Evolving connectionist systems: A theory and a case study on adaptive speech recognition

摘要

The paper introduces evolving connectionist systems (ECOS) as an effective approach to building online adaptive intelligent systems. ECOS evolve through incremental, hybrid (supervised/unsupervised), online learning. They can accommodate new input data, including new features, new classes, etc. through local element tuning. New connections and new neurons are created during the operation of the system. The ECOS framework is presented and illustrated on a particular type of evolving neural networks-evolving fuzzy neural network (EFuNN). EFuNN can learn spatial-temporal sequences in an adaptive way, through one pass learning. Rules can be inserted and extracted at any time of the system operation. The characteristics of ECOS and EFuNN are illustrated on a case study of adaptive, phoneme-based spoken language recognition.