels. The finite difference method was found to be simple and efficient for solving parabolic partial differential equations.
The above studies solved the governing equations under the concept of fuzzy set theory, which has a range of values in [0, 1]. Recently, certain medical applications have been represented by fuzzy sets to solve complex biological systems and create reasonable algorithmic solutions [24]. Fuzzy sets were developed into complex fuzzy sets (CFSs) by expanding the range of the membership function from [0, 1] to a unit disk in the complex plane. This expansion enables us to represent information in the human brain with greater detail without losing its full meaning. Normally, humans obtain meaningful information from large amounts of data and yield reasonable solutions. Human minds are affected by different phases and factors that lead to different changes in thinking and decisions. Therefore, our hypothesis was to use CFS to provide a suitable basis for the ability to summarize and extract large amounts of data related to the human brain affected by different phases/factors that are related to the performance of the task at hand.
In 1987, Buckley and his colleague [24–27] introduced the concept of fuzzy complex numbers (FCNs). Buckley’s definition incorporates complex numbers into the support of a fuzzy set to create FCNs. The fuzzy set represents the FCN as an ordinary fuzzy set with a range of membership functions within [0, 1]. However, the concept of generalizing the image of the membership function of fuzzy sets from [0, 1]to a unit disk in the complex plane crystallized a novel notion called CFSs in 2002 [28]. CFSs differ from FCNs, a novel fuzzy set with complex-valued grades of membership functions. The concept of CFSs [28] has been extensively adopted, applied, and studied by numerous scholars [29–39]. The innovation of the CFS appeared in further dimension membership, with the image function lying on the unit disk. The present idea of obtaining a wider range of CFS lies in its ability to denote both uncertainty and periodicity semantics simultaneously without losing its full meaning. The degree phase was developed to classify similar data measured at different phases or levels. Polar and Cartesian forms with two fuzzy components were used to represent complex fuzzy membership grades [21]. The uncertainty and periodicity semantics are denoted by the amplitude
The fuzzifications of the fuzzy heat equation are represented by values in (real numbers) [0, 1]. This study aims to fuzzify the complex fuzzy heat equation, represented by two values (amplitude and phase terms) in the unit disk in the complex plane, to provide generalization and increased accuracy in its solution by considering new periodicity semantics present in complex fuzzy information [28, 36].
Consider the general form of the one-dimensional complex fuzzy heat equation [37]
where
In
Here
Fuzzification of
A complex membership function was defined using the fuzzy extension principle (reference based on a complex).
Substituting
This section adapts and uses a forward difference approximation for the first-order time derivative and a central difference approximation for the second-order space derivative to solve the complex fuzzy heat equation.
The partial time derivative
Furthermore, the second partial derivatives
Subsequently, substitute
By assuming that
For each spatial grid point,
The forward time-centered space (FTCS) scheme in
Although the proof is for a crisp heat equation is standard, we describe it as a fuzzy complex heat equation for completeness.
Let
Let
Let
The fuzzy absolute error is established by the following form:
The fuzzy error equations for
Suppose that
Substituting
Divide
Since
By simplifying
For the Fourier method, a scheme is stable if and only if |
The fuzzy heat equation in [39] is generalized by adding the phase term, as discussed in the previous sections, as a complex heat equation as follows:
The boundary conditions are
Here,
and
The exact solution of
In Figures 1
In this study, a FCN was applied to solve the complex fuzzy heat equation based on the FTCS scheme. The fuzziness of the problem appears in the initial and boundary conditions, as well as the coefficients in both the amplitude and phase terms, simultaneously. The obtained results using the FTCS scheme satisfy the complex fuzzy number properties by assuming the triangular fuzzy number shape for both the real part and imaginary part and have an accuracy of order
Exact solution of
Exact and numerical solution of
Exact and numerical solution of
Table 1 . Numerical solution of Eq. (33) by FTCS at
Lower solution | Upper solution | ||||
---|---|---|---|---|---|
0 | −0.09091 − 0.279796 | 0.0030 + 0.0095 | 0.09091 − 0.279796 | 0.0030 + 0.0095 | |
0.2 | −0.07273 − 0.22383 | 0.0025 + 0.0076 | 0.07273 − 0.22383 | 0.0025 + 0.0076 | |
0.4 | −0.05455 − 0.16787 | 0.0019 + 0.0057 | 0.05455 − 0.16787 | 0.0019 + 0.0057 | |
0.6 | −0.03636 − 0.11191 | 0.0012 + 0.0038 | 0.03636 − 0.11191 | 0.0012 + 0.0038 | |
0.8 | −0.01818 − 0.05596 | 0.00061 + 0.0019 | 0.01818 − 0.05596 | 0.00061 + 0.0019 | |
1 | 0 | 0 | 0 | 0 | |
0 | 0.23800 − 0.17292 | 0.0081 + 0.0059 | −0.23800 − 0.17292 | 0.0081 + 0.0059 | |
0.2 | 0.19040 − 0.13833 | 0.0065 + 0.0047 | −0.19040 − 0.13833 | 0.0065 + 0.0047 | |
0.4 | 0.14280 − 0.10375 | 0.0049 + 0.0035 | −0.14280 − 0.10375 | 0.0049 + 0.0035 | |
0.6 | 0.09520 − 0.06917 | 0.0032 + 0.0023 | −0.09520 − 0.06917 | 0.0032 + 0.0023 | |
0.8 | 0.04760 − 0.03458 | 0.0016 + 0.0012 | −0.04760 − 0.03458 | 0.0016 + 0.0012 | |
1 | 0 | 0 | 0 | 0 |
Table 2 . Numerical solution of Eq. (33) by FTCS at
Lower solution | Upper solution | ||||
---|---|---|---|---|---|
0 | 0.23800 + 0.17292 | 0.0081 + 0.0059 | −0.23800 + 0.17292 | 0.0081 + 0.0059 | |
0.2 | 0.19040 + 0.13833 | 0.0065 + 0.0047 | −0.19040 + 0.13833 | 0.0065 + 0.0047 | |
0.4 | 0.14280 + 0.10375 | 0.0049 + 0.0035 | −0.14280 + 0.10375 | 0.0049 + 0.0035 | |
0.6 | 0.09520 + 0.06917 | 0.0032 + 0.0023 | −0.09520 + 0.06917 | 0.0032 + 0.0023 | |
0.8 | 0.04760 + 0.03458 | 0.0016 + 0.0012 | −0.04760 + 0.03458 | 0.0016 + 0.0012 | |
1 | 0 | 0 | 0 | 0 | |
0 | −0.09091 + 0.279796 | 0.0030 + 0.0095 | 0.09091 + 0.279796 | 0.0030 + 0.0095 | |
0.2 | −0.07273 + 0.22383 | 0.0025 + 0.0076 | 0.07273 + 0.22383 | 0.0025 + 0.0076 | |
0.4 | −0.05455 + 0.16787 | 0.0019 + 0.0057 | 0.05455 + 0.16787 | 0.0019 + 0.0057 | |
0.6 | −0.03636 + 0.11191 | 0.0012 + 0.0038 | 0.03636 + 0.11191 | 0.0012 + 0.0038 | |
0.8 | −0.01818 + 0.05596 | 0.00061 + 0.0019 | 0.01818 + 0.05596 | 0.00061 + 0.0019 | |
1 | 0 | 0 | 0 | 0 |
Exact solution of
Exact and numerical solution of
Exact and numerical solution of