Neural networks for microwave modeling: Model development issues and nonlinear modeling techniques

Abstract
Artificial neural networks (ANN) recently gained attention as a fast and flexible vehicle to microwave modeling and design. Fast neural models trained from measured/simulated microwave data can be used during microwave design to provide instant answers to the task they have learned. We review two important aspects of neural-network-based microwave modeling, namely, model development issues and nonlinear modeling. A systematic description of key issues in neural modeling approach such as data generation, range and distribution of samples in model input parameter space, data scaling, etc., is presented. Techniques that pave the way for automation of neural model development could be of immense interest to microwave engineers, whose knowledge about ANN is limited. As such, recent techniques that could lead to automatic neural model development, e.g., adaptive controller and adaptive sampling, are discussed. Neural modeling of nonlinear device/circuit characteristics has emerged as an important research area. An overview of nonlinear techniques including small/large signal neural modeling of transistors and dynamic recurrent neural network (RNN) modeling of circuits is presented. Practical microwave examples are used to illustrate the reviewed techniques. © 2001 John Wiley & Sons, Inc. Int J RF and Microwave CAE 11: 4–21, 2001.

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