Self-learning neurofuzzy controller

Chunshien Li*, Roland Priemer

*Corresponding author for this work

Research output: Contribution to conferenceConference Paperpeer-review


A self-learning fuzzy logic system is given for control of unknown multiple-input-multiple-output (MIMO) plants. A concise formulation of fuzzy controllers for MIMO plants is presented. Through new terminology and data types, relations among the crisp input vector, the fuzzy basis set for all linguistic input variables, the cardinality vector of fuzzy partitions in all input universes of discourse, the rule base linguistic value set, and the fuzzy inference action vector are established. The fuzzy controller can be cast into neural net structure. The integration of fuzzy logic and a neural network takes advantage of fuzzy data representation, fuzzy inference, parallel processing, and learning ability. The random optimization method is used to train the controller. The training process uses observations of plants input and output behavior, so that a model of the plant is not required.

Original languageEnglish
Number of pages4
StatePublished - 1996
Externally publishedYes
EventProceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3) - Ames, IA, USA
Duration: 18 08 199621 08 1996


ConferenceProceedings of the 1996 IEEE 39th Midwest Symposium on Circuits & Systems. Part 3 (of 3)
CityAmes, IA, USA


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