Abstract: An overview of advanced neural network methods for modeling radio-frequency (RF) and microwave electronic devices is presented. Knowledge-based engineering concept is utilized where the knowledge of RF/microwave electronics in the form of equivalent circuits and empirical formulas is combined with neural networks. Advantages of adding knowledge on the performance of the neural models in terms of generalization ability versus different sizes of training data through a knowledge based neural network (KBNN) technique are demonstrated and examples of comparisons with conventional MLP (without any knowledge-base) are given. Several methods of combining existing circuit models with neural networks, including the source difference method, the prior knowledge input method, and the space-mapped neural models, are also introduced. Application examples on modeling microwave transmission line and high electron mobility transistor (HEMT) device demonstrate that KBNN is an efficient approach for modeling various types of microwave devices.