Our purpose of this paper was to investigate a class of novel Caputo–Hadamard type fuzzy fractional partial differential coupled systems with generalized Hukuhara difference and integral boundary conditions. We proposed properties of the solution, including its existence and uniqueness, continuous dependence on the initial conditions, and chaotic behavior in specific cases. Using Banach fixed-point theorem, we established the existence and uniqueness theorems of solutions for the partial differential coupled systems, and subsequently discussed continuous dependence of the solutions on initial conditions. Furthermore, a numerical example is presented to validate the major conclusions. The local solution ehibited chaotic behavior, which was accompanied by a corresponding circuit implementation. Finally, the existence and uniqueness of the solution for a novel fuzzy projection neural network system were established.
Citation: Si-yuan Lin, Heng-you Lan, Ji-hong Li. Solution continuous dependence of novel Caputo–Hadamard type fuzzy fractional partial differential coupled systems with applications[J]. AIMS Mathematics, 2026, 11(4): 10400-10443. doi: 10.3934/math.2026429
Our purpose of this paper was to investigate a class of novel Caputo–Hadamard type fuzzy fractional partial differential coupled systems with generalized Hukuhara difference and integral boundary conditions. We proposed properties of the solution, including its existence and uniqueness, continuous dependence on the initial conditions, and chaotic behavior in specific cases. Using Banach fixed-point theorem, we established the existence and uniqueness theorems of solutions for the partial differential coupled systems, and subsequently discussed continuous dependence of the solutions on initial conditions. Furthermore, a numerical example is presented to validate the major conclusions. The local solution ehibited chaotic behavior, which was accompanied by a corresponding circuit implementation. Finally, the existence and uniqueness of the solution for a novel fuzzy projection neural network system were established.
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