Reflective Modular Neural Network Systems

摘要

Many of the current artificial neural network systems have serious limitations,concerning accessibility, flexibility, scaling and reliability. In order to go some way to removing these, the authors suggest a reflective neural network architecture. In such an architecture, the modular structure is the most important element. The building-block elements are called 'MINOS' modules. They perform self-observation and inform on the current level of development, or scope of expertise, within the module. A Pandemonium system integrates such submodules so that they work together to handle mapping tasks. Network complexity limitations are attacked in this way with the Pandemonium problem decomposition paradigm, and both static and dynamic unreliability of the whole Pandemonium system is effectively eliminated through the generation and interpretation of confidence and ambiguity measures at every moment during the development of the system. (Copyright (c) GMD 1992.)