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Fuzzy control method based on intuitionistic fuzzy evidence support degree uncertainty reasoning

A technology with fuzzy intuition and uncertainty, applied in the direction of adaptive control, general control system, control/regulation system, etc., it can solve the problems of limiting the application range, affecting the control effect, difficult to obtain satisfactory control effect, etc., to simplify the operation. Effect

Active Publication Date: 2015-04-22
南通辰丰自动化设备制造有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, if lack of fuzzy experience, it is difficult to obtain satisfactory control effect
At the same time, the existing fuzzy control system only considers certain and subjective membership degrees and inference rules, and cannot make corresponding inferences in the absence of information, which limits its application range and affects the control effect.

Method used

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  • Fuzzy control method based on intuitionistic fuzzy evidence support degree uncertainty reasoning
  • Fuzzy control method based on intuitionistic fuzzy evidence support degree uncertainty reasoning
  • Fuzzy control method based on intuitionistic fuzzy evidence support degree uncertainty reasoning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] Embodiment 1: The logical reasoning method of single-rule intuitive fuzzy evidence support degree

[0023] Such as figure 1 As shown, embodiment 1 is based on the fuzzy hypothetical reasoning model, and uses the logical reasoning method of intuitionistic fuzzy evidence support to solve the problem, and the description form is as follows:

[0024] Known A B (major premise)

[0025] and given A * (minor premise)

[0026]

[0027] Ask for B * (in conclusion).

[0028] The specific method is to introduce the CRI method of fuzzy reasoning under the premise of collecting information, and the solution steps of the logical reasoning method (referred to as TD-CRI) of the intuitionistic fuzzy evidence support degree are as follows:

[0029] Step 1: Intuitionistic fuzzification; express the language in the rules in the form of intuitionistic fuzzy sets;

[0030] Step 2: knowledge representation; that is, the known conditions in the rules ...

Embodiment 2

[0055] Embodiment 2: Logical reasoning method of multi-rule intuitionistic fuzzy evidence support degree

[0056] Such as figure 1 As shown, embodiment 2 utilizes the aggregation principle to convert multiple intuitionistic fuzzy rule reasoning problems into the following form:

[0057] Known A 1 B 1 ,

[0058] A2 B 2 ,

[0059]

[0060] A n B n .(major premise)

[0061] and given A * (minor premise)

[0062]

[0063] Ask for B * (in conclusion)

[0064] Solve in the following two ways: first infer then aggregate (FITA) and first aggregate then infer (FATI).

[0065] 1) Reasoning first and then aggregation (FITA)

[0066] Answer reasoning questions of the form:

[0067] Known A i B i (major premise)

[0068] and given A * (minor premise)

[0069]

[0070] Ask for B * (in conclusion)

[0071] In Example 1, n intermediate results are obtained after step 5 , aggregate the n inte...

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Abstract

The invention discloses a fuzzy control method based on intuitionistic fuzzy evidence supporting uncertainty reasoning. The fuzzy control method includes the following steps of intuitionistic fuzzification, knowledge representation, logical reasoning of the intuitionistic fuzzy evidence support degree and fuzzy processing. According to the fuzzy control method, an intuitionistic fuzzy reasoning problem can be converted into a fuzzy reasoning problem through calculation of the evidence support degree, therefore, the problem of controlling over evidence having the positive aspect and the negative aspect is solved, and the defects that only degree of membership and a reasoning rule which are certain and have certain subjectivity are considered in the fuzzy reasoning process are overcome. According to the fuzzy control method, experience and knowledge of an operator can be recorded and subjected to fuzzy reasoning in the manner of an intuitionistic fuzzy control rule under the condition of information shortage, and ingenious control identical with control by the operator can be achieved through a computer. The reasoning process in the fuzzy control method simulates the thinking process of a human, and therefore completed systems can be processed.

Description

technical field [0001] The invention belongs to fuzzy control technology in artificial intelligence, in particular to a fuzzy control method based on intuitionistic fuzzy evidence support degree uncertainty reasoning. Background technique [0002] Since Zadeh proposed fuzzy set theory in 1965, the theory has developed rapidly. Atanassov extended fuzzy sets, gave the concept of intuitionistic fuzzy sets, and pointed out that fuzzy sets are special cases of intuitionistic fuzzy sets from two aspects of membership degree and non-membership degree. In today's fuzzy knowledge representation, intuitionistic fuzzy knowledge representation has become a hot field of research, and the research on intuitionistic fuzzy set theory and its application in many fields such as decision-making, logic programming, medical diagnosis, machine learning and market forecasting has attracted people's attention. Great attention has been paid to it and fruitful results have been achieved. [0003] I...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 邹丽徐本强郑宏亮谭雪微
Owner 南通辰丰自动化设备制造有限公司