Self-organizing fuzzy intelligent system

Chunshien Li, Chun Yi Lee

Research output: Contribution to journalConference articlepeer-review


A self-organizing fuzzy system (SOFS) is presented. A plant model is not required for training, that is, the plant model is unknown to the SOFS. Using new data types, the vectors and matrices, a concise formulation is developed for the organization process and the inference activities of the SOFS. The fuzzy system can learn its rule-based structure and parameters from input/output training data. There is no fuzzy IF-THEN rule in the system initially. The fuzzy control policy is constructed automatically during learning process when the system is simulated by input/output training data. With the well-known random optimization (RO) method, the generated fuzzy system can learn its parameters for specific applications. The proposed SOFS is applied on temperature control problem.

Original languageEnglish
Pages (from-to)473-477
Number of pages5
JournalConference Record - IAS Annual Meeting (IEEE Industry Applications Society)
StatePublished - 2002
Event37th IAS Annual Meeting and World Conference on Industrial applications of Electrical Energy - Pittsburgh, PA, United States
Duration: 13 Oct 200218 Oct 2002


  • Clustering
  • Fuzzy control
  • Inverse learning control
  • Random optimization
  • Self-learning
  • Self-organization
  • Temperature control


Dive into the research topics of 'Self-organizing fuzzy intelligent system'. Together they form a unique fingerprint.

Cite this