Training techniques for neural network applications in ATM

The main problems of adaptive ATM quality of service (QoS) control methods using neural networks were the exponentially wide range of the output target and the real-time training data sampling. But new practical techniques to overcome these problems may open new neural network applications. In this article, the framework of connection admission control (CAC) is described as a typical example of neural-network-based QoS estimation and two practical techniques, called relative target method and virtual output buffer method, are presented to enhance the neural network performance in CAC