The concept of Evolving Intelligent Systems (EISs) was conceived around the turn of the century[3][4][5][6][7][8][9] with the phrase EIS itself coined for the first time by Angelov and Kasabov in a 2006 IEEE newsletter[8] and expanded in a 2010 text.
[9] EISs develop their structure, functionality and internal knowledge representation through autonomous learning from data streams generated by the possibly unknown environment and from the system self-monitoring.
[20][21] P. Angelov, D. Filev, N. Kasabov and O. Cordon organised the first IEEE Symposium on EFSs in 2006 (the proceedings of the conference can be found in[22]).
[38] More recently, the stability of the evolving fuzzy rule-based systems that consist of the structure learning and the fuzzily weighted recursive least square[7] parameter update method has been proven by Rong.
Fuzzy rule-based classifiers[34] are the methodological basis of a new approach to deep learning that was until now considered as a form of multi-layered neural networks.
These include: Most, if not all, of the above limitations can be avoided with the use of the Deep (Fuzzy) Rule-based Classifiers,[46][47] which were recently introduced based on ALMMo, while achieving similar or even better performance.
The resulting prototype-based IF...THEN...models are fully interpretable and dynamically evolving (they can adapt quickly and automatically to new data patterns or even new classes).