FUZZY CONTROLS BY PRODUCT-SUM-GRAVITY METHOD DEALING WITH FUZZY RULES OF EMPHATIC AND SUPPRESSIVE TYPES

1994 ◽  
Vol 02 (03) ◽  
pp. 305-319 ◽  
Author(s):  
M. MIZUMOTO
Keyword(s):  
Fuzzy Control ◽  
Membership Function ◽  
Fuzzy Inference ◽  
Good Control ◽  
Fuzzy Rules ◽  
Gravity Method ◽  
Control Rules

This paper shows that emphatic effects on fuzzy inference results are observed under product-sum-gravity method by using fuzzy control rules whose consequent part is characterized by a membership function whose grades are greater than 1. Suppressive effects are also realized by employing fuzzy control rules whose consequent part is characterized by a negative-valued membership function. It is shown that good control results are obtained by using the fuzzy control rules of emphatic and suppressive types.

scholarly journals Reinforcement Leaning of Fuzzy Control Rules with Context-Specitic Segmentation of Actions

2002 ◽  
Vol 6 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Hideki Yamagishi ◽  
◽  
Hiroshi Kawakami ◽  
Tadashi Horiuchi ◽  
Osamu Katai ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Decision Tree ◽  
Fuzzy Control ◽  
Knowledge Acquisition ◽  
Fuzzy Inference ◽  
Experimental Information ◽  
Proper Action ◽  
Proper Actions ◽  
Control Rules ◽  
Action Decision

Knowledge acquisition mainly involves two approaches: deriving general or abstract rules from human expertise such as heuristics of target systems, refined properly using further information, and extracting proper rules from experimental information, i.e., information on rewards and penalties obtained from all the possible alternative rules initially prepared ‐ our approach. Reinforcement learning methods are applied to problems where meaningful I/O sets cannot be specified beforehand. There are, however few algorithms to extract heuristics for action selection by using results of reinforcement learning. We propose a way to apply symbolic processing methods such as C4.5 to results of reinforcement learning where methods of fuzzy inference are incorporated. We also derive a proper action decision tree where conditions of proper actions for agents are effectively integrated and simplified.

Application of genetic algorithms and fuzzy control to a combined sewer pumping station

1999 ◽  
Vol 39 (9) ◽  
pp. 217-224 ◽  
Author(s):  
S. Yagi ◽  
S. Shiba
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Fitness Function ◽  
Pumping Station ◽  
Fitness Functions ◽  
Control Rules ◽  
Combined Sewer ◽  
Pumping Rates ◽  
Input Variables

In the present study, fuzzy logic control and genetic algorithms are applied to achieve improved pump operations in a combined sewer pumping station. Pumping rates are determined by fuzzy inference and fuzzy control rules corresponding to input variables. Genetic algorithms are used to automatically improve the fuzzy control rules through genetic operations such as selection, crossover and mutation. The effects of different fitness functions and learning conditions are investigated using a stormwater runoff model. It is found that current pump operations can be improved by adding the sewer water quality to the input variables and to the fitness function; the improved operations can reduce not only floods in the drainage area but also pollutant loads discharged to the receiving waters.

Optimization Design of Fuzzy Control Rules Based on Ant Colony Algorithm

2014 ◽  
Vol 716-717 ◽  
pp. 1662-1665
Author(s):  
Ya Lang Xing ◽  
He Xin ◽  
Jin Cheng Zhao
Keyword(s):  
Fuzzy Control ◽  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Fuzzy Rule ◽  
Coefficient Matrix ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Rule Base ◽  
Control Rules ◽  
And Control

To avoid the fuzzy rules getting into “rule exploding” in fuzzy control system, a fuzzy control rules optimization algorithm based on compatibility coefficient is proposed. The method defines the compatibility coefficient of fuzzy rules, and the compatibility coefficient matrix is used to be the heuristic information in ant colony algorithm. Ant colony algorithm is used to optimize designed complete fuzzy rule base. Simulation results show that the fuzzy rules have good compatibility and control performance.

Developing Fuzzy Control Irrigate System Based on PLC

2014 ◽  
Vol 721 ◽  
pp. 261-264
Author(s):  
Lei Xiao ◽  
Li Li ◽  
Xiao Long Wu
Keyword(s):  
Computer Simulation ◽  
Fuzzy Control ◽  
Real Time ◽  
Programmable Logic Controller ◽  
Fuzzy Rules ◽  
Programmable Logic ◽  
Real Time Control ◽  
The Real ◽  
Time Control ◽  
Control Rules

This paper will describe that the fuzzy control is used to realize irrigate real-time control. And some reasonable fuzzy rules are found by computer simulation in MATLAB. Then the real-time irrigate will be applied by fuzzy control rules with Programmable Logic Controller (PLC) circuit. At last, writer made a conclusion that debugging greenhouse seedlings is well to meet the requirements of greenhouse.

A Reinforcement Learning Scheme of Fuzzy Rules with Reduced Conditions

2000 ◽  
Vol 4 (2) ◽  
pp. 146-151 ◽  
Author(s):  
Hiroshi Kawakami ◽  
◽  
Osamu Katai ◽  
Tadataka Konishi ◽  
Keyword(s):  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Q Value ◽  
Huge Number ◽  
Q Learning ◽  
Control Rules ◽  
A Value ◽  
Rule System

This paper proposes a new method of Q-learning for the case where the states (conditions) and actions of systems are assumed to be continuous. The components of Q-tables are interpolated by fuzzy inference. The initial set of fuzzy rules is made of all combinations of conditions and actions relevant to the problem. Each rule is then associated with a value by which the Q-values of condition/action pairs are estimated. The values are revised by the Q-learning algorithm so as to make the fuzzy rule system effective. Although this framework may require a huge number of the initial fuzzy rules, we will show that considerable reduction of the number can be done by adopting what we call Condition Reduced Fuzzy Rules (CRFR). The antecedent part of CRFR consists of all actions and the selected conditions, and its consequent is set to be its Q-value. Finally, experimental results show that controllers with CRFRs perform equally well to the system with the most detailed fuzzy control rules, while the total number of parameters that have to be revised through the whole learning process is considerably reduced, and the number of the revised parameters at each step of learning increased.

scholarly journals Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

2021 ◽  
Vol 11 (8) ◽  
pp. 3484
Author(s):  
Martin Tabakov ◽  
Adrian Chlopowiec ◽  
Adam Chlopowiec ◽  
Adam Dlubak
Keyword(s):  
Fuzzy Inference ◽  
Real Data ◽  
Fuzzy Rules ◽  
Fuzzy Information ◽  
Benchmark Data ◽  
System A ◽  
Footprint Of Uncertainty ◽  
Interval Type ◽  
Derived Rules

In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

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