In this research for the first time by using radial basis function neural network (RBFNN), filters based on serially coupled microring resonators have been modeled. Also, signal flow graph (SFG) method based on Mason’s rule has been used to simulate filters. It has been represented when RBFNN has been learned, model can extract the outputs same as what was simulated by SFG method. It has been proved that RBFNN model can properly obtain results in several cases in which some parameters of filter like the order of filter; MRRs radius; coupling coefficients; and propagation loss have been changed. In these cases to design filter by an analytical method like the SFG, we need to obtain new transfer function. Obtaining novel transfer function would make filter designating complicated in terms of calculation and simulation time while the RBFNN can match with any change as fast as possible. The RBFNN has advantages of optimization ability, straightforward topological architecture, stable generalization ability, appropriate tolerance against input noise, online learning ability, accuracy in dynamically nonlinear approximation, predictability and fast and easy learning algorithms. These properties of RBFNN make it suitable to model pliable optical systems.
