Fractional (non-integer order) differential equations often describe the behavior of various materials and process better than the classical differential equations with integer order derivatives. However, it is usually not possible to find an exact solution to these equations, and so we need to find their solutions approximately, which requires the development of special methods. The question, which functions are fractionally differentiable, also continues to be relevant.
Fuzzy integral and differential equations contain functions with fuzzy values, which describe the situation where the information is incomplete or approximate, e.g. due to measurement errors or noise. The existence and uniqueness and smoothness of solutions to such problems together with numerical methods are of interest.
The solution of the ill-posed problem does not depend continuously on the data, and to reduce the effect of data errors (e.g. measurement errors), special regularization methods are used to solve such problems. The main problem of using regularization methods is the choice of a suitable regularization parameter depending on the information on the noise level of the data. In practice, the noise level information is often unknown, and parameter selection rules that do not use this information are of particular interest.
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