Emotional neural networks provide promising characteristics such as fast response, learning ability, and approximation property. They are thus expected to handle the uncertainties and complexities when applied to the uncertain nonlinear systems. This paper employs the Radial Basis Emotional Neural Network (RBENN) in an indirect adaptive control design for approximating the unknown dynamics of a class of uncertain affine nonlinear systems. The proposed method adaptively updates the parameters of the radial basis functions in Thalamus nodes in addition to the adaptive weights of the Amygdala and the orbitofrontal cortex. The overall stability of the system is also verified according to the Lyapunov stability theory. Simulation results show the superiority of the proposed controller in considerably lower tracking error using slightly lower control energy compared to the RBENN with nonadaptive parameters of the radial basis functions.

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