In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using two synaptic weight neural networks (TSWNN). Firstly, a novel approach to train the TWSWNN is introduced, which employs an adaptive fuzzy generalized learning vector quantization (AFGLVQ) technique and recursive least squares algorithm with variable forgetting factor (VRLS). The AFGLVQ adjusts the kernels of the TSWNN while the VRLS updates the connection weights of the network. The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. Secondly, on the basis of the one-step ahead TSWNN predictor, the control law is optimized iteratively through a numerical stable Davidon?s least squares-based (SDLS) minimization approach. A nonlinear example is simulated to demonstrate the effectiveness of the identification and control algorithms.