Title :
A plant modeling with a fuzzy associative memory system
Author :
Goto, Keisuke ; Yamaguchi, Toru
Author_Institution :
Lab. for Int. Fuzzy Eng. Res., Yokohama
Abstract :
Summary form only given. An attempt was made to demonstrate a useful qualitative model using causal concepts for modeling the operations of a physical plant. In order to control and monitor plants, support based on the intuitive, qualitative perspective of causal relations becomes important, along with the usual numerical, logical approach. This goal is realized by using a qualitative model, summarized instances and associative memories. Using a network model as a knowledge representation of the qualitative model, plant modeling is implemented by a fuzzy associative memory system called FAMOUS (Fuzzy Associative Memory Organizing Units System). The proposed modeling has three features: (1) the intuition of experts can be inferred by feedback associative memory, (2) the degree of influence of fuzzy causal relations can be clarified by a differential Hebbian learning rule; and (3) information having human-friendliness can be expressed. The authors have applied FAMOUS to the qualitative model to an object plant
Keywords :
content-addressable storage; feedback; fuzzy logic; inference mechanisms; knowledge representation; learning systems; FAMOUS; Fuzzy Associative Memory Organizing Units System; causal relations; differential Hebbian learning rule; expert intuition; feedback associative memory; inference; knowledge representation; plant control; plant modeling; plant monitoring; qualitative model; summarized instances; user friendliness; Associative memory; Feedback; Fuzzy systems; Hebbian theory; Humans; Knowledge representation; Laboratories; Monitoring; Organizing; Predictive models;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155469