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Hybrid intelligent adaptive systems: A framework and a case study on speech recognition
Author(s) -
Kasabov Nikola,
Kozma Robert
Publication year - 1998
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199806)13:6<455::aid-int2>3.0.co;2-k
Subject(s) - computer science , adaptation (eye) , artificial neural network , artificial intelligence , architecture , adaptive system , fuzzy logic , intelligent decision support system , speech recognition , machine learning , art , physics , optics , visual arts
This paper explores a multimodular architecture of an intelligent information system and proposes a method for adaptation. The method is based on evaluating which of the modules need to be adapted based on the performance of the whole system on new data. These modules are then trained selectively on the new data until they improve their performance and the performance of the whole system. The modules are fuzzy neural networks, especially designed to facilitate adaptive training and knowledge discovery, and spatial temporal maps. A particular case study of spoken language recognition is presented along with some preliminary experimental results of an adaptive speech recognition system. © 1998 John Wiley & Sons, Inc.