TY - JOUR
T1 - Local Optimality of Self-Organising Neuro-Fuzzy Inference Systems
AU - Gu, Xiaowei
AU - Angelov, Plamen Parvanov
AU - Rong, Haijun
N1 - Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/11/30
Y1 - 2019/11/30
N2 - Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-organising neuro-fuzzy inference system (SONFIS) as an example of typical zero-order EISs and analyse the local optimality of its solutions. The optimality problem is firstly formulated in a mathematical form, and detailed optimality analysis is conducted. The conclusion is that SONFIS does not generate a locally optimal solution in its original form. Then, an optimisation method is proposed for SONFIS, which helps the system to attain local optimality in a few iterations using historical data. Numerical examples presented in this paper demonstrate the validity of the optimality analysis and the effectiveness of the proposed optimisation method. In addition, it is further verified numerically that the proposed concept and general principles can be applied to other types of zero-order EISs with similar operating mechanisms.
AB - Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-organising neuro-fuzzy inference system (SONFIS) as an example of typical zero-order EISs and analyse the local optimality of its solutions. The optimality problem is firstly formulated in a mathematical form, and detailed optimality analysis is conducted. The conclusion is that SONFIS does not generate a locally optimal solution in its original form. Then, an optimisation method is proposed for SONFIS, which helps the system to attain local optimality in a few iterations using historical data. Numerical examples presented in this paper demonstrate the validity of the optimality analysis and the effectiveness of the proposed optimisation method. In addition, it is further verified numerically that the proposed concept and general principles can be applied to other types of zero-order EISs with similar operating mechanisms.
KW - Data partitioning
KW - Evolving intelligent system
KW - Local optimality
KW - Neuro-fuzzy system
KW - Self-organising
UR - http://www.research.lancs.ac.uk/portal/en/publications/local-optimality-of-selforganising-neurofuzzy-inference-systems(b3ec62d3-1862-4ba9-9ec2-5e16e2c408f8).html
UR - http://www.scopus.com/inward/record.url?scp=85068547704&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.07.006
DO - 10.1016/j.ins.2019.07.006
M3 - Article
SN - 0020-0255
VL - 503
SP - 351
EP - 380
JO - Information Sciences
JF - Information Sciences
ER -