Cluster-based self-organizing neuro-fuzzy system with hybrid learning approach for function approximation

Chunshien Li, Kuo Hsiang Cheng, Chih Ming Chen, Jin Long Chen

Research output: Contribution to journalConference articlepeer-review


A novel hybrid cluster-based self-organizing neuro-fuzzy system (HC-SONFS) is proposed for dynamic function approximation and prediction. With the mechanism of self-organization, fuzzy rules are generated in the form of clusters using the proposed self-organization method to achieve compact and sufficient system structure if the current structure of knowledge base is insufficient to satisfy the required performance. A hybrid learning algorithm combining the well-known random optimization (RO) and the least square estimation (LSE) is use for fast learning. An example of chaos time series for system identification and prediction is illustrated. Compared to other approaches, excellent performance of the proposed HC-SONFS is observed.

Original languageEnglish
Pages (from-to)1186-1189
Number of pages4
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Issue numberPART III
StatePublished - 2005
EventFirst International Conference on Natural Computation, ICNC 2005 - Changsha, China
Duration: 27 Aug 200529 Aug 2005


Dive into the research topics of 'Cluster-based self-organizing neuro-fuzzy system with hybrid learning approach for function approximation'. Together they form a unique fingerprint.

Cite this