Abstarct: Aabstract In this dissertation, an intelligent approach baxsed on neural networks and fuzzy systems is presented for solving a class of fuzzy optimal control problems under generalized Hukuhara and granular derivatives. At the first step, we consider the fuzzy Euler-Lagrange conditions for fuzzy calculus of variations problems and the Pontryagin Maximum Principle (PMP) for fuzzy optimal control problems, both of them depending on the generalized Hukuhara derivatives. The necessary optimality conditions for these problems are examined in the form of two-point boundary value problems (TPBVPs). A fuzzy neural network approach that utilizes Radial Basis Functions (RBFs) as activation functions for one of its hidden laxyers is employed to approximate the solutions of the TPBVP. The proposed neural network uses a combination of points as the training dataset, and the Levenberg-Marquardt algorithm is selected as the optimizer. In the optimization problem, trial solutions for the state, co-state, and control functions are used, wherein these trial solutions are constructed using a basic Radial Basis Function Neural Network (RBFNN) model. Furthermore, simulation results demonstrate that the proposed model is feasible and effective. Due to some problems of Hukuhara derivatives, solving fuzzy optimal control problems under granular derivatives using generalized hyperbolic fuzzy models is also investigated.