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An integrated approach of radial basis function neural network (RBFNN) and Takagi-Sugeno (TS) fuzzy scheme with a genetic optimization of their parameters has been developed in this paper to design intelligent adaptive controllers for improving the transient stability performance of power systems. At the outset, this concept is applied to a simple device such as thyristor-controlled series capacitor (TCSC) connected in a single-machine infinite bus power system and is then extended to interline power-flow controller (IPFC) connected in a multimachine power system. The RBFNN uses single neuron architecture and its parameters are dynamically updated in an online fashion with TS-fuzzy scheme designed with only four rules and triangular membership function. The rules of the TS-fuzzy scheme are derived from the real- or reactive-power error and their derivatives either at the TCSC or IPFC buses depending on the device. Further, to implement this combined scheme only one coefficient in the TS-fuzzy rules needs to be optimized. The optimization of this coefficient as well as the coefficient for auxiliary signal generation is performed through genetic algorithm. The performance of the new controller is evaluated in single-machine and multimachine power systems subjected to various transient disturbances. The new genetic-neuro-fuzzy control scheme exhibits a superior damping performance as well as a greater critical clearing time in comparison to the existing PI and RBFNN controller with updating of its parameters through the extended Kalman filter (EKF). Its simple architecture reduces the computational burden, thereby making it attractive for real-time implementation. Index Terms-Damping modal oscillations, FACTS, fuzzy, genetic, intelligent controller, neural, power system, stability.