Neural network structures and training algorithms for RF and microwave applications

Neural networks recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. After learning and abstra cting from micron ave data, through a process called training, neural netwo rk models are used during microwave design to provide instant answers to th e task learned. Appropriate neural network structure and suitable training algorithm are two of the major issues in developing neural network models f or microwave applications. Together. they decide amount of training data re quired, accuracy that could possibly be achieved, and more importantly deve lopmental cost of neural models. A review of the current status of this eme rging technology is presented, with emphasis on neural network structures a nd training algorithms suitable for micron ave applications. Present challe nges and future directions of the area are discussed. (C) 1999 John Wiley & Sons, Inc.