| Project/Area Number |
05680306
|
|---|
| Research Category |
Grant-in-Aid for General Scientific Research (C)
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Intelligent informatics
|
|---|
| Research Institution | Osaka Electro-Communication University |
|---|
Principal Investigator |
MIZUMOTO Masaharu Osaka Electro-Communication University, Faculty of Engineering, Professor, 工学部, 教授 (40029541)
|
|---|
| Project Period (FY) |
1993 – 1994
|
| Project Status |
Completed (Fiscal Year 1994)
|
| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1994: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1993: ¥1,200,000 (Direct Cost: ¥1,200,000)
|
|---|
| Keywords | Fuzzy controls / Product-sum-gravity method / Fuzzy singleton-type reasoning method / Simplified reasoning method / Tuning / Time-variant control / ミニ-マックス-重心法 / ファジイシングルトン型推論法 / ファジィ規則 / ニューロ技法 / ファジィ推論 / 最急降下法 |
|---|
| Research Abstract |
Fuzzy control rules used in fuzzy controls are usually constructed through trial and error by human operators. But it becomes difficult to construct the fuzzy rules by humans when the controlled plants are complicated.
This project aims to acquire fuzzy control rules automatically by using neural techniques in tuning the antecedent and consequent parts of fuzzy rules.
Fuzzy reasoning method used here is a "fuzzy singleton-type reasoning method" proposed by this investigator. This reasoning method consists of fuzzy control rules whose consequent part is a fuzzy singleton (that is, a real number with a positive weight) , and the method can adjust subtly control results by changing the weights of fuzzy control rules.
We give a method of tuning the center and width of the antecedent part as well as the position and weight of the consequent part of fuzzy control rules of fuzzy singleton-type reasoning method by using the deepest decendent method used in the neuro technique. It is shown from the computer simulation of several functions that this method can generate smaller number of fuzzy control rules compared with the case by the simplified reasoning method which is used usually and widely in fuzzy controls.
Moreover, we propose a time-variant fuzzy reasoning method which is realized by changing weights with time for fuzzy rules of a fuzzy singleton-type reasoning method. The time-variant fuzzy reasoning method is applied to fuzzy controls in which fuzzy control rules or their groups are changed with time by varying weights of fuzzy control rules, and good control results are obtained.
|
