Abstract
Future of communications inevitably calls for technologies that feature high-level flexibility, adaptability, and intelligence. Intelligent adaptable systems are particularly suitable for this mission. Majority of adaptable systems utilize neural networks. Artificial neural networks are systems with huge network-like interconnectivity. They are not programmed. Neural Networks gain valuable properties through the process of adaptation called learning. Presented intelligent adaptable system utilizes neural network technology. The system incorporates internal adaptability at several levels. It autonomously adapts its parameters and structure to the presented data. Externally, it appropriately manages its input-output interfaces. The system is able to select suitable training exemplars from the available amount of data in order to achieve the optimal learning performance. After training the system provides logical output format of the task. Introduced intelligent adaptable system consists of several modules. Principle and functionality of each module is described and illustratively demonstrated.
Highlights
Rapid expansion of communication technologies is widely influencing our everyday life
The presented dynamic sample selection is capable of selecting an appropriate exemplar set that may vary in size and in the selected samples at each iteration of training
Controlling the performance of a network by eliminating the low-performance structural elements and increasing the performance of a network by adding new structural elements represents a new concept for dynamic structural adaptation of neural networks
Summary
Rapid expansion of communication technologies is widely influencing our everyday life. The com-munication systems are composed of units that process signal/information transmitted over cables or ether using welldefined protocols such as TCP/IP, ISDN, CDMA, TDMA, etc. This apparent parallel uncovers enormous potential of neural networks in communication technologies of future. With the current advancement of neural networks all these qualities can be achieved using conventional computer systems without substantial investment in rebuilding the existing physical communication infrastructure This economical solution is likely to determine the future course of communication technologies. Further expansion and progress of communication technologies and services will uncover numerous other areas where IAS will be the preferred technological choice
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