Neural network structures and training algorithms for RF and microwave applications

✍ Scribed by Fang Wang; Vijaya K. Devabhaktuni; Changgeng Xi; Qi-Jun Zhang

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 386 KB
• Volume: 9
• Category: Article
• ISSN: 1096-4290
• DOI: 10.1002/(sici)1099-047x(199905)9:3<216::aid-mmce7>3.0.co;2-w

✦ Synopsis

Neural networks recently gained attention as fast and flexible vehicles to microwave modeling, simulation, and optimization. After learning and abstracting from microwave data, through a process called training, neural network models are used during microwave design to provide instant answers to the task learned. Appropriate neural network structure and suitable training algorithm are two of the major issues in developing neural network models for microwave applications. Together, they decide amount of training data required, accuracy that could possibly be achieved, and more importantly developmental cost of neural models. A review of the current status of this emerging technology is presented, with emphasis on neural network structures and training algorithms suitable for microwave applications. Present challenges and future directions of the area are discussed.